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Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic treatmentsPurba, Abdul

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Pharmacoeconomics of prophylactic, empirical, and diagnostic-based

antibiotic treatmentsFocus on surgical site infection and

hospitalized community-acquired pneumonia

Abdul Khairul Rizki Purba

Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic

treatments

Focus on surgical site infection and hospitalized community-acquired pneumonia

Author: Abdul Khairul Rizki Purba

Cover: Abdul Khairul Rizki Purba (concept) and zannoism.com (design)

Lay-out: Douwe Oppewal

Printing: Ipskamp Printing, Enschede

ISBN (book): 978-94-034-2805-5

ISBN (electronic version): 978-94-034-2806-2

This thesis was funded by grants from DIKTI BPPLN scholarship (the scholarship from Directorate

General of Resources for Science, Technology and Higher Education, Ministry of Research,

Technology and Higher Education, Republic of Indonesia), from Universitas Airlangga, and from the

University Medical Center Groningen, the Netherlands. Also, the work presented in this thesis was

performed at the Department of Health Sciences and the Department of Medical Microbiology

of the University Medical Center Groningen. Financial support for printing this thesis was kindly

provided by the University of Groningen and the Groningen University for Drug Exploration

(GUIDE) of the Graduate School of Medical Sciences (GSMS).

Copyright, 2020, Abdul Khairul Rizki Purba

All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any

means, electronically or mechanically by photocopying, recording, or otherwise, without written

permission of the author. The copy right of previously published chapters of this thesis remains

with the publisher or journal.

Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic

treatments

Focus on surgical site infection and hospitalized community-acquired pneumonia

PhD thesis

to obtain the degree of PhD at theUniversity of Groningen on the authority of the

Rector Magnificus Prof. C. Wijmengaand in accordance with

the decision by the College of Deans.

This thesis will be defended in public on

Wednesday 8 July 2020 at 12.45 hours

by

Abdul Khairul Rizki Purba

born on 22 February 1984 in Surabaya, Indonesia

Pharmacoeconomics of prophylactic, empirical, and diagnostic-based antibiotic

treatments

Focus on surgical site infection and hospitalized community-acquired pneumonia

Author: Abdul Khairul Rizki Purba

Cover: Abdul Khairul Rizki Purba (concept) and zannoism.com (design)

Lay-out: Douwe Oppewal

Printing: Ipskamp Printing, Enschede

ISBN (book): 978-94-034-2805-5

ISBN (electronic version): 978-94-034-2806-2

This thesis was funded by grants from DIKTI BPPLN scholarship (the scholarship from Directorate

General of Resources for Science, Technology and Higher Education, Ministry of Research,

Technology and Higher Education, Republic of Indonesia), from Universitas Airlangga, and from the

University Medical Center Groningen, the Netherlands. Also, the work presented in this thesis was

performed at the Department of Health Sciences and the Department of Medical Microbiology

of the University Medical Center Groningen. Financial support for printing this thesis was kindly

provided by the University of Groningen and the Groningen University for Drug Exploration

(GUIDE) of the Graduate School of Medical Sciences (GSMS).

Copyright, 2020, Abdul Khairul Rizki Purba

All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any

means, electronically or mechanically by photocopying, recording, or otherwise, without written

permission of the author. The copy right of previously published chapters of this thesis remains

with the publisher or journal.

SupervisorsProf. M.J. Postma Prof. A.W. Friedrich

Co-supervisorDr. J.W. Dik

Assessment CommitteeProf. Kuntaman Prof. B. Wilffert Prof. J.C. Wilschut

ParanymphsErley F. Lizarazo ForeroM. Rifqi Rokhman

Bismillaahirrahmaanirrahiim…

8

9

CONTENTS

Part 1. Introduction 11

Chapter 1 General introduction 13

Chapter 2 The burden and costs of sepsis and reimbursement of its treatment in

a developing country: An observational study on focal infections in Indonesia 21

Part 2. Prophylactic antibiotics for surgical site infection prevention 43

Chapter 3 Prevention of surgical site infections: A systematic review of cost analyses

in the use of prophylactic antibiotics 45

Chapter 4 The impacts of deep surgical site infections on readmissions, length of stay,

and costs: A matched case-control cohort study in an academic hospital

in the Netherlands 81

Part 3. Empirical antibiotics for hospitalized community-acquired pneumonia 97

Chapter 5 Multidrug-resistant infections among hospitalized adults with

community-acquired pneumonia in an Indonesian tertiary referral hospital 99

Chapter 6 Cost-effectiveness of culture-based versus empirical antibiotic treatment

for hospitalized adults with community-acquired pneumonia in Indonesia:

A real-world patient-database study 119

Part 4. Discussion 143

Chapter 7 General discussion and future perspectives 145

Chapter 8 Appendix: Laboratory findings as predictors of sepsis mortality

among adult patients in a general hospital in Indonesia 155

Addendum 163

Summary 164

Samenvatting 168

Ringkasan 172

Acknowledgments 176

Curriculum vitae 185

List of publications 189

Biography 190

10

11

1Part

Introduction

12

CHAPTER 1General introduction

13

14

INTRODUCTION

Surgical site infection (SSI) and hospitalized community-acquired pneumonia (CAP) reflect

two infectious conditions caused by bacterial infections. The focal infections underlying SSI

and hospitalized CAP can develop into further advanced complications with manifestations of

systemic infection symptoms such as sepsis, with pathogens spreading into the blood circulation

and organs. Specific situations require antibiotics for prophylactic and/or empirical treatments.

Potentially, sometimes antibiotics are being used improperly and ineffectively, leading to further

impacts such as antimicrobial resistance (AMR) with a related burden in terms of morbidity,

mortality, and cost, especially in developing countries.1,2 Diagnostic-based antibiotic treatments

are the potential solution to reduce such unguided antibiotic treatments. Ergo, there is a need

for careful analysis of infections, its treatment and AMR, both in the developed world like The

Netherlands as well as in the developing world like in Indonesia. This thesis aims to address this

issue in both settings, with a focus on pharmacoeconomic aspects.

Among limited-resource countries, Indonesia has a documented low application of

pharmacoeconomics in infectious disease treatments.3,4 Such assessment is needed to provide

adequate evidence and contribute to Indonesian national government policy strategies, with the

pharmacoeconomics approach being used to evaluate the treatment and diagnostic bundles

within a restricted budget setting. In addition to pharmacoeconomics assessments of infection

treatments, medical microbiology evaluations are essential in order to develop strategies to

prevent increased antibiotic resistance by identifying the specific characteristics of the underlying

pathogens causing the infections. In this thesis, we illustrate this approach with data, figures

analyses for SSI and hospitalized CAP.

The surgical site is a potential port of entry for exogenous organisms: these pose an immediate

threat to the body, and infections cause prolonged wound healing.5,6 SSI is the most common

focal infection related to surgery and an important target for infectious disease prevention.6,7

In low and middle-income countries, SSI rates in 100 surgical patients doubled from 5.6 to 11.8

between 1995 and 2008.8 Antiseptic and prophylactic antibiotics used properly in the preoperative

phase should be considered to prevent SSI. Parenteral and oral antibiotic prophylaxis based on

the patterns of bacteria and antibiotic susceptibility has been recommended recently to reduce

SSI rates efficiently.5 The most crucial goal in the preoperative preparation period is to reduce

the bacterial load surrounding the incision area.9,10 Various modalities have been implemented,

ranging from the prevention of SSI using a prophylactic antibiotic before surgical incision and in

the postoperative period.10–12 The World Health Organization (WHO) has released guidelines on

preventing SSI by assessing prophylactic antibiotics in three consecutive periods: preoperative,

intraoperative and postoperative. Broad utilization of prophylactic antibiotics, however, comes

with the potential danger of improper use and leads to repercussions such as readmission and

additional cost. Economic analysis of prophylactic antibiotic use can help in guiding adequate SSI

prevention. Given its resource-limited setting, Indonesia has employed the limited bundle for SSI

prevention adopting four out of the 21 WHO measures (Figure 1.1).10,13 As yet, there has been no

integrated efficacy and cost assessment of antibiotic prophylaxis for SSI prevention in Indonesia.

Chapter 1

15

Figure 1.1 The measures for preoperative, intraoperative, and postoperative care to prevent surgical site infections.10

Note: *These four measures have been considered in Indonesia for SSI prevention.13

Hospitalized CAP is a non-surgical-related infectious disease, contributing high morbidity in terms

of more hospitalizations for ICU admissions, requiring mechanical ventilators and further sepsis

complication.14–16 Elevated hospitalization costs for ICU patients with CAP were strongly associated

with the use of a mechanical ventilator, the presence of severe sepsis and sepsis shock.17 According

to information from the Centers for Disease Control and Prevention (CDC) in Indonesia, lower

respiratory tract infections (LRTIs) reflected the most common cause of death among infectious

disease cases.18 Among LRTIs in Indonesia, CAP has been reported with an incidence rate of 4%

in 2018.19,20 CAP can pose challenges in treatment and primary healthcare providers, especially in

a limited-resource setting such as Indonesia, frequently refer moderate and severe CAP cases to

hospitals that have more comprehensive facilities. Hospitalization is often required for cases with

underlying bacterial infections and to need at least one empirical antibiotic.21–25

General Introduction

1

16

Table 1.1 Microorganisms causing hospitalized community-acquired pneumonia in Indonesia, based on a local survey between 1989 and 2001 in Surabaya, Medan, and Makasar26

Microorganisms n %

Klebsiella pneumoniae 85 21.5%

Streptococcus pneumoniae 51 12.9%

Staphylococcus aureus 39 9.8%

Streptococcus β haemolyticus 35 8.8%

Staphylococcus epidermidis 27 6.8%

Streptococcus viridans 25 6.3%

Streptococcus pyogenes 21 5.3%

Enterobacter aerogenous 19 4.8%

Proteus vulgaris 18 4.5%

Pseudomonas aeruginosa 15 3.8%

Candida albican 12 3.0%

Streptococcus gamma haemolyticus 7 1.8%

Klebsiella oxytoca 7 1.8%

Escherichia coli 7 1.8%

Staphylococcus albus 7 1.8%

Klebsiella ozainae 6 1.5%

Proteus mirabillis 6 1.5%

Enterobacter agglomerans 3 0.8%

Peptostreptococcus spp. 2 0.5%

Streptococcus faecalis 2 0.5%

Candida parapsilosis 2 0.5%

Note: Adapted from Indonesian Society of Respirology, Guideline for diagnosis and management of community-acquired pneumonia in Indonesia [Perhimpunan Dokter Paru Indonesia. Pneumonia Komuniti: pedoman diagnosis dan penatalaksanaan di Indonesia]. 2003. Available from https://www.klikpdpi.com/

With the increasing problem of AMR, guidelines for antibiotic use are needed. Underlying such

guidelines should be updated antimicrobial resistance data and data on treatment efficacy,

for example, supporting new national guidelines on the empirical use of antibiotics can be

put forward. Table 1.1 shows the underlying pathogens causing hospitalized CAP in three big

cities in Indonesia. The current Indonesian guidelines on hospitalized CAP treatment have

been developed in 2003, following the American Thoracic Society (ATS) guidelines.23,25 These

guidelines, however, were based on studies conducted in 1991-2001 in high-income countries

where Streptococcus pneumoniae and some Gram-positive bacteria were the most dominant

pathogens causing CAP.27 The guidelines, therefore, recommend selecting antibiotics that are

particularly effective to Gram-positive bacteria.23 In contrast, among the pathogens causing

CAP in Indonesia, Gram-negative bacteria are considered to be the most common underlying

Chapter 1

17

pathogen.28,29 Indiscriminate use of antimicrobials as a result of unrepresentative guidelines, not

guided by local/national microbiological evidence, generally results in the increasing emergence

of antimicrobial resistance, both in individual patients and at the community level.

SCOPE OF THE THESIS

The main focus of this thesis is to assess pharmacoeconomic aspects in the management of

bacterial infections; applied to illustrative cases, namely, SSI and hospitalized CAP. Chapter 2

presents a discussion of the burden of two conditions concerning sepsis related to various focal

infections. This chapter also puts forward a proposed national unit price taking into account

the cost of focal infections with sepsis with universal health coverage now being introduced in

Indonesia. Focusing on the use of prophylactic antibiotics for SSI prevention, Chapter 3 reviews

the methodologies used in published economic evaluations on prophylactic antibiotics in SSI

prevention. The review also includes a comprehensive discussion of the local epidemiology of

pathogen-causing SSIs. In addition, Chapter 4 discusses the impact of surgical site infections

on readmissions and costs in an academic hospital in the Netherlands. This potentially provides

a prospective model for developing a health policy on implementation strategies to tackle SSI

cases in Indonesia as the Dutch model may be conceived as being successful. For example, the

European Centre for Disease Prevention and Control’s point prevalence surveys in 2016 and 2017

documented that the Netherlands successfully decreased SSI incidence from 2.2 to 1.0 per 100

surgeries.30,31 Focusing on the use of empirical antibiotics versus diagnostic-based antibiotic

treatment, Chapter 5 outlines the clinical epidemiology of multidrug-resistant infections among

hospitalized adults with CAP in an Indonesian tertiary referral hospital. Chapter 6 analyzes the

cost-effectiveness of culture-based versus empirical antibiotic treatment for hospitalized adults

with CAP in Indonesia, based on a real-world patient database study. Finally, Chapter 7 discusses

the main findings presented in the previous chapters and makes several recommendations,

particularly for institutions or policymakers who are currently facing uncertainty about bundling

SSI and hospitalized CAP sepsis in healthcare management due to the excessive cost in a limited-

resource setting. As an Annex to Chapter 7 additional clinical evidence on laboratory biomarkers

as independent factors to sepsis mortality is presented in Chapter 8.

General Introduction

1

18

REFERENCES

1. Azmi, S. et al. Assessing the burden of pneumonia using administrative data from Malaysia, Indonesia, and the Philippines. Int. J. Infect. Dis. 49, 87–93 (2016).

2. Ling, M. L., Apisarnthanarak, A. & Madriaga, G. The Burden of Healthcare-Associated Infections in Southeast Asia: A Systematic Literature Review and Meta-analysis. Clin. Infect. Dis. 60, 1690–1699 (2015).

3. Teerawattanasook, N. et al. Capacity and Utilization of Blood Culture in Two Referral Hospitals in Indonesia and Thailand. Am. J. Trop. Med. Hyg. 97, 1257–1261 (2017).

4. Babar, Z.-U.-D. & Scahill, S. Is there a role for pharmacoeconomics in developing countries? Pharmacoeconomics 28, 1069–1074 (2010).

5. Berrios-Torres, S. I. et al. Centers for Disease Control and Prevention Guideline for the Prevention of Surgical Site Infection, 2017. JAMA Surg. 152, 784–791 (2017).

6. Mangram, A. J., Horan, T. C., Pearson, M. L., Silver, L. C. & Jarvis, W. R. Guideline for Prevention of Surgical Site Infection, 1999. Centers for Disease Control and Prevention (CDC) Hospital Infection Control Practices Advisory Committee. Am. J. Infect. Control 27, 97–132; quiz 133–4; discussion 96 (1999).

7. Merkow, R. P. et al. Underlying reasons associated with hospital readmission following surgery in the United States. JAMA 313, 483–495 (2015).

8. Allegranzi, B. et al. Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet (London, England) 377, 228–241 (2011).

9. Lundberg, P. W. et al. Pre-Operative Antisepsis Protocol Compliance and the Effect on Bacterial Load Reduction. Surg. Infect. (Larchmt). 17, 32–37 (2016).

10. World Health Organization. Global guidelines on the prevention of surgical site infection. (2016). Available at: https://www.who.int/gpsc/ssi-prevention-guidelines/en/. (Accessed: 1st January 2020)

11. European Centre for Disease Prevention and Control. Surgical site infections. Available at: https://www.ecdc.europa.eu/en/publications-data/directory-guidance-prevention-and-control/healthcare-associated-infections-0. (Accessed: 2nd December 2019)

12. Health, N. C. C. for W. and C. Surgical Site Infection: Prevention and Treatment of Surgical Site Infection. (2008).13. Ministry of Health of the Republic of Indonesia. A guideline for infection prevention in healthcare fascilities. (2017).

Available at: http://ditjenpp.kemenkumham.go.id/arsip/bn/2017/bn857-2017.pdf. (Accessed: 1st January 2020)14. Sligl, W. I. & Marrie, T. J. Severe community-acquired pneumonia. Crit. Care Clin. 29, 563–601 (2013).15. Remington, L. T. & Sligl, W. I. Community-acquired pneumonia. Curr. Opin. Pulm. Med. 20, 215–224 (2014).16. Montull, B. et al. Predictors of Severe Sepsis among Patients Hospitalized for Community-Acquired Pneumonia. PLoS One

11, e0145929 (2016).17. Gumus, A. et al. Factors Affecting Cost of Patients with Severe Community-Acquired Pneumonia in Intensive Care Unit.

Turkish Thorac. J. 20, 216–223 (2019).18. Centers for Disease Control and Prevention. Global Health - Indonesia. (2012). Available at: https://www.cdc.gov/

globalhealth/countries/indonesia/default.htm. (Accessed: 29th December 2019)19. National report of basic health research (Laporan nasional Riskesdas 2018), Ministry of Health, Republic of Indonesia. 73–7

(2018). Available at: http://labdata.litbang.depkes.go.id/riset-badan-litbangkes/menu-riskesnas/menu-riskesdas/426-rkd-2018. (Accessed: 13th November 2019)

20. Ministry of Health, Republic of Indonesia, Basic health research (Riskesdas 2013). 98–101 (2013). Available at: http://labdata.litbang.depkes.go.id/riset-badan-litbangkes/menu-riskesnas/menu-riskesdas/374-rkd-2013.

21. Anevlavis, S. & Bouros, D. Community acquired bacterial pneumonia. Expert Opin. Pharmacother. 11, 361–374 (2010).22. Eccles, S. et al. Diagnosis and management of community and hospital acquired pneumonia in adults: Summary of NICE

guidance. BMJ 349, 1–5 (2014).23. Mandell, L. A. et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the

management of community-acquired pneumonia in adults. Clin. Infect. Dis. 44 Suppl 2, S27-72 (2007).24. Woodhead, M. New guidelines for the management of adult lower respiratory tract infections. The European respiratory

journal 38, 1250–1251 (2011).25. Lim, W. S. et al. BTS guidelines for the management of community acquired pneumonia in adults: update 2009. Thorax 64

Suppl 3, iii1-55 (2009).26. Indonesian Society of Respirology. Guideline for diagnosis and management of community pneumonia in Indonesia.

(2003). Available at: https://www.scribd.com/doc/125419923/Pnemonia-Komuniti-Pdpi. (Accessed: 22nd April 2019)27. File, T. M. Community-acquired pneumonia. Lancet (London, England) 362, 1991–2001 (2003).28. Purba, A. K. et al. Multidrug-Resistant Infections Among Hospitalized Adults With Community-Acquired Pneumonia In An

Indonesian Tertiary Referral Hospital. Infect. Drug Resist. 12, 3663–3675 (2019).29. Farida, H. et al. Viruses and Gram-negative bacilli dominate the etiology of community-acquired pneumonia in Indonesia,

a cohort study. Int. J. Infect. Dis. 38, 101–107 (2015).

Chapter 1

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30. European Centre for Disease Prevention and Control (ECDC). Healthcare-associated infections: surgical site infections. 1–18 (2017). Available at: https://www.ecdc.europa.eu/sites/default/files/documents/AER_for_2017-SSI.pdf. (Accessed: 26th December 2019)

31. European Centre for Disease Prevention and Control (ECDC). Healthcare-associated infections: surgical site infections. 1–15 (2016). Available at: https://www.ecdc.europa.eu/sites/default/files/documents/AER_for_2016-SSI_0.pdf. (Accessed: 26th December 2019)

General Introduction

1

20

CHAPTER 2

The burden and costs of sepsis and reimbursement of its treatment in a

developing country: An observational study on focal infections in Indonesia

Abdul Khairul Rizki Purba

Nina Mariana

Gestina Aliska

Sonny Hadi Wijaya

Riyanti Retno Wulandari

Usman Hadi

Hamzah

Cahyo Wibisono Nugroho

Jurjen van der Schans

Maarten J. Postma

Int J Infect Dis. 2020;S1201-9712(20)30294-0. doi:10.1016/j.ijid.2020.04.075.

21

22

ABSTRACT

Objectives: This study aimed to determine the burden of sepsis with focal infections in the

resource-limited context of Indonesia and to propose national prices for sepsis reimbursement.

Methods: A retrospective observational study was conducted from 2013-2016 on cost of surviving

and non-surviving sepsis patients from a payer perspective using inpatient billing records in

four hospitals. The national burden of sepsis was calculated, and proposed national prices for

reimbursement were developed.

Results: Of the 14,076 sepsis patients, 5,876 (41.7%) survived and 8.200 (58.3%) died. The mean

hospital costs incurred per surviving and deceased sepsis patient were US$1,011 (SE +23.4) and

US$1,406 (SE +27.8), respectively. The national burden of sepsis in 100,000 patients was estimated

to be US$130 million. Sepsis patients with multifocal infections and a single focal lower-respiratory

tract infection (LRTI) were estimated as being the two with the highest economic burden (US$48

million and US$33 million, respectively, within 100,000 sepsis patients). Sepsis with cardiovascular

infection was estimated to warrant the highest proposed national price for reimbursement

(US$4,256).

Conclusions: Multifocal infections and LRTIs are the major focal infections with the highest burden

of sepsis. This study showed varying cost estimates for sepsis, necessitating a new reimbursement

system with adjustment of the national prices taking the particular foci into account.

Chapter 2

23

INTRODUCTION

Sepsis is estimated to involve 31.5 million cases each year worldwide.1 Of these cases, 19.4 million

are characterized by severe sepsis, accounting for 5.3 million deaths annually.1 These estimates are

derived from data compiled for high-income countries. However, the highest mortalities occur in

low-income countries, followed by low-middle income countries (LMICs).2 There is a surprising lack

of data on mortality and costs among sepsis patients in LMICs, such as most African and Asian

countries, including Indonesia.1,3 Indonesia, which is the most populated country in Southeast Asia

and the fourth most populated country globally, has a high incidence of communicable diseases.4,5

Ascertaining the granularity of the sepsis burden in Indonesia has become essential in light of the

government’s introduction of a new national health insurance system (Jaminan Kesehatan Nasional).6

In 2018, universal health coverage (UHC), provided by a single national payer, became available for

203 million people.7 During the period 2019-2020, coverage will be extended to the entire Indonesian

population (approximately 264 million people).4,7 Accordingly, a national reimbursement price for

each disease will need to be accounted for within the reimbursement system.7–9

The economic burden of sepsis, which includes providing medication and fluid resuscitation

during hospitalization, has been reported to be very high.10 In the United States, hospitalization

costs for sepsis patients were approximately US$20 billion in 2011.11 A previous systematic review,

which mostly included studies performed in the United States, revealed that an essential analysis

of the economic burden of sepsis concerned an evaluation between survivors and non-survivors,

because of a major difference in the mean total hospital costs per day (US$351 vs. US$948,

respectively).12 The difference in burden between survivors and non-survivors is unknown in LMICs.

International budgetary guidelines for sepsis management mostly apply to developed countries

and therefore may require cost adjustments of service bundles relating to sepsis management in

resource-limited settings.13,14

A focal infection terminology was firstly introduced in 1910 by William Hunter, who elaborated the

relationship between focal infections and systemic diseases.15 A focal infection is a potential source

of microorganisms that may disseminate into deep tissue and spread to the bloodstream. A further

impact of the dissemination of the microorganisms and their toxin in the bloodstream is activation of

the inflammatory mediators and worsening organ dysfunction due to sepsis.16 According to the third

consensus definitions for sepsis and septic shock17, sepsis has at least an underlying focal infection as

an entry of the pathogen to the systemic circulation. Each focal infection causing sepsis comes with

different complications, with a wide range of costs. Therefore, the reimbursement of sepsis needs

cost adjustments according to the underlying focal infection. In Indonesia, sepsis and the associated

focal infections are not coded together when calculating the national price of diseases, resulting

in possible under-budgeting for sepsis-related expenditure.18 Therefore, a reevaluation of the costs

for sepsis has become urgent for countries like Indonesia, including dealing with underlying focal

infections. This study analyzed costs for surviving and deceased sepsis patients, explicitly considering

underlying focal infections. In addition, it then estimated national prices for reimbursement under

UHC based on the analyzed burden and costs of sepsis.

The burden and costs of sepsis

2

24

METHODS

Study designA retrospective observational study was conducted on patients with sepsis in four Indonesian

medical centers: (1) Dr. Soetomo General Academic Hospital in Surabaya, a national healthcare

referral center, with 1,514 beds, serving eastern Indonesia; (2) Universitas Airlangga Hospital in

Surabaya, a teaching medical center with 180 beds in Surabaya; (3) The Prof. Dr. Sulianti Saroso

National Center for Infectious Diseases Hospital, with 180 beds in Jakarta; and (4) Dr. M. Djamil

Hospital in Padang, a national referral center with 800 beds, serving western Indonesia. Inpatient

registries and hospital discharge data were obtained from the Department of Medical Records for

the period 01 January 2013 to 31 December 2016. The dataset covered patients demographics,

diagnoses, hospital-discharge mortalities, laboratory tests, and medications.

Criteria for selecting patientsAll patients with sepsis and aged > 18 years were included. The diagnosis of sepsis was clarified by

the physicians. Previously, in Indonesia, the physician used sepsis criteria based on International

Sepsis Definition Conference 2001 supported by the Society of Critical Care Medicine, the

European Society of Intensive Care Medicine, the American College of Chest Physicians, the

American Thoracic Society and the Surgical Infection Society.19 The pathophysiology of sepsis has

systematically defined from systemic inflammatory response syndrome (SIRS) to shock sepsis. SIRS

was defined at least two of the following clinical signs: the body temperature < 36oC or >38oC,

tachycardia (heart rate > 90beats/min), tachypnoea (>20 breaths/min or PaCO2 <30 mmHg or

with mechanical ventilation), white blood cells <4,000 cells/µL or >12,000 cells/µL or >10% of band

forms.20 Sepsis was defined as SIRS with focal infections.21 Severe sepsis was defined as sepsis with

organ dysfunctions or hypoperfusion (oliguria, lactic acidosis, acute mental status alteration) or

sepsis-induced hypotension (systolic blood pressure lower 90mmHg). In addition, septic shock

is defined as severe sepsis with a condition which requires vasopressor administration after

adequate fluid resuscitation.17 In 2016 and afterwards, the criteria for sepsis diagnosis followed

the Indonesian Ministry of Health adopted Third International Consensus Definitions for Sepsis

and Shock, Sepsis-3 17, and diagnostic criteria for sepsis entailed in the Sequential Organ Failure

Assessment (SOFA) score that includes at least two of the following three ‘quick’ SOFA (qSOFA)

criteria: systolic blood pressure ≤ 100 mmHg, respiratory rate ≥ 22 breaths per minute, and

incorporating altered mentation (Glasgow Coma Scale score < 15).22 In this study, the source

infection of sepsis was pointed as focal infection.

The study categorized single focal infections per site of the infections as cardiovascular

infections (CVIs), gastrointestinal tract infections (GTIs), lower-respiratory tract infections (LRTIs),

neuromuscular infections (NMIs), urinary tract infections (UTIs), and wound infections (WIs). WIs

recognized at the sites of surgery were subclassified as surgical site infections (SSIs). The physicians

confirmed SSI diagnoses according to the Centers for Diseases Control and Prevention.23 Focal

mouth and dental infections were included in the NMI category since those infections anatomically

Chapter 2

25

involved soft tissues such as nerves and muscles. Sepsis patients with two or more focal infections

were grouped into sepsis with multifocal infections. Moreover, an unspecified focal infection was

labeled as an unidentified focal infection (UFI). The International Classification of Diseases version

10 was applied to determine and record focal infections (see supplement 2.1).

Cost calculationCost was analyzed from a payer perspective using billing records that included the costs of beds,

drugs, laboratory and radiology procedures, other medical facilities, and total costs. Bed costs

encompassed hospital administration fees, daily room services, nursing and medical staff care,

and technicians’ services. Drug costs were extracted from the pharmacy department’s budget

that covered expenses relating to drugs, fluids, blood products for transfusion, disposable

devices, mechanical ventilators, oxygen therapy, and pharmacy services. Physiotherapists’ – as

rehabilitation specialists – consultancy costs were recorded and considered under patients bed

service costs. Costs for administrations, patient transfer, and ambulance, and other expenses

were included in the costs for other medical facilities. The hospitalization costs per admission

were analyzed, considering the day spent in an intensive care unit (ICU), presence of SSIs, types

of focal infections, and whether the patient survived or not. The 2016 currency exchange rate

(US$1 = 13,308.33 IDR) was used, as applied by the Organization for Economic Cooperation and

Development (OECD) to convert Indonesian Rupiahs (IDR) into US Dollars (US$)24, with inflation

rates of 6.40% for 2013, 6.42% for 2014, 6.38% for 2015, and 3.53% for 2016.25 The economic burden

of sepsis was assessed according to the distribution of disease incidence over focal infections and

the mean cost of each focal infection using a denominator of 100,000 patients with sepsis.26

Extrapolation of the cost to the national levelThe national costs for sepsis were analyzed based on the rates defined by the Indonesian Health

Ministry for Indonesia Case Base Groups (INA-CBGs). The INA-CBGs’ rates were used as national

projections for extrapolating the sepsis costs – obtained from patient’s billing records – into

Proposed National Prices (PNPs) for sepsis reimbursements by considering the following four

aspects.18 The first aspect concerned the room classes in the hospital, which were divided into

three classes. Class I, patients had more privacy within one room, accommodating up to two

patients. Class II accommodating three or four people; Class III service accommodating five or

six people in a room.18,27 This study provided the PNP in Class III as the reference. It calculated the

actual costs from Classes I, II, and III ) ( CP )– obtained from patient’s billing records – and divided

them by the specific factor (α) according to the INA-CBGs at 1.4, 1.2, and 1.0, respectively.18

The second aspect concerned the private or public sector ownership of the hospital. In the

INA-CBG system, reimbursement provided by the government through subsidies was 1.03 (β) times

higher for private healthcare services compared with the public healthcare services.18 The third

and fourth aspects concerned the type of hospital and the region where the hospital is located, to

correspond with the specific INA-CBG prices (ICPy) that were published by the Indonesian Ministry

of Health in 2016.18 The classification of hospital type in Indonesia was categorized into types A,

The burden and costs of sepsis

2

26

B, C and D on the basis of the medical specialist services (see Supplement 2.2).18,27,28 There were

five INA-CBG regions covering 34 provinces in total (Figure 2.1).18 The ICP for the Hospital Type A in

Region I was used as the denominator reference for ICP in the calculation of a PNP, since the actual

costs were obtained from the hospitals with type A located in the INA-CBG Region I. Eventually,

for a particular focal infection inpatient, in a class of room, in a specific type of hospital in a certain

region under the private or the public sectors, a PNP for sepsis with an x focal infection was

defined as in the following Formula 2.1:

11"

"

#%#! & '("#) *(+"#"

+"##$%&'()*+,"&-+.+'/+0-1'$/+2*,-"

R3,;&'*"1<!"I)7"K3&,"*8M7=28"K3,"%767'3M+:9"*"0F0"J7,7"2)7";7*:"*=2&*'"=3828",7K'7=2+:9"2)7"

8+:9'7";7*:"='*88"M,+=7"!"#$$$$HO"2)7"8M7=+K+="K*=23,"GvH"3K"7*=)"5'*88",33;O"2)7"8M7=+K+="AF#@5EN"M,+=78"GA50.HO"*:%"2)7"9367,:;7:2"8&$8+%?"K*=23,"GwH<""

"

I)+8"82&%?"%767'3M7%"1\T"0F08"G8767:"K3=*'"+:K7=2+3:8O"K3&,"2?M78"3K")38M+2*'8O"2J3"87=23,8O"

*:%"K+67",79+3:8H"K3,",7+;$&,87;7:2"3K"87M8+8"J+2)"M*,2+=&'*,"K3=*'"+:K7=2+3:8"+:"2)7"K+67"AF#@

5EN" ,79+3:8<" I3" =3;M*,7" J+2)" 2)7" ,7K7,7:=7" A508O" 2)7" 0F08" J7,7" =*2793,+.7%" +:23" 2),77"

9,3&M84"2)387"J+2)"*"8;*''"%+KK7,7:=7"J+2)"2)7"A50"3K"pQBmXTTO"*";7%+&;"%+KK7,7:=7"3K"QBmXTT"

@"!OTTTO"*:%"*";*U3,"%+KK7,7:=7"oQBm!OTTT<"

"

"L4>2*7!8-,"I)7"K+67",79+3:8"=367,7%"+:"2)7"A:%3:78+*"5*87"E*87"N,3&M"GAF#@5ENH"8?827;<"-79+3:"!"G+:"9,77:H"=3;M,+878"E*:27:O"k*/*,2*O"`782"k*6*O"57:2,*'"k*6*O"x39?*/*,2*O"*:%"D*82"

k*6*<"-79+3:"1"G+:"$'&7H"=3;M,+878"`782"B&;*2,*O"-+*&O"B3&2)"B&;*2,*O">*;M&:9O"E*'+O"*:%"

`782"F&8*"I7:99*,*<"-79+3:"V"G+:",7%H"=3;M,+878"#=7)"L*,&88*'*;O"F3,2)"B&;*2,*O"k*;$+O"

E7:9/&'&O"E*:9/*"E7'+2&:9O"-+*&"A8'*:%8O"̀ 782"(*'+;*:2*:O"F3,2)"B&'*J78+O"57:2,*'"B&'*J78+O"

B3&2)7*82" B&'*J78+O"`782" B&'*J78+O" B3&2)" B&'*J78+O" *:%" N3,3:2*'3<" -79+3:" W" G+:" ?7''3JH"

=3;M,+878"B3&2)"(*'+;*:2*:O"D*82"(*'+;*:2*:O"F3,2)"(*'+;*:2*:O" *:%"57:2,*'"(*'+;*:2*:<"

-79+3:"X"G+:"M&,M'7H"=3;M,+878"D*82"F&8*"I7:99*,*O"C*'&/&O"F3,2)"C*'&/&O"0*M&*O"*:%"`782"

0*M&*<"I)7";*M"J*8"=,7*27%"+:";*M=)*,2<:72<"

"

,#3#*+#*(35$3"35/+'+$

L*2*" J7,7" *:*'?.7%" &8+:9" AEC" B0BB" 82*2+82+=8" 1XO" M,36+%+:9" %78=,+M2+67" %*2*" 3:" $*87'+:7"

=)*,*=27,+82+=8" +:"M7,=7:2*978<"5)+@8Y&*,7" 27828"J7,7"M7,K3,;7%" 23"%727,;+:7" 2)7"%+KK7,7:=78"

$72J77:"8&,6+6+:9"*:%"%7=7*87%"87M8+8"M*2+7:28<"!OTTT"8*;M'78"J7,7"$33282,*MM7%O"*:%"+:"=*878"

J)7,7"2)7"%*2*"J7,7"367,'?"8/7J7%O"2)7"82*:%*,%"7,,3,"GBDH"J*8"*%U&827%"K3,"2)7";7*:"=382<"#:"

Formula 2.1 The four aspects for developing a PNP were the mean actual costs reflecting the single mean class price( CP ), the specific factor (α) of each Class room, the specific INA-CBG prices (ICPy), and the government subsidy factor (β).

This study developed 280 PNPs (seven focal infections, four types of hospitals, two sectors, and

five regions) for reimbursement of sepsis with particular focal infections in the five INA-CBG

regions. To compare with the reference ICPs, the PNPs were categorized into three groups: those

with a small difference with the ICP of <US$500, a medium difference of US$500 - 1,000, and a

major difference >US$1,000.

Figure 2.1 The five regions covered in the Indonesia Case Base Group (INA-CBG) system. Region 1 (in green) comprises Banten, Jakarta, West Java, Central Java, Yogyakarta, and East Java. Region 2 (in blue) comprises West Sumatra, Riau, South Sumatra, Lampung, Bali, and West Nusa Tenggara. Region 3 (in red) comprises Aceh Darussalam, North Sumatra, Jambi, Bengkulu, Bangka Belitung, Riau Islands, West Kalimantan, North Sulawesi, Central Sulawesi, Southeast Sulawesi, West Sulawesi, South Sulawesi, and Gorontalo. Region 4 (in yellow) comprises South Kalimantan, East Kalimantan, North Kalimantan, and Central Kalimantan. Region 5 (in purple) comprises East Nusa Tenggara, Maluku, North Maluku, Papua, and West Papua. The map was created in mapchart.net.

Chapter 2

27

Statistical analysesData were analyzed using IBM SPSS statistics 25, providing descriptive data on baseline

characteristics in percentages. Chi-square tests were performed to determine the differences

between surviving and deceased sepsis patients. 1,000 samples were bootstrapped, and in cases

where the data were overly skewed, the standard error (SE) was adjusted for the mean cost. An

Independent Sample T-test was applied to evaluate the statistical cost difference between the

surviving and deceased patient groups. Subgroup analyses of hospitalization costs relating to ICU

treatment, having SSIs, and types of focal infections were performed. Statistical significance was

defined when the p-value was < 0.05.

RESULTS

Of the 14,076 patients with sepsis, 5,876 (41.7%) survived and 8,200 (58.3%) died. The patients were

predominantly male (53%). The average age among all patients was 49.4 (+18.9) years. Surviving and

deceased sepsis patients evidenced statistical differences for the following single focal infections:

LRTIs (38% vs. 62%, respectively, p <0.001), UTIs (56% vs. 44%, respectively, p <0.001), and WIs (18%

vs. 82%, respectively, p <0.001). Thirty-one percent of the sepsis patients were diagnosed with

multifocal infections with a significant difference between surviving and deceased patients (40%

vs. 60%, respectively, p <0.001). Of the 2,138 sepsis patients with SSIs, 74.2% died. Also, patients

with sepsis who were hospitalized in an ICU demonstrated a high case fatality rate (69%). Table

2.1 presents a summary of the clinical characteristics of surviving and deceased sepsis patients.

Table 2.1. Baseline characteristics of surviving and deceased sepsis patients

Characteristics All cases (n=14,067) % Survivors

(n=5,876) % Deceased (n=8,200) % p-value

Sex Male 7,467 53.0 3,115 41.7 4,352 58.3 0.943Female 6,609 47.0 2,761 41.8 3,848 58.2

Aged >60 years 1,638 11.6 626 38.2 1,012 61.8 0.002Single focal infections

CVI 110 0.8 39 35.5 71 64.5 0.179GTI 1,328 9.4 565 42.5 763 57.5 0.534LRTI 3,932 27.9 1,486 37.8 2,446 62.2 <0.001*NMI 368 2.6 153 41.6 215 58.4 0.947UTI 1,348 9.6 755 56.0 593 44.0 <0.001*WI 1,049 7.5 191 18.2 858 81.8 <0.001*

Multifocal infections 4,304 30.6 1,700 39.5 2,604 60.5 <0.001*UFI sepsis 1,637 11.6 987 60.3 650 39.7 <0.001*Having SSIs 2,138 15.2 551 25.8 1,587 74.2 <0.001*ICU 4,297 30.8 1,328 30.9 2,969 69.1 <0.001*

Note: CVI = cardiovascular infections, GTI = gastrointestinal tract infection, ICU = intensive care unit, LRTI = lower-respiratory tract infection, NMI = neuromuscular infection, SSI = surgical site infection, UFI = unidentified focal infection, UTI = urinary tract infection, and WI = wound infection.*Statistically significant, p < 0.05

The burden and costs of sepsis

2

28

Hospitalization costsThe costs per admission for surviving and deceased sepsis patients were, respectively, US$1,011

(+23.4) and US$1,406 (+27.8) (i.e., a difference of US$396, p <0.001). The mean cost for all sepsis

cases was US$1,253 (+19.4). Among non-ICU sepsis patients, the average cost was lower for

surviving patients (US$960 [+24.3]) compared with that of deceased patients (US$1,189 [+23.6])

per admission (p <0.001). For ICU sepsis patients, the cost per admission was US$1,618 (+47.9),

with respective mean costs of US$1,187 (+61.7) and US$1,785.5 (+56.3) for surviving and deceased

patients (p <0.001). The cost incurred for patients with sepsis who had SSIs was higher compared

with that incurred for patients who did not have SSIs (US$2,938 vs. US$926). Table 2.2 shows these

costs divided into unit costs for beds, laboratory and radiology, pharmacy, and other medical

facilities.

The national burden of sepsisThe analyses of the treatment costs per admission for sepsis patients with focal infections (see

Table 2.2) indicated that the cost was highest for sepsis patients with CVIs (US$1,731), followed

by those with WIs (US$1,703), multifocal infections (US$1,584), LRTIs (US$1,122), NMIs (US$986),

UTIs (US$748), and GTIs (US$720). The national burden of sepsis revealed a total budget of US$130

million (+US$5,7 million) per 100,000 patients. Sepsis with multifocal infections had the highest

national burden of disease within 100,000 sepsis patients (US$48 million), followed by sepsis with

LRTIs (US$33 million), UFIs (US$15 million), UTIs (US$11 million), GTIs (US$10.7 million), WIs (US$8.6

million), NMIs (US$2.7 million), and CVIs (US$0.9 million). Figure 2.2 depicts the economic burden

of sepsis with focal infections.

Figure 2.2 The economic burden of sepsis with particular focal infections for 100,000 patients with survived (in green) and deceased (in blue).

Note: CVI = cardiovascular infections, GTI = gastrointestinal tract infection, LRTI = lower-respiratory tract infection, NMI = neuromuscular infection, UFI = unidentified focal infection, UTI = urinary tract infection, and WI = wound infection.

Chapter 2

29

Table 2.2 Hospitalization costs for sepsis patients per admission (in 2016 US$)

Hospitalization cost All casesmean(SE)

Survived mean(SE)

Deceasedmean(SE) Cost difference p-value

Non-ICU stayBed costs 222.12(3.72) 196.31(5.17) 242.16(4.95) 45.85(7.49) <0.001Laboratory and radiology costs 327.29(6.24) 276.49(8.65) 366.49(8.28) 90.01(12.55) <0.001

Pharmacy costs 404.61(7.15) 369.76(10.37) 431.74(9.53) 61.98(14.40) <0.001Other medical facilities costs 142.14(2.30) 126.49(3.24) 154.29(3.07) 27.80(4.64) <0.001ICU stayBed costs 330.29(9.81) 243.08(13.05) 364.27(11.52) 121.19(21.76) <0.001Laboratory and radiology costs 416.60(14.29) 297.47(18.40) 462.711(16.77) 165.25(31.74) <0.001

Pharmacy costs 662.612(20.59) 491.54(26.36) 729.47(24.19) 237.93(45.64) <0.001Other medical facilities costs 207.33(6.07) 151.53(7.56) 229.08(7.12) 77.56(13.45) <0.001Having SSIsNo 925.92(13.13) 838.59(19.75) 988.55(17.18) 149.96(26.58) <0.001*Yes 2,937.89(88.80) 2,595.84(133.88) 3,042.17(101.32) 446.33(209.61) 0.033*Types of focal infectionsCVI 1,731.09(90.18) 1,634.30(168.91) 1,750.87(98.95) 116.57(240.24) 0.628GTI 719.76(25.12) 618.06(33.50) 792.711(32.77) 174.65(50.70) 0.001*LRTI 1,122.47(29.76) 818.83(30.51) 1,306.77(37.42) 487.94(60.88) <0.001*NMI 985.62(73.65) 855.84(101.65) 1,076.29(95.69) 220.45(149.21) 0.140UTI 747.83(29.81) 733.51(41.95) 765.31(44.42) 31.81(59.91) 0.595WI 1,702.58(221.88) 1,579.36(264.01) 1,765(272.84) 186.60(468.17) 0.690Multifocal infections 1,583.51(19.36) 1,363.16(51.83) 1,723.78(56.05) 395.64(39.58) <0.001*UFI 1,268.26(65.14) 1,315.27(84.09) 1,197.25(102.94) 118.02(133.11) 0.375

Note: CVI = cardiovascular infections, GTI = gastrointestinal tract infection, ICU = intensive care unit, LRTI = lower-respiratory tract infection, NMI = neuromuscular infection, SSI = surgical site infection, SE = standard error, UFI = unidentified focal infection, UTI = urinary tract infection, and WI = wound infection.*Statistically significant, p < 0.05

The prospective national price for sepsis patientsThe lowest price within the INA-CBG system (ICP) was for UFI sepsis with the ICP at US$298 in a type

D public hospital in Region 1, for which a PNP of US$803 was estimated (difference: US$505). The

highest PNP was for sepsis with CVIs in type A private hospitals in Region 5 (US$4,256), compared

with the ICP of US$2,270 (difference: US$1,986). A remarkable difference between the PNP and ICP

was evident for healthcare services relating to sepsis with WIs in type A private hospitals in Region

5 (US$3,995 vs. US$1,421; difference: US$2,574). Reimbursement levels under the overall PNP for

sepsis were higher for all types of private hospitals compared with those for public hospitals (all

types) in all INA-CBG regions. Out of 280 PNPs, 87 (31.1%) had major differences from the reference

ICPs (>US$1,000). PNPs with a major difference were predominantly for reimbursement of sepsis

with WIs (Table 2.3). Supplement 2.3 presents the details between the PNPs and the rates specified

for the ICPs for sepsis with focal infections in all five regions of Indonesia.

The burden and costs of sepsis

2

30

Tabl

e 2.

3 Th

e pr

opos

ed n

atio

nal p

rice

per p

atie

nt fo

r sep

sis w

ith fo

cal i

nfec

tions

in a

ll fiv

e re

gion

s of I

ndon

esia

(in

2016

US$

)

Regi

onal

Hos

pita

l S

epsi

s with

GTI

Sep

sis w

ith N

MI

Sep

sis w

ith U

TI U

FI s

epsi

s S

epsi

s with

LRT

I S

epsi

s with

CVI

Seps

is w

ith W

I*

Regi

on 1

Pub

lic A

1

,296

.9

1,7

70.9

2

,084

.1

1

,721

.0

3,0

23.6

3

,897

.8

3,6

59.4

Priv

ate

A

1

,335

.9

1,8

24.1

2,14

6.7

1,7

72.6

3

,114.

3

4

,014.

7

3

,769.

2

Pub

lic B

1

,003

.8

1,2

39.3

1

,346

.6

1,2

04.3

1

,754.

6

2

,727

.7

2,6

54.4

Priv

ate

B

1

,033

.9

1,2

76.5

1

,329

.9

1,2

40.5

1

,807

.2

2,6

68.6

2

,734

.0

Pub

lic C

806

.2

9

95.3

1

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967

.2

1,59

4.7

2,11

8.4

2,2

81.1

Priv

ate

C

830

.3

1,0

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1

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9

96.2

1

,642

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2,18

1.9

2,3

49.6

Pub

lic D

669

.8

8

27.0

1

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803

.6

1,3

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1

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.1

1

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ate

D

6

89.9

851

.8

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827

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1,43

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on 2

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lic A

1

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2

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1

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ate

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0

1

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5

3

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3

4

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lic B

1

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8

1

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1

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2

1

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78.3

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ate

B

1

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1,2

88.0

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51.6

1

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2,8

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6

Pub

lic C

813

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1

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6

975

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2,13

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Priv

ate

C

837

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1

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1

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2

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0.7

Pub

lic D

675

.8

8

34.4

1

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8

10.8

1

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36.5

1

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ate

D

6

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1

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2

1

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on 3

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lic A

1

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5

1

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2

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2

1

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4

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ate

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1

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1

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lic B

1

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8

1

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8

1

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ate

B

1

,046

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1,2

91.8

1

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1

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9

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lic C

815

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lic D

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ate

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Chapter 2

31

Regi

onal

Hos

pita

l S

epsi

s with

GTI

Sep

sis w

ith N

MI

Sep

sis w

ith U

TI U

FI s

epsi

s S

epsi

s with

LRT

I S

epsi

s with

CVI

Seps

is w

ith W

I*

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on 4

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lic A

1

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1,8

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2

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4

1

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3

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2

4

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.0

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8.3

Priv

ate

A

1

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1

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2,2

04.6

1

,820

.4

3,19

8.4

4,12

3.1

3,8

71.0

Pub

lic B

1

,030

.9

1,2

72.8

1

,326.1

1

,236

.9

1,8

02.0

2

,801

.4

2,7

26.1

Priv

ate

B

1

,061

.8

1,31

1.0

1,3

65.9

1

,274

.0

1,8

56.0

2

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.4

2,8

07.9

Pub

lic C

827

.9

1,0

22.2

1

,203

.7

9

93.3

1

,637

.8

2,17

5.6

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Priv

ate

C

687

.9

8

49.3

1

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.9

8

25.3

1

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2

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1

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lic D

687

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8

49.3

1

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8

25.3

1

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1

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.2

1,8

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Priv

ate

D

7

08.5

874

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1,07

8.3

8

50.1

1,47

6.1

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Regi

on 5

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lic A

1

,374.

8

1

,877

.2

2,2

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1

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3

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Priv

ate

A

1

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0

1

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5

2

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.5

1,8

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3

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fect

ion.

The burden and costs of sepsis

2

32

DISCUSSION

In this study, the economic burden for focal infections associated with sepsis was comprehensively

determined in the resource-limited setting, in Indonesia. Sepsis was mostly induced by LRTIs,

accounting for the high associated total cost per patient. Besides LRTIs, the findings indicated a

strong correlation between high costs and having SSIs. The costs especially increased for patients

with multifocal infections. In the broader scale, the economic burden of sepsis with focal infections

was higher for deceased patients than for surviving patients. In the new Indonesian UHC system,

the reimbursement for sepsis entails four aspects: class of patient’s room, government subsidies,

type of hospital, and the INA-CBG region. Moreover, the current findings show the great difference

in costs between PNP and ICP especially for sepsis-related costs with the focal infections of WIs

and CVIs.

There is convincing evidence of a positive correlation between LRTIs and sepsis with regard

to mortality outcome.29 Over the last decade, LRTIs have been the most prevalent communicable

disease in Indonesia.30 The economic burden of sepsis with LRTIs in ICUs in a developing country

such as Turkey was estimated at US$2,722 per patient.31 In addition, LRTIs such as community-

acquired pneumonia contributes high morbidity in terms of more hospitalizations for ICU

admissions, requiring mechanical ventilators, and further sepsis complications.32–34 In addition,

elevated hospitalization costs for ICU patients with LRTIs were strongly associated with the use

of a mechanical ventilator, presence of severe sepsis and septic shock.31 Confirming these results,

some studies have reported that in addition to being induced by LRTIs, sepsis also originates from

WIs, GTIs, and UTIs (approximately 16.5%, 16.7%, and 28.3%, respectively).29,35,36 Sepsis arising from

GTIs and WIs is mostly associated with surgical wounds.29,37 Infections on the site of surgeries

after elective and emergency procedures that contribute to sepsis account for 5.8% and 24.8%,

respectively.35 A previous study covering 6.5 million elective surgeries performed in the United

States reported an incidence of 1.2% of post-surgical sepsis cases with a high mortality rate of

26%.38 The current data revealed a high case fatality rate of sepsis with SSI. SSI-related costs that

include medicines, prolonged length of stay, and readmission could rise to US$22,130 per patient.39

In the current study, sepsis with CVIs presented the highest cost per inpatient but accounted

for the lowest national economic burden for sepsis, with focal infections giving relatively low

numbers. In a previous systematic review, endocarditis was reported to be a rare disease with costly

consequences.40 Sepsis with UTIs, or urosepsis commonly causes kidney dysfunction, leading to

high mortality rates. In the current study, the urinary tract ranked third in incidence as an infection

site associated with sepsis. The incidence of urosepsis in the United States is about 30% and is

higher among women compared with men.41,42 The study was in line with the current findings,

where among UTIs the female and male ratio was at 2:1. The incidence of sepsis-associated with

multifocal infections remains unknown, particularly in developing countries, but it we found that

they are the costliest. Identifying multisource infections with sepsis prior to the occurrence of

organ dysfunction is thus an urgent task.43

The further impacts of sepsis-related costs should be considered when formulating a national

Chapter 2

33

budget to support private and public healthcare services. In 2016, Indonesia’s health expenditure

was approximately US$111.6 billion or 3.1% of its GDP.44 Thus, establishing sufficient healthcare

facilities to support the care of sepsis patients is a challenge. According to the National Health

Account data published by the OECD in 2016, Indonesia’s inpatient expenditure amounted to

IDR158,499.2 billion (or US$11.9 billion).24,44 This expenditure accounts for 40.9% of the country’s

national total health expenditure of IDR387,648.5 billion or US$29.1 billion.44 For the sepsis

inpatient expenditure, the current findings suggest that the prices in the current INA-CBGs should

be upwardly adjusted as well as made specific for infection sites. As a specific item in the INA-

CBGs, each individual pays health coverage according to the class of service selected. The service

class categories merely relate to the provision of rooms with specific numbers of beds. Therefore,

this categorization is ineffective, as all patients receive the same medical services or even when

they are placed in ICUs or isolated rooms. Additionally, community healthcare centers, which play

an essential role in resource-limited settings in preventing infection complications such as sepsis,

could potentially serve as a budget control mechanism by averting hospital infections and then

reducing inpatient costs.41

It is believed that this is the first study to assess the burden of disease, incorporating the costs

and mortality outcomes of sepsis with focal infections in a resource-limited setting. Notably, it

offers a robust methodology for calculating the national price for sepsis based on a consideration

of particular focal infections. However, the study had several limitations. First, it did not assess the

costs associated with losses in productivity during hospitalization, and indirect costs were not

recorded. Moreover, infrastructure costs – such as security systems, parking, and transportation

costs – were not included. Second, post-sepsis impact on individual patients’ occupational or

educational trajectories, and those of their relatives, was not assessed because the data obtained

from the hospitals were not linked to the socioeconomic statuses of individual patients. Third,

the national price was modeled with reference to four referral centers. Nevertheless, the resulting

national model seemed reasonable. Forth, it was a retrospective study and potential bias could

have existed such as misdiagnosis and under-reported focal infections. However, the study was

conducted with a big sample size to provide epidemiological and health economic findings that

are needed by the Indonesian government for improving the new health insurance system with a

resource-limited setting. Last, it did not consider following hospital discharge, particularly for ICU

patients. Evidently, the higher mortality rate among sepsis patients after being discharged was a

late-onset outcome of their ICU stays.45–47

CONCLUSIONS

It is essential to consider mortality and focal infections in an assessment of the burden of sepsis.

Each underlying focal infection determines the particular course of sepsis. In a resource-limited

context such as that of Indonesia, where a new UHC system has been introduced, the adequate

provision of healthcare services requires a reevaluation and recalculation of the price for sepsis.

The burden and costs of sepsis

2

34

Furthermore, in context, sepsis cases with multifocal infections and LRTIs should be categorized

as high-burden sepsis cases, reflecting the most obvious examples requiring adjustments to the

national price for private and public healthcare services reimbursement.

ETHICAL APPROVAL

The study was approved by the ethical committee of Dr. Soetomo General Academic Hospital,

Surabaya (No. 418/Panke.KKE/VII/2017), Airlangga University Hospital (No. 114/KEH/2017), and

the National Center of Infectious Diseases at Prof. Dr. Sulanti Saroso Hospital, Jakarta (No. 02/

xxxviii.10/5/2018). The study met the Indonesian governmental requirements on conducting

research and the ethical principles for medical research involving human subjects under the

Helsinki Declaration.48 All data was deidentified to guarantee patient anonymity.

Abbreviations:CVI: Cardiovascular infection

GTI: Gastrointestinal tract infection

IDR: Indonesian Rupiah

ICP: INA-CBG price

ICU: Intensive care unit

INA-CBG: Indonesia Case Base Group

LMIC: Low-Middle Income Country

LRTI: Lower-respiratory tract infection

NMI: Neuromuscular infection

OECD: Organization for Economic Cooperation and Development

PNP: Proposed National Price

qSOFA: quick Sequential Organ Failure Assessment

SIRS: Systemic inflammatory response syndrome

SOFA: Sequential Organ Failure Assessment

SSI: Surgical site infection

UHC: Universal health coverage

UTI: Urinary tract infection

WI: Wound infection

Chapter 2

35

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14. Tufan ZK, Eser FC, Vudali E, et al. The Knowledge of the Physicians about Sepsis Bundles is Suboptimal: A Multicenter Survey. J Clin Diagn Res. 2015;9(7):OC13-6. doi:10.7860/JCDR/2015/12954.6220

15. Reimann HA, Havens WP. Focal infection and systemic disease: A critical appraisal. The case against indiscriminate removal of teeth and tonsils clinical lecture at St. Louis session. J Am Med Assoc. 1940;114(1):1-6. doi:10.1001/jama.1940.02810010003001

16. Babu NC, Gomes AJ. Systemic manifestations of oral diseases. J Oral Maxillofac Pathol. 2011;15(2):144-147. doi:10.4103/0973-029X.84477

17. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi:10.1001/jama.2016.0287

18. Health Ministry of the Republic of Indonesia. Health Ministry, Republic of Indonesia No. 52, 2016, The standard tarif for healthcare service in the implementation of universal health coverage. (Standar tarif kesehatan dalam penyelenggaraan program jaminan kesehatan). http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No._52_Tahun_2016_Tentang_Standar_Tarif_Pelayanan_Kesehatan_Dalam_Penyelenggaraan_JKN_.pdf. Published 2016. Accessed August 7, 2019.

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20. Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):1644-1655.

21. Kaukonen K-M, Bailey M, Pilcher D, Cooper DJ, Bellomo R. Systemic inflammatory response syndrome criteria in defining severe sepsis. N Engl J Med. 2015;372(17):1629-1638. doi:10.1056/NEJMoa1415236

22. Health Ministry of the Republic of Indonesia. A National Guideline for the Medical Services in the Management of Sepsis No. HK.01.07/MENKES/342/2017. (Pedoman Nasional Pelayanan Kedokteran Tata Laksana Sepsis). http://hukor.kemkes.go.id/uploads/produk_hukum/KMK_No._HK_.01_.07-MENKES-342-2017_ttg_Pedoman_Pelayanan_Kedokteran_Tata_Laksana_Sepsis_.pdf. Published 2017. Accessed January 13, 2020.

23. Horan TC, Gaynes RP, Martone WJ, Jarvis WR, Emori TG. CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol. 1992;13(10):606-608.

24. Organization for Economic Cooperation and Development. Exchange rates. https://data.oecd.org/conversion/exchange-rates.htm. Published 2016. Accessed August 6, 2019.

25. Worldwide Inflation Data. Historic inflation Indonesia. https://www.inflation.eu/inflation-rates/indonesia/historic-inflation/cpi-inflation-indonesia.aspx. Published 2020. Accessed April 25, 2020.

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26. The World Bank. Suicide mortality rate. https://data.worldbank.org/indicator/SH.STA.SUIC.P5?locations=ID. Published 2016. Accessed April 25, 2020.

27. President of Republic of Indonesia. Regulations of President of the Republic of Indonesia No. 19, 2016, the second edition of the changes of the president regulation No. 12, 2013, regarding universal health coverage. (Perubahan Kedua atas Peraturan Presiden Republik Indonesia nomor 19 tahun. https://bpjs-kesehatan.go.id/bpjs/arsip/detail/537. Published 2016. Accessed August 7, 2019.

28. Health Ministry of the, Indonesia R of. The classification and licence for the hospital No. 30, 2019 (Tentang Klasifikasi dan Perijinan Rumah Sakit). http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__30_Th_2019_ttg_Klasifikasi_dan_Perizinan_Rumah_Sakit.pdf. Published 2019. Accessed January 3, 2020.

29. Jaja BNR, Jiang F, Badhiwala JH, et al. Association of Pneumonia, Wound Infection, and Sepsis with Clinical Outcomes after Acute Traumatic Spinal Cord Injury. J Neurotrauma. 2019;36(21):3044-3050. doi:10.1089/neu.2018.6245

30. National report of basic health research (Laporan nasional Riskesdas 2018), Ministry of Health, Republic of Indonesia. http://labdata.litbang.depkes.go.id/riset-badan-litbangkes/menu-riskesnas/menu-riskesdas/426-rkd-2018. Published 2018. Accessed November 13, 2019.

31. Gumus A, Cilli A, Cakin O, et al. Factors Affecting Cost of Patients with Severe Community-Acquired Pneumonia in Intensive Care Unit. Turkish Thorac J. 2019;20(4):216-223. doi:10.5152/TurkThoracJ.2018.18084

32. Sligl WI, Marrie TJ. Severe community-acquired pneumonia. Crit Care Clin. 2013;29(3):563-601. doi:10.1016/j.ccc.2013.03.00933. Remington LT, Sligl WI. Community-acquired pneumonia. Curr Opin Pulm Med. 2014;20(3):215-224. doi:10.1097/

MCP.000000000000005234. Montull B, Menéndez R, Torres A, et al. Predictors of Severe Sepsis among Patients Hospitalized for Community-Acquired

Pneumonia. PLoS One. 2016;11(1):e0145929. doi:10.1371/journal.pone.014592935. Shankar-Hari M, Harrison DA, Ferrando-Vivas P, Rubenfeld GD, Rowan K. Risk Factors at Index Hospitalization

Associated With Longer-term Mortality in Adult Sepsis Survivors. JAMA Netw open. 2019;2(5):e194900. doi:10.1001/jamanetworkopen.2019.4900

36. Mayr FB, Yende S, Angus DC. Epidemiology of severe sepsis. Virulence. 2014;5(1):4-11. doi:10.4161/viru.2737237. Muresan MG, Balmos IA, Badea I, Santini A. Abdominal Sepsis: An Update. J Crit care Med (Universitatea Med si Farm din

Targu-Mures). 2018;4(4):120-125. doi:10.2478/jccm-2018-002338. Vogel TR, Dombrovskiy VY, Carson JL, Graham AM, Lowry SF. Postoperative sepsis in the United States. Ann Surg.

2010;252(6):1065-1071. doi:10.1097/SLA.0b013e3181dcf36e39. Purba AKR, Setiawan D, Bathoorn E, Postma MJ, Dik J-WH, Friedrich AW. Prevention of Surgical Site Infections: A Systematic

Review of Cost Analyses in the Use of Prophylactic Antibiotics. Front Pharmacol. 2018;9:776. doi:10.3389/fphar.2018.0077640. Abegaz TM, Bhagavathula AS, Gebreyohannes EA, Mekonnen AB, Abebe TB. Short- and long-term outcomes in infective

endocarditis patients: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2017;17(1):291. doi:10.1186/s12872-017-0729-5

41. Kumar MB, Madan JJ, Achieng MM, et al. Is quality affordable for community health systems? Costs of integrating quality improvement into close-to-community health programmes in five low-income and middle-income countries. BMJ Glob Heal. 2019;4(4):e001390. doi:10.1136/bmjgh-2019-001390

42. Esper AM, Moss M, Lewis CA, Nisbet R, Mannino DM, Martin GS. The role of infection and comorbidity: Factors that influence disparities in sepsis. Crit Care Med. 2006;34(10):2576-2582. doi:10.1097/01.CCM.0000239114.50519.0E

43. Zhou X, Su L-X, Zhang J-H, Liu D-W, Long Y. Rules of anti-infection therapy for sepsis and septic shock. Chin Med J (Engl). 2019;132(5):589-596. doi:10.1097/CM9.0000000000000101

44. The World Bank. The World Bank. Current health expenditure per capita (current US$). https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD?end=2016&most_recent_year_desc=true&start=2016&view=map. Published 2016. Accessed November 13, 2019.

45. Freitas FTM, Araujo AFOL, Melo MIS, Romero GAS. Late-onset sepsis and mortality among neonates in a Brazilian Intensive Care Unit: a cohort study and survival analysis. Epidemiol Infect. 2019;147:e208. doi:10.1017/S095026881900092X

46. Biason L, Teixeira C, Haas JS, Cabral C da R, Friedman G. Effects of Sepsis on Morbidity and Mortality in Critically Ill Patients 2 Years After Intensive Care Unit Discharge. Am J Crit Care. 2019;28(6):424-432. doi:10.4037/ajcc2019638

47. Aguiar-Ricardo I, Mateus H, Goncalves-Pereira J. Hidden hospital mortality in patients with sepsis discharged from the intensive care unit. Rev Bras Ter intensiva. 2019;31(2):122-128. doi:10.5935/0103-507X.20190037

48. World Medical Association. Ethical Principles for Medical Research Involving Human Subjects Version 2013; 64th World Medical Association General Assembly, Fortaleza, Brazil, October 2013. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/. Published 2013. Accessed July 11, 2019.

Chapter 2

37

SUPPLEMENT 2.1

International Classification of Diseases version 10 for patient selectionSepsis

- A41.x: other sepsis

- A40.x: Streptococcal sepsis

- A02.1: Salmonella sepsis

- A22.7: Anthrax sepsis

- A24.1: Melioidosis sepsis

- A54.8: Gonococcal sepsis

- B37.7: Candidal sepsis

- R65.1: Systemic inflammatory response syndrome of infectious origin with organ failure

severe

- R65.20: severe sepsis without septic shock

- R65.21: severe sepsis with septic shock

- T81.12XA: postprocedural septic shock, initial encounter

- T81.12XD: postprocedural septic shock, subsequent encounter

- T81.12XS: postprocedural septic shock, sequela

- R65.10: SIRS of noninfectious origin without acute organ dysfunction

- R65.11: SIRS of noninfectious origin with acute organ dysfunction

Sites of infections:

1. Infections in the gastrointestinal tract

- A00: cholera

- A01: typhoid

- A02: other salmonella infections

- A03: shigellosis

- A04: other bacterial intestinal infections

- A05: other bacterial foodborne intoxications

- A06: amoebiasis

- A07: other protozoal intestinal diseases

- A08: viral and other specified intestinal infections (A08.0 rotavirus enteritis)

- A09: other infections in gastrointestinal tracts

- B15-B19: viral hepatitis

- K21.0: Gastro-oesophageal reflux disease with oesophagitis

2. Infections in the lower respiratory tract

- A15-A19: tuberculosis

- J00-J99: lower and upper respiratory diseases

3. Infections in the urinary tracts

- N39: urinary tract infections

The burden and costs of sepsis

2

38

4. Nervous systems

- A39: bacteremia meningococcal

- A80-A89: infections of the central nervous system

- G00-G09: bacterial meningitis

o G01: meningitis in bacterial diseases classified elsewhere

o G02-G04: meningitis in other infectious and parasitic diseases classified elsewhere

o G05: encephalitis, myelitis, and encephalomyelitis in diseases classified elsewhere

5. Musculoskeletal

- M01: direct infections of joint in infectious and parasitic diseases classified elsewhere

- M00-M03: infectious arthropathies

6. Oral sites

- K04.7: Abscess in alveolar, apical, dental, dentoalveolar, lateral (alveolar), periapical,

periodontal, teeth-root, infection of a tooth, and dental infection

- K11.3: abscess of salivary gland

- K14.0: Abscess of tongue

- K04.6-K04.7: periapical abscess

- K05.21: periodontal abscess

- J36: peritonsillar abscess

7. Wound

- L00-L08: infections of the skin and subcutaneous tissue

- T20-T32: burns

- T8: complications of surgical and medical care, not classified elsewhere

o T81: wound infections

o T82: Infection and inflammatory reaction due to other cardiac and vascular devices,

implants and grafts

o T85.7: Infection and inflammatory reaction due to other internal prosthetic devices,

implants and grafts

o T87: post-amputation infection

- O86: obstetric surgical wound infection

8. Cardiovascular dan cardiovascular systems

- I30: acute pericarditis

- I31: other diseases of the pericardium

- I32: pericarditis in diseases classified elsewhere

- I33: acute and subacute endocarditis

- I38: endocarditis, valve unspecified

- I39: endocarditis and heart valve disorders in diseases classified elsewhere

- I40: acute myocarditis

- I41: myocarditis in diseases classified elsewhere

Chapter 2

39

SUPPLEMENT 2.2Medical specialties for minimum requirements in Hospitals Type A, B, C and D (Health Ministry of Republic of Indonesia 2019)

Hospital type

Type A Type B Type C Type DPrimary medical specialties

- Internist- Pediatric- Surgeon- Obstetrician and gynecologist

- Internist- Pediatric- Surgeon- Obstetrician and

gynecologist

- Internist- Pediatric- Surgeon- Obstetrician and

gynecologist

- Internist- Pediatric

Secondary medical specialties

- Ophthalmologist- Otolaryngologist- Neurologist- Cardiologist- Dermatologist- Psychiatrist- Pulmonologist- Orthopedic - Urologist- Neurosurgeon- Plastic surgeon- Cardiothoracic and vascular surgeon

- Ophthalmologist- Otolaryngologist- Neurologist- Cardiologist- Dermatologist- Psychiatrist- Pulmonologist- Orthopedic

N/A N/A

Tertiary medical specialties

- Anesthesiologist- Physical medicine and rehabilitation specialist- Radiologist- Clinical pathologist- Anatomy pathologist- Clinical microbiologist- Clinical nutritionist- Clinical parasitologist- Clinical pharmacologist

- Anesthesiologist- Physical medicine and

rehabilitation specialist- Radiologist- Clinical pathologist- Anatomy pathologist- Clinical microbiologist

Anesthesiologist N/A

Quaternary medical specialties or Sub-medical specialties

- A surgeon with a digestive subspecialist- An internist with a gastroenterohepatology

subspecialist- An internist with hypertension and nephrology

subspecialist- A pediatrician with a neonatology subspecialist- An obstetrician with a fetomaternal subspecialist- An ophthalmologist with a subspecialist- A pulmonologist with a subspecialist- An anesthesiologist with a subspecialist in intensive

therapy

N/A N/A N/A

Source: Ministry of Health of the Republic of Indonesia. The classification and license for the hospital No. 30, 2019. Available:http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__30_Th_2019_ttg_Klasifikasi_dan_Perizinan_Rumah_Sakit.pdf. Published 2019. Accessed January 3, 2020.Note: N/A = not available

The burden and costs of sepsis

2

40

SUPP

LEM

ENT

2.3

A co

mpa

rison

of t

he p

ropo

sed

natio

nal p

rice

for s

epsi

s with

foca

l inf

ectio

ns w

ith th

e ra

tes s

peci

fied

for t

he In

done

sia

Case

Bas

e G

roup

s in

all fi

ve re

gion

s of

Indo

nesi

a (in

US$

) H

ospi

tal

Seps

is w

ith L

RTI

Seps

is w

ith U

TISe

psis

with

GTI

Seps

is w

ith N

MI

Seps

is w

ith W

ISe

psis

with

CVI

UFI

sep

sis

IC

P P

NP

Diff

ICP

PN

P D

iffIC

P P

NP

Diff

ICP

PN

P D

iffIC

P P

NP

Diff

ICP

PN

P D

iffIC

P P

NP

Diff

Reg

ion

1

Pub

lic A

1,6

90.8

3,02

3.6

1,332

.81,3

47.8

2,08

4.173

6.3

1,088

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96.9

208.

41,3

64.2

1,770

.940

6.7

1,301

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659.

42,

357.7

2,07

9.0

3,89

7.81,

818.

863

9.11,7

21.0

1,081

.9

Priv

ate

A 1,7

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3,114

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1,388

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1,335

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4.7

1,405

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824.1

418.

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2,42

8.4

2,141

.44,

014.

71,

873.

465

8.3

1,772

.61,1

14.3

Pub

lic B

98

1.2

1,754

.677

3.4

870.

81,3

46.6

475.

784

2.5

1,003

.816

1.3

954.

71,2

39.3

284.

694

4.3

2,65

4.4

1,710

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54.9

2,72

7.71,2

72.8

447.2

1,204

.375

7.1

Priv

ate

B 1,0

10.6

1,80

7.279

6.6

860.1

1,329

.946

9.986

7.71,0

33.9

166.

298

3.3

1,276

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3.197

2.6

2,73

4.0

1,761

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23.4

2,66

8.6

1,245

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1,240

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lic C

89

1.8

1,594

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758.

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72.1

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676.

680

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676

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0

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ate

C 91

8.5

1,642

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426.

569

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lic D

78

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827.0

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ate

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823.

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1,87

4.7

874.

830

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Reg

ion

2

Pub

lic A

1,7

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3,05

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1,344

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ate

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542

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450.

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4,05

0.9

1,89

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664.

21,7

88.5

1,124

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lic B

99

0.0

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763.

9

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ate

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875.

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992.

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898

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2,75

8.6

1,777

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1,322

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1,251

.678

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lic C

89

9.8

1,609

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9.3

764.

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82.6

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961

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ate

C 92

6.8

1,657

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0.5

787.7

1,218

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703.

283

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796.

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843.

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370.

71,5

27.4

1,174

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201.6

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lic D

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620.

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1,83

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850

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ate

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374.

358

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859.

419

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731

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525.

0

Reg

ion

3

Pub

lic A

1,7

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3,05

9.8

1,348

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12.5

210.

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61,

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94.9

Priv

ate

A 1,7

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72.4

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457.6

2,167

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27.7

Pub

lic B

99

2.9

1,775

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845.1

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966.

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288.

095

5.6

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1,218

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ate

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8.9

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299

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1,255

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lic C

90

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1,613

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419.1

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ate

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8

Chapter 2

41

Hos

pita

lSe

psis

with

LRT

ISe

psis

with

UTI

Seps

is w

ith G

TISe

psis

with

NM

ISe

psis

with

WI

Seps

is w

ith C

VIU

FI s

epsi

s

ICP

PN

P D

iffIC

P P

NP

Diff

ICP

PN

P D

iffIC

P P

NP

Diff

ICP

PN

P D

iffIC

P P

NP

Diff

ICP

PN

P D

iff P

ublic

D

789.

71,4

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622.

566

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31.6

364.

556

8.9

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108.

964

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919

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54.3

982.

41,

841.9

859.

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351

1.2

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ate

D

813.

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51,

845.

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1,011

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897.2

885.

331

1.183

7.752

6.6

Reg

ion

4

Pub

lic A

1,7

36.5

3,105

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1,384

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40.4

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1,332

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758.

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35.1

4,00

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7.965

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ate

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677

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430.

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3,87

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494.

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99.2

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01,

820.

41,1

44.4

Pub

lic B

1,0

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1,80

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385

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586

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1,236

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ate

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1,85

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883.

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lic C

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4

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ate

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284

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lic D

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8

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ate

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4

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ion

5

Pub

lic A

1,7

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ate

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878.

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81.2

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lic B

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lic C

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887

7.455

1.6

Not

e: C

VI =

car

diov

ascu

lar i

nfec

tions

, diff

= d

iffer

ence

, GTI

= g

astro

inte

stin

al tr

act i

nfec

tion,

ICP

= IN

A-CB

G p

rice.

LRT

I = lo

wer

-resp

irato

ry tr

act i

nfec

tion.

NM

I = n

euro

mus

cula

r inf

ectio

n, P

NP

= pr

opos

ed n

atio

nal p

rice,

SSI

= su

rgic

al si

te in

fect

ion,

UFI

= u

nide

ntifi

ed fo

cal i

nfec

tion,

UTI

= u

rinar

y tr

act i

nfec

tion,

and

WI =

wou

nd in

fect

ion.

The burden and costs of sepsis

2

42

43

2Part

Prophylactic antibiotics for surgical site

infection prevention

44

45

CHAPTER 3

Prevention of surgical site infections:

A systematic review of cost analyses in the use of prophylactic antibiotics

Abdul Khairul Rizki Purba

Didik Setiawan

Erik Bathoorn

Maarten J. Postma

Jan-Willem Dik

Alex W. Friedrich

Frontiers in Pharmacology, 2018; 9(776): 1-18. doi: 10.3389/fphar.2018.00776.

46

ABSTRACT

Introduction: The preoperative phase is an important period in which to prevent surgical site

infections (SSIs). Prophylactic antibiotic use helps to reduce SSI rates, leading to reductions in

hospitalization time and cost. In clinical practice, besides effectiveness and safety, the selection

of prophylactic antibiotic agents should also consider the evidence with regard to costs and

microbiological results. This review assessed the current research related to the use of antibiotics

for SSI prophylaxis from an economic perspective and the underlying epidemiology of

microbiological findings.

Methods: A literature search was carried out through PubMed and Embase databases from 1

January 2006 to 31 August 2017. The relevant studies which reported the use of prophylactic

antibiotics, SSI rates and costs were included for analysis. The causing pathogens for SSIs were

categorized by sites of the surgery. The quality of reporting on each included study was assessed

with the “Consensus on Health Economic Criteria” (CHEC).

Results: We identified 20 eligible full-text studies that met our inclusion criteria which were

subsequently assessed studies had a reporting quality scored on the CHEC list averaging 13.03

(8-18.5). Of the included studies, 14 were trial-based studies, and the others were model-based

studies. The SSI rates ranged from 0 to 71.1% with costs amounting to US$480-22,130. Twenty-four

bacteria were identified as causative agents of SSIs. Gram-negatives were the dominant causes

of SSIs especially in general surgery, neurosurgery, cardiothoracic surgery, and obstetric cesarean

sections.

Conclusions: Varying results were reported in the studies reviewed. Yet, information from both

trial-based and model-based costing studies could be considered in the clinical implementation

of proper and efficient use of prophylactic antibiotics to prevent SSIs and antimicrobial resistance.

Nevertheless, the findings of economics and microbiology from the included studies have

reported diverse results.

Chapter 3

47

INTRODUCTION

Surgical site infections (SSIs) reflect an important complication in modern healthcare.1 As the

surgical site is a potential port entry for exogenous organisms, it poses an immediate threat to the

body and infections lead to prolonged wound healing.2,3 The preoperative phase is considered

the most crucial period of a surgical procedure in which the goal is to reduce the bacterial

load surrounding the incision area. Using antibiotics prior to surgical incision is considered to

be effective in preventing SSIs, which are among the most common preventable post-surgery

complications involving healthcare-associated infections (HAIs).2,4 A parenteral prophylaxis agent

has been recommended recently to reduce SSI rates efficiently.3 In contrast, some preoperative

procedures, such as hair removal and mechanical bowel preparation are considered today to be

inefficient in reducing SSIs.5,6

In the US, SSIs were identified in approximately 1.9% of 849,659 surgical procedures in 43

states from 2006 to 2008.7 The economic burden of SSIs should be taken into account in the

use of prophylactic antibiotics. In the US in 2010, more than 16 million surgical procedures were

performed.8 The annual costs of SSIs amounted to approximately US$3 billion in 2012, having

increased from an estimated US$1.6 billion cost attributable to SSIs in 2005.9,10 In low-and middle-

income countries, SSI rates doubled from 5.6 to 11.8 in 100 surgical patients between 1995 and

2008.11 The reporting of cost and effectiveness in infectious disease presents a crucial topic,

ideally supported by updated antimicrobial resistance data. Notably, economic analyses can be

differentiated into trial-based – directly linked to a trial that often also already comprises part

of the economic variables – and model-based studies with information gathered from various

sources and integrated into a health-economic model. The aim of this study is to present recent

evidence from trial-based and model-based costing studies and analyze the methodologies

used in economic evaluations of prophylactic antibiotics in SSI prevention. In addition, the

study comprehensively analyzes the quality of the included studies and local epidemiology of

pathogen-causing SSIs.

MATERIALS AND METHODS

This review was registered in PROSPERO with number CRD42017076589. This study was designed

according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)

statement.12

Search StrategyWe searched the updated relevant evidence from PubMed and EMBASE databases. To consider

changes over time in inflation rates, value of money, and patterns in microbial causes of SSIs and

contemporary antimicrobial susceptibility, we initially searched a ten-year period (2006-2016)

which we later updated to 31 August 2017. The search used search terms or phrases represented

Prevention of surgical site infections

3

48

in Medical Subject Headings (MeSH) with the operator ‘tiab’ for PubMed. Subsequently, the terms

or phrases used in PubMed were translated to the EMBASE database by using strings and the

symbols ‘ab,ti’. To refine the result, we employed a search strategy using the Boolean operator ‘OR’

within sequences of terms with close or similar meanings and ‘AND’ for one or more sequences of

terms that contained completely different meanings. Whole terms and phrases for either PubMed

or Embase were identified by two persons (AKRP and KS) who dealt with the search strategy.

Study SelectionTrial-based and model-based studies that examined the clinical benefits and costs related to

the uses of prophylactic antibiotics for SSIs were considered eligible for inclusion in this review.

Therefore, we developed criteria to identify the eligible studies which contained economic

analysis and followed the defined PICO-approach (Patient or Problem, Intervention, Control, and

Outcomes). Concerning the patient (P), patients undergoing all types of surgical procedures were

included. There was no restriction on age or gender. For both the intervention (I) and comparison

or control (C), this review included studies concerning the utilization of antibiotic prophylaxis

administered intravenously, orally, or locally to prevent SSI. Other terms of post-surgical infections

such as wound infections and sternal wound infections (SWIs) were included. We excluded

studies mainly evaluating comparisons of the use of antiseptic, pharmaceutical care interventions

or guideline adherence issues. For the outcomes (O), we included studies evaluating both SSI rates

and cost. Eventually, the integrated results of searches were restricted to English full-text studies.

Studies issued as commentary, editorials, research protocols or reviews were excluded.

Data ExtractionTwo authors (AKRP and DS) independently assessed all included papers. Any disagreements

between those authors were discussed with a third author (JWD) until the discrepancies were

resolved by consensus. Fields of the extracted data included the authorship, year of publication,

journal, country, type of surgery, wound categorization, gender, age, sample size, outcomes,

prophylactic antibiotics, SSI rates, the timing for the prophylactic strategy, follow-up and length of

stay. To address the outcome from a microbiology perspective, we extracted the pathogens based

on the sites of the surgery, antimicrobial susceptibility, and their resistance rates. Furthermore, we

grouped the types of SSIs based on the definitions and classifications of SSIs from the Centers for

Disease Control and Prevention (CDC).13

Cost Analysis and Data SynthesisWe categorized the methodology on the health-economic analyses and outcomes for each

eligible study according to four approaches.14 The first was cost-minimization analysis (CMA),

which represents a straightforward method to identify the costs of different alternatives with

estimated equal health outcomes of the interventions. The second was cost-effectiveness analysis

(CEA) where the outcomes are expressed in a natural unit of health including the number of

patients with clinical improvement of an infectious disease or life-years gained. The third was a

Chapter 3

49

cost-benefit analysis (CBA), in which the interventions are made comparable in terms of benefit

and cost with all aspects being expressed in financial units. The last was cost-utility analysis

(CUA), which includes utility estimates potentially representing preferences for health outcomes,

reporting quality-adjusted life years (QALYs) or alternatively disability-adjusted life years (DALYs).

Furthermore, for the cost types, we took into account cost perspectives with components

of (1) direct costs such as costs for prophylactic antibiotics, hospitalization, side-effects, and

antimicrobial resistance, and (2) indirect costs including costs of loss of productivity. We made

costs comparable among individual studies using currency conversions to US$ and corrections

for inflation rates. We calculated inflation rates based on the 2015 annual GDP growth index in the

World Data Bank for each respective country.15 If the individual article did not state the actual year

for the cost analyses, we made the assumption that the year of the cost estimate was the same as

the last year of data collection.

Quality AssessmentsWe used the Consensus on Health Economic Criteria (CHEC) list to assess the quality of reporting

of the health economic outcomes, including potential bias in individual studies.16 This CHEC

instrument comprises a 19-item list that relates to study design (4 items), time horizon, actual

perspective, cost evaluation (5 items), outcome measurements (3 items), discounting, conclusion,

generalization, conflict of interest, and ethical issues.17 These items can be conceived to reflect the

minimum requirements for health economic papers. We scored one point for “yes”, indicating an

item to be satisfied. Marks of “unclear” and “no” were scored half a point and zero respectively.

Therefore, the minimum and maximum scores for the individual studies were in a range of 0 to 19.

RESULTS

Search ResultsThis review initially identified a total of 644 and 1,417 articles from PubMed and Embase

respectively. A comprehensive listing of the searches in both PubMed and Embase can be found

in Supplement 3.1 and Supplement 3.2. After removing duplications, we screened 1,529 titles

and abstracts. Subsequently, we excluded 1,321 articles for the reasons listed in the Materials and

Methods section. Eventually, we assessed 208 eligible full-text studies of which we excluded 188

because of being reviews, having incomplete data related to costs and lack of presenting on

the outcomes of prophylactic antibiotic uses and SSI incidence. A total of 20 articles remained

according to the inclusion criteria and were extracted systematically for further analyses.18–30 A

flow chart of the search is shown in Figure 3.1.

Prevention of surgical site infections

3

50

Figure 3.1. Flow chart of search strategy on identifying eligible and included studies

General Characteristics of Included StudiesThe general characteristics of the included studies are presented in Table 3.1. For the further

characteristics of the 20 included studies, the most studies came from North-America18,19,21,24,30–33,

followed by Asia20,22,29,34, Europe26–28,35, Africa25,36, Australia23 and South America37. The reviewed

articles concerned 14 trial-based studies19–22,24–30,33,34,36 and 6 model-based studies18,23,31,35,38,39.

Table 3.2 presents the baseline characteristics of the included studies. Moreover, six trial-based

studies were performed as a formal randomized controlled trial (RCT) with the number of patients

involved in the studies ranging between 50 and 1,196.22,25,26,29,30,36

Chapter 3

51

Table 3.1. General characteristics of 20 included articlesCharacteristics Included articles n(%)RegionAfrica 2(10)Asia 4(20)Australia 1(5)Europe 4(20)North America 8(40)

South America 1(5)

Type of surgery

Cardiothoracic 2(10)General 5(25)Neurosurgery 2(10)Obstetric gynaecology 2(10)Oncology 3(15)Orthopedic 6(30)

Type of economic evaluation

CBA 0CEA 3(15)CMA 15(75)CUA 2(10)

Note: CMA, cost-minimization analysis; CBA, cost-benefit analysis; CEA, cost-effectiveness analysis; CUA, cost-utility analysis

Prevention of surgical site infections

3

52

Tabl

e 3.

2. B

asel

ine

char

acte

ristic

s of

incl

uded

stu

dies

reg

ardi

ng c

ount

ry, t

ype

of s

urge

ry, p

roph

ylac

tic a

ntib

iotic

s, ge

nder

, mea

n ag

e, n

umbe

r of

su

bjec

ts, o

utco

me

mea

sure

, stu

dy d

esig

n, a

ntib

iotic

susc

eptib

ility

, and

pro

phyl

actic

tim

ing

Aut

hor,

year

Coun

try

Type

of

surg

ery

Prop

hyla

ctic

ant

ibio

ticG

ende

rM

ean

age

(yea

rs)

Num

ber

of su

bjec

tO

utco

me

mea

sure

Stud

y de

sign

Ant

ibio

tic

susc

eptib

ility

Pr

ophy

lact

ic

timin

gSt

udy

Gro

upCo

ntro

l Gro

up

Gen

eral

surg

ery

Chau

dhur

i et

al., 2

006

Ger

man

yEx

cisio

n of

pi

loni

dal

sinus

es

Met

roni

dazo

le

500m

g i.v

.Ce

furo

xim

e 1.5

g i.v

.+

met

roni

dazo

le

500m

g i.v

.

F an

d M

27SG

: 25

CG: 2

5In

fect

ion-

rela

ted

wou

nd

com

plic

atio

ns,

tota

l cos

ts

Dou

ble-

blin

ded

RCT

NA

30 m

inut

es p

rior

to in

cisio

n an

d co

ntin

ued

to d

ay

5 fo

r mul

ti-dr

ug

Wils

on e

t al.,

2008

Un

ited

Stat

esEl

ectiv

e co

lore

ctal

su

rger

y

Erta

pene

m

1g i.v

.Ce

fote

tan

2g i.v

.F

and

MSG

: 61.3

; CG

: 60.

2SG

: 338

; CG

: 334

SSIs,

ant

ibio

tic u

se,

anas

tom

otic

leak

of

the

bow

el, c

ost

per d

ose,

dire

ct

med

ical

cos

ts

Obs

erva

tiona

l st

udy:

anal

ysis

from

PRE

VEN

T tri

al (I

tani

et a

l., 20

06)

NA

30 m

inut

es p

rior

to in

cisio

n

Mat

sui e

t al.,

2014

Un

ited

Stat

esLa

paro

scop

ic

chol

ecys

t-ec

tom

y

Cefa

zolin

so

dium

1g

i.v.

With

out

prop

hyla

ctic

an

tibio

tics

F an

d M

Ove

r 65:

197

pa

tient

s (S

G) a

nd

202

patie

nts

(CG)

SG: 5

18;

CG: 5

19Po

stop

erat

ive

infe

ctio

ns (S

SIs,

dist

ant i

nfec

tions

), ho

spita

l sta

y, an

tibio

tic c

osts

, di

rect

med

ical

co

sts

RCT

NA

Befo

re sk

in

inci

sion

Sing

h et

al.,

2014

Un

ited

Stat

esAb

dom

inal

su

rger

yTr

iclo

san-

coat

ed su

ture

sW

ithou

t tri

clos

an-

coat

ed su

ture

s

NA

All a

ges

incl

uded

1,000

in

divi

dual

sCo

st-s

avin

g of

tri

clos

an-c

oate

d su

ture

s whe

n SS

I-ris

ks w

ere

5%, 1

0%

and

15%

Mod

el-b

ased

st

udy

NA

NA

Ozd

emir

et

al., 2

016

Turk

eyEl

ectiv

e co

lore

ctal

re

sect

ions

Cefa

zolin

1g

i.v. a

nd

met

roni

dazo

le

500m

g i.v

. pl

us m

etro

-ni

da zo

le

4g p

.o. +

ge

ntam

icin

48

0mg

p.o.

Cefa

zolin

1

g i.v

. and

m

etro

nida

zole

50

0mg

i.v.

F an

d M

SG: 5

8CG

: 59

SG: 4

5CG

:45

SSIs,

leng

th o

f ho

spita

l sta

ys,

cost

-sav

ing

Retro

spec

tive

stud

yN

ASt

art f

rom

dur

ing

anes

thes

ia

indu

ctio

n to

5

days

pos

t-op

erat

ion

Chapter 3

53

Aut

hor,

year

Coun

try

Type

of

surg

ery

Prop

hyla

ctic

ant

ibio

ticG

ende

rM

ean

age

(yea

rs)

Num

ber

of su

bjec

tO

utco

me

mea

sure

Stud

y de

sign

Ant

ibio

tic

susc

eptib

ility

Pr

ophy

lact

ic

timin

gSt

udy

Gro

upCo

ntro

l Gro

up

Ort

hope

dic

Ellio

tt e

t al.,

2010

Un

ited

King

dom

Prim

ary

hip

arth

ropl

asty

Ceph

alos

porin

Vanc

omyc

in o

r co

mbi

natio

n of

van

com

ycin

an

d ce

phal

ospo

rin

M65

1770

SSIs,

MRS

A in

fect

ion,

leng

th

of st

ay, m

orta

lity,

utili

ty in

QAL

Ys,

cost

s

Mod

el-b

ased

st

udy

NA

NA

Cour

ville

et

al., 2

012

Unite

d St

ates

Tota

l hip

an

d kn

ee

arth

ropl

asty

Nas

al

mup

iroci

nW

ithou

t m

upiro

cin

NA

Hyp

othe

ti-ca

l coh

ort o

f 65

-yea

r-old

NA

SSIs,

util

ity in

Q

ALYs

, cos

tsM

odel

-bas

ed

stud

yN

AN

A

Mer

ollin

i et

al., 2

013

Aust

ralia

Tota

l hip

ar

thro

plas

tyAn

tibio

tic

prop

hyla

xis

No

antib

iotic

pr

ophy

laxi

s, an

tibio

tic-

impr

egna

ted

cem

ent,

lam

inar

air

oper

atin

g ro

oms.

NA

65 y

ears

30,0

00

hypo

thet

i-ca

l co

hort

s

SSIs,

util

ity in

Q

ALYs

, leng

th o

f st

ay, m

orta

lity

Dec

ision

mod

el

and

cost

-eff

ectiv

enes

s an

alys

is

NA

NA

Theo

logi

s et

al., 2

014

Unite

d St

ates

Thor

acol

umba

r ad

ult d

efor

mity

re

cons

truct

ion

Intra

veno

us

antib

iotic

s and

va

ncom

ycin

po

wde

r 2 g

Intra

veno

us

antib

iotic

sF

and

MSG

: 62.

4;CG

: 60.

0SG

: 151

CG: 6

4SS

Is, u

tility

in

QAL

Ys, a

dd-o

n im

preg

nate

d pr

ophy

lact

ic

antib

iotic

cos

t, co

st-s

avin

g

Retro

spec

tive

coho

rt st

udy

NA

NA

Gra

ves e

t al.,

2016

Un

ited

Stat

esPr

imar

y hi

p re

plac

emen

tN

o sy

stem

ic

antib

iotic

s Sy

stem

ic

antib

iotic

s N

AN

A77

,321

Dee

p in

fect

ions

, ut

ility

in Q

ALYs

, co

sts

Mod

el-b

ased

st

udy

NA

NA

Ceba

llos e

t al

., 201

7 Co

lom

bia

Low

er li

mb

ampu

tatio

nCe

fazo

lin,

ceph

alot

hin,

ce

fota

xim

e,

cefo

xitin

, ce

furo

xim

e

Non

-pr

ophy

lact

ic

antib

iotic

s

NA

NA

10,0

00

simul

a-tio

ns

Dec

ision

ana

lytic

m

odel

of

supe

rfici

al a

nd

deep

infe

ctio

ns,

heal

ing,

seps

is,

re-a

mpu

tatio

n,

mor

talit

y, co

sts

Mod

el-b

ased

st

udy

NA

NA

Prevention of surgical site infections

3

54

Aut

hor,

year

Coun

try

Type

of

surg

ery

Prop

hyla

ctic

ant

ibio

ticG

ende

rM

ean

age

(yea

rs)

Num

ber

of su

bjec

tO

utco

me

mea

sure

Stud

y de

sign

Ant

ibio

tic

susc

eptib

ility

Pr

ophy

lact

ic

timin

gSt

udy

Gro

upCo

ntro

l Gro

up

Neu

rosu

rger

y

Emoh

are

et

al., 2

014

Unite

d St

ates

Post

erio

r spi

nal

surg

ery

Cefa

zolin

i.v

. and

va

ncom

ycin

po

wde

r 1 g

in

tra-w

ound

Cefa

zolin

i.v.

F an

d M

SG: 5

3.7;

CG: 5

8.2

SG: 9

6;

CG: 2

07SS

Is, in

tra-w

ound

co

sts,

dire

ct c

osts

Retro

spec

tive

coho

rt st

udy

NA

NA

Lew

is et

al.,

2017

Un

ited

Stat

esCr

ania

l sur

gery

an

d su

bdur

al

or su

bgal

eal

drai

ns

PPSA

s of

Cefa

zolin

and

Va

ncom

ycin

Non

-PPS

As

F an

d M

SG: 5

9CG

: 57

SG: 1

05CG

: 80

SSIs,

dire

ct c

osts

an

d co

st sa

ving

Retro

spec

tive

stud

yN

AN

A

Card

ioth

orac

ic su

rger

y

Dha

dwal

et

al., 2

007

Unite

d Ki

ngdo

mCo

rona

ry a

rter

y by

pass

gra

fting

su

rger

y

Rifa

mpi

cin

600m

g p.

o.,

gent

amic

in

2mg/

kg i.v

. and

va

ncom

ycin

15

mg/

kg i.v

.

Cefu

roxi

me

1.5g

i.v.

F an

d M

SG: 6

2.8;

CG

: 65.

4SG

: 87;

CG: 9

9SW

Is, a

ntib

iotic

an

d ho

spita

l cos

tsD

oubl

e-bl

inde

d RC

T N

Arif

ampi

cin

1h

preo

pera

tivel

y, ge

ntam

icin

and

va

ncom

ycin

af

ter a

nest

hesia

in

duct

ion

Josh

i et a

l., 20

16

Unite

d Ki

ngdo

mCa

rdia

c su

rger

y in

hig

h ris

k of

SW

I

Gen

tam

icin

-im

preg

nate

d co

llage

n sp

onge

s

With

out

gent

amic

in-

impr

egna

ted

colla

gen

spon

ges

NA

NA

1251

SWI i

ncid

ence

, m

edia

n po

stop

erat

ive

cost

, ann

ual

addi

tiona

l cos

t fo

r SW

Is an

d th

e ge

ntam

icin

-im

preg

nate

d co

llage

n sp

onge

s

Obs

erva

tiona

l st

udy

NA

NA

Obs

tetr

ic g

ynec

olog

ical

surg

ery

Alek

we

et a

l., 20

08

Nig

eria

Elec

tive

cesa

rean

se

ctio

n

Ceftr

iaxo

ne 1

g

i.v.

Ampi

cilli

n/

clox

acill

in

1g q

.i.d. i.

v.,

gent

amic

in

80m

g t.i

.d.

i.v. a

nd

met

roni

dazo

le

500m

g t.i

.d. i.

v.

FSG

: 33.

53;

CG: 3

2.08

SG: 1

00CG

: 100

Infe

ctio

us

mor

bidi

ty,

endo

met

ritis,

UTI

, fe

brile

mor

bidi

ties,

wou

nd in

fect

ions

, du

ratio

n of

ho

spita

l sta

y, an

tibio

tic c

osts

RCT

NA

NA

Chapter 3

55

Aut

hor,

year

Coun

try

Type

of

surg

ery

Prop

hyla

ctic

ant

ibio

ticG

ende

rM

ean

age

(yea

rs)

Num

ber

of su

bjec

tO

utco

me

mea

sure

Stud

y de

sign

Ant

ibio

tic

susc

eptib

ility

Pr

ophy

lact

ic

timin

gSt

udy

Gro

upCo

ntro

l Gro

up

Kosu

s et a

l., 20

10

Turk

eyCe

sare

an

sect

ion

Ceftr

iaxo

ne

1g i.v

. and

rif

amyc

in

250m

g

Ceftr

iaxo

ne

1g i.v

.F

SG: 2

8.4;

CG: 2

6.8

SG: 5

96;

CG: 6

00SS

I rat

es, c

ost f

or

rifam

ycin

and

SSI

tre

atm

ents

RCT

NA

NA

Onc

olog

ic su

rger

y

Patil

et a

l., 20

11

Indi

aH

ead

and

neck

on

co-s

urge

ries

Sing

le

antib

iotic

of

cefa

zolin

, or

cipr

oflox

acin

, or

cef

proz

il, or

cl

inda

myc

in

Com

bina

tion

antib

iotic

s of

cefa

zolin

and

m

etro

nida

zole

, or

clin

dam

ycin

an

d ge

ntam

icin

, or

am

pici

llin/

cl

oxac

illin

, or

mox

iflox

acin

an

d m

etro

nida

zole

, or

cip

roflo

xaci

n an

d m

etro

nida

zole

, or

cef

proz

il an

d m

etro

nida

zole

F an

d M

NA

50Po

st-o

pera

tive

wou

nd in

fect

ions

, co

sts f

or

prop

hyla

ctic

an

tibio

tics a

nd

post

-ope

rativ

e an

tibio

tics

Obs

erva

tiona

l st

udy

NA

NA

Gul

luog

lu e

t al

., 201

3 Tu

rkey

Brea

st c

ance

r su

rger

yAm

pici

llin-

sulb

acta

m

1g i.v

.

With

out

prop

hyla

ctic

an

tibio

tics

FSG

: 58.

8; C

G:

58.2

SG: 1

87;

CG: 1

82SS

Is, ti

me

to S

SIs,

cultu

re re

sults

, ad

vers

e re

actio

ns

due

to a

ntib

iotic

s, m

ean

SSI-r

elat

ed

cost

s

RCT

NA

NA

El-M

ahal

law

y et

al.,

2013

Eg

ypt

Canc

er su

rger

y(b

ladd

er,

stom

ach,

col

on,

rect

um)

Peni

cilli

n G

sodi

um

4,00

0,00

0IU

i.v. a

nd

gent

amic

in 8

0 m

g i.v

.

Clin

dam

ycin

60

0mg

i.v.

and

amik

acin

50

0mg

i.v.

F an

d M

<40

year

s: 72

>40

year

s: 12

8

SG: 1

00CG

: 100

SSI i

ncid

ence

an

d co

st fo

r pr

ophy

lact

ic

antib

iotic

s

RCT

NA

NA

CG, c

ontro

l Gro

up; F

, fem

ale;

i.v.,

intr

aven

ous;

M, m

ale;

MRS

A, M

ethi

cilli

n-re

sista

nce

Stap

hylo

cocc

us a

ureu

s; N

A, n

ot a

vaila

ble;

p.o

., pe

r ora

l; PP

SAs,

prol

onge

d pr

ophy

lact

ic s

yste

mic

ant

ibio

tics;

QAL

Ys, q

ualit

y ad

just

ed li

fe y

ears

; q.i.d

., qua

rter

in d

ie (f

our t

imes

a d

ay);

RCT,

rand

omiz

ed c

ontro

l tria

l; SG

, stu

dy g

roup

; SSI

s, su

rgic

al si

te in

fect

ions

; SW

Is, st

erna

l wou

nd in

fect

ions

; t.i.d

., ter

in d

ie

(thre

e tim

es a

day

); U

TI, u

rinar

y tr

act i

nfec

tio

Prevention of surgical site infections

3

56

Antibiotic Prophylaxis in General SurgeryFive included studies analyzed the cost and effectiveness of antibiotic prophylaxis in general

surgery.21,26,30,31,34 The types of surgery were pilonidal sinus excision26, elective colorectal surgery21,34,

laparoscopic cholecystectomy30, and general abdominal surgery31. The included studies indicated

that new generation antibiotics generated economic benefit in SSI prevention. An observational

study reported that the use of ertapenem in elective colorectal surgery achieved cost savings of

roughly US$2,200 per patient compared with cefotetan.21 The secondary costs due to selection

regarding resistance were not taken into account in this study and would need to be assessed

in future studies. Another study showed that triclosan-coated sutures seemed to be cost-saving

and effective at reducing SSI rates from the hospital, payer, and societal perspectives.31 However,

no long-term data on tissue-toxicity and possible triclosan-induced inflammatory response was

included in this study. In addition, single prophylactic antibiotics and both oral or intravenous

administration were demonstrated to have a positive impact on reducing SSI rates and medical

costs in general surgery.26,30

Antibiotic Prophylaxis in Orthopedic surgeryVarious studies modeled economic and clinical impacts from the societal and healthcare

perspectives of patients undergoing total hip arthroplasty (THA), total knee arthroplasty (TKA),

and lower limb amputation.18,23,35,38,39 Economic analysis on the implementation of the use

of nasal mupirocin to prevent deep SSI of Staphylococcus aureus in THA and TKA showed that

mupirocin was more cost-effective compared to non-preoperatively administered mupirocin with

incremental cost-effectiveness ratios (ICERs) at US$380.09/QALY and US$517.16/QALY for THA and

TKA respectively.18 Vancomycin has also been taken into account as an intra-wound antibiotic,

with SSI rates of 3% and 11% were identified in the group with and without 2g vancomycin

powder, respectively. Clinically and economically, these percentages were considered to reflect a

significant impact with cost savings of US$2,762 per operative procedure at day 90 post-surgery 24. Furthermore, two studies addressed that prophylactic intervention was dominant over no

prophylactic antibiotics on SSI rates and cost reductions in total hip arthroplasty and lower limb

amputation.23,39

Antibiotic Prophylaxis in NeurosurgeryTwo cost-minimization studies on neurosurgery concerned intra-wound vancomycin and

Prolonged Prophylactic Systemic Antimicrobials (PPSAs).19,33 Firstly, a cohort study, for the purpose

of reimbursement to the hospital for SSI costs, evaluated the cost savings achieved by adding

intra-wound vancomycin powder as prophylactic therapy to standard intravenous cefazolin in

patients who underwent spinal surgery. No SSIs were reported in patients who received intra-

wound vancomycin, whereas 7 out of 207 patients who were given only cefazolin developed

SSIs at a cost of US$2,879 per patient.19 Secondly, a retrospective study looked into the duration

of prophylactic antibiotic use in cranial surgery and subdural or subgaleal drains. Continuous

prophylactic antibiotics or PPSAs were considered costly compared with non-PPSA treatment in

the operation, which saved US$93,195 per patient.33

Chapter 3

57

Antibiotic Prophylaxis in Cardiothoracic SurgeryTwo included RCTs and an observational study evaluated the clinical and economic impact of

antibiotics for the prevention of SWIs in coronary surgery. First, one RCT study reported that the

use of triple antibiotics of rifampicin gentamicin, and vancomycin for SSI prophylaxis could reduce

the total cost of treatment by US$4,521 per patient compared to single prophylaxis of cefuroxime.27

Second, an observational study on Gentamicin-impregnated Collagen Sponges (GCS) to prevent

SSIs in cardiac surgery noted a unit cost of GCS of roughly US$129 per patient. Nevertheless, in their

cost analysis, they remarked that GCS provided no economic benefit in reducing SSI incidence in

a two-year period.28

Antibiotic Prophylaxis in Obstetric and Gynecological SurgeryIn obstetric and gynecological surgery, two included RCT-based studies analyzed SSI incidence and

performed an economic impact analysis of ceftriaxone prophylactic in delivery through cesarean

section. The first RCT compared single-dose prophylactic ceftriaxone to a triple-drug combination

of ampicillin/cloxacillin, gentamicin, and metronidazole. Notwithstanding the economic benefit

of a single dose of ceftriaxone compared with the combination regimen, the SSI rates were

between 7% with ceftriaxone and 8% with the triple drugs.25 The second RCT was carried out on

the implementation of subcutaneous rifamycin as an add-on therapy for prophylactic ceftriaxone.

Twelve allocated subjects for the standard prophylactic were followed up with SSI, and in these

cases, the total cost related to SSI treatment amounted to US$483 per patient. On the other hand,

no patient developed SSI by the end of the follow-up period in the intervention group.29

Antibiotic Prophylaxis in Oncology SurgeryThree included studies concerned different operative procedures in oncology surgery used

prophylactic antibiotics for malignancy of the breast, head-neck, bladder, stomach, colon, and

rectum.20,22,36 An observational study in surgery for head and neck cancer by Patil et al.,20 conveyed

that no significant difference was indicated in the total cost between single and combination

antibiotics. On the other hand, an RCT study on breast cancer surgery by Gulluoglu et al.,22

presented that antibiotic prophylaxis with intravenous ampicillin-sulbactam was cost-saving

and effective compared to no prophylaxis, resulting in a 9% reduction in the SSI rate and a cost

reduction of US$11 per patient. Moreover, another RCT on abdominal cancer by El-Mahallawy

et al.,36 indicated cost savings with the combination of penicillin and gentamicin overusing

clindamycin and amikacin. Table 3.3 compares the included studies on reporting cost analysis. In

addition, the SSI rates and the cost ranges of each surgical procedure are presented in Table 3.4.

Prevention of surgical site infections

3

58

Tabl

e 3.

3. C

ompa

rison

s of i

nclu

ded

stud

ies o

n re

port

ing

of c

ost i

ndex

yea

r, co

st a

naly

sis m

etho

d, c

ost p

ersp

ectiv

e, a

nd a

djus

ted

cost

s in

US$

at 2

015

infla

tion

rate

Stud

y, y

ear o

f pu

blic

atio

n

Cost

in

dex

year

Cost

an

alys

is

met

hod

Cost

pe

rspe

ctiv

eA

djus

ted

cost

s in

US$

at 2

015-

infla

tion

rate

Gen

eral

surg

ery

Chau

dhur

i et a

l., 20

06

2006

CMA

NA

Tota

l cos

t in

the

grou

p w

ith a

sing

le-d

ose

Met

roni

dazo

le: U

S$11

.53

per p

atie

ntTo

tal c

ost f

or S

SI c

ompl

icat

ions

: US$

813.

25 p

er p

atie

nt

Wils

on e

t al.,

2008

20

05CM

AN

ACo

st p

er d

ose

of e

rtap

enem

: US$

47.8

6 pe

r pat

ient

Cost

per

dos

e of

cef

otet

an: U

S$29

.78

per p

atie

ntD

irect

med

ical

cos

t in

the

grou

p w

ith e

tape

nem

pro

phyl

axis:

US$

16,4

33.8

9 pe

r pat

ient

Dire

ct m

edic

al c

ost i

n th

e gr

oup

with

cef

otet

an p

roph

ylax

is: U

S$18

,812

.66

per p

atie

nt

Mat

sui e

t al.,

2014

20

13CM

AN

ACo

st fo

r ant

ibio

tics i

n th

e gr

oup

with

cef

azol

in: U

S$25

.73

per p

atie

nt;

Cost

for a

ntib

iotic

s in

the

grou

p w

ithou

t pro

phyl

actic

: US$

8.37

per

pat

ient

;D

irect

med

ical

cos

t in

the

grou

p w

ith c

efaz

olin

: US$

791.5

9 pe

r pat

ient

;D

irect

med

ical

cos

t in

the

grou

p w

ithou

t pro

phyl

actic

: US$

859.

58 p

er p

atie

nt.

Sing

h et

al.,

2014

20

13CM

AH

ealth

care

, pa

yer a

nd

soci

etal

pe

rspe

ctiv

e

For 1

5% S

SI ri

sk, t

riclo

san-

coat

ed su

ture

save

d:-

US$4

,232

.27

– 14

,394

.25

per p

atie

nt (H

ospi

tal p

ersp

ectiv

e)-

US$4

,256

.99

– 14

,725

.91 p

er p

atie

nt (P

ayer

per

spec

tive)

- US

$41,3

30.8

1 –

54,8

41.3

2 pe

r pat

ient

(Soc

ieta

l per

spec

tive)

Ozd

emir

et a

l., 20

16

2016

CMA

NA

Tota

l hos

pita

l cos

t:-

In th

e gr

oup

with

cef

azol

in a

nd m

etro

nida

zole

intra

veno

usly

plu

s met

roni

dazo

le a

nd g

enta

mic

in o

rally

: US$

2,69

9 pe

r pa

tient

- In

the

grou

p w

ith c

efaz

olin

and

met

roni

dazo

le in

trave

nous

ly: U

S$4,

411

per p

atie

nt

Ort

hope

dic

Ellio

tt e

t al.,

2010

20

05CU

ASo

ciet

al

pers

pect

ive

Tota

l cos

t per

QAL

Y fo

r SSI

-trea

tmen

ts-

In th

e gr

oup

with

van

com

ycin

pro

phyl

actic

: US$

1,417

.78/

QAL

Y-

In th

e gr

oup

with

cep

halo

spor

in p

roph

ylac

tic: U

S$1,4

18.01

/QAL

Y-

In th

e gr

oup

with

com

bina

tion

prop

hyla

ctic

: US$

1,421

.48/

QAL

Y

Cour

ville

et a

l., 20

12

2005

CEA

Soci

etal

pe

rspe

ctiv

eAv

erag

e co

st p

er Q

ALY:

Tota

l hip

art

hrop

last

y: -

Trea

ted

with

mup

iroci

n: U

S$34

,990

.65/

QAL

Y-

Trea

ted

with

mup

iroci

n an

d sc

reen

ed p

ositi

ve fo

r S.a

ureu

s: US

$35,

308.

54/Q

ALY

- W

ithou

t mup

iroci

n: U

S$35

,370

.74/Q

ALY

Tota

l kne

e ar

thro

plas

ty:

- Tr

eate

d w

ith m

upiro

cin:

US$

41,3

68.18

/QAL

Y-

Trea

ted

with

mup

iroci

n an

d sc

reen

ed p

ositi

ve fo

r S.a

ureu

s: US

$41,7

75.92

/QAL

Y-

With

out m

upiro

cin:

US$

41,8

85.3

4/Q

ALY

Chapter 3

59

Stud

y, y

ear o

f pu

blic

atio

n

Cost

in

dex

year

Cost

an

alys

is

met

hod

Cost

pe

rspe

ctiv

eA

djus

ted

cost

s in

US$

at 2

015-

infla

tion

rate

Mer

ollin

i et a

l., 20

13

2011

CEA

Hea

lthca

re

pers

pect

ive

ICER

non

-pro

phyl

actic

com

pare

d w

ith p

roph

ylac

tic a

ntib

iotic

s: US

$9,91

7.14/

QAL

Y-lo

stAd

d-on

ant

ibio

tic-im

preg

nate

d pr

ophy

laxi

s sav

ing

US$4

,164.

81/Q

ALY-

gain

ed

Theo

logi

s et a

l., 20

14

2009

CMA

NA

Cost

for v

anco

myc

in p

owde

r: US

$38.

30 p

er o

pera

tive

proc

edur

eTo

tal c

ost i

n th

e gr

oup

with

van

com

ycin

pow

der:

US$7

8,74

5.18

per o

pera

tion

Tota

l cos

t in

the

grou

p w

ithou

t van

com

ycin

pow

der:

US$7

1,514

.31 p

er o

pera

tion

Cost

-sav

ing

usin

g va

ncom

ycin

pow

der:

US$2

76,17

4.26

per

100

ope

rativ

e pr

oced

ures

Gra

ves e

t al.,

2016

20

12CU

AH

ealth

care

pe

rspe

ctiv

eW

ith th

e re

fere

nce

of n

on-s

yste

mic

ant

ibio

tics +

pla

in c

emen

t + c

onve

ntio

nal v

entil

atio

n (T

0), IC

ERs o

f T1

to T

8:-

T1: U

S$12

0,98

9.52

/QAL

Y-

T2: U

S$83

,904

.20/

QAL

Y-

T3: U

S$75

,533

.82/

QAL

Y-

T4: U

S$88

,054

.96/

QAL

Y-

T5: U

S$95

,765.

38/Q

ALY

- T6

: US$

44,61

5.47

/QAL

Y-

T7: U

S$63

,185.1

3/Q

ALY

- T8

: US$

21,3

02/Q

ALY

Ceba

llos e

t al.,

2017

20

14CE

AH

ealth

care

pe

rspe

ctiv

eIn

crem

enta

l cos

t bet

wee

n no

n-pr

ophy

lact

ic a

nd p

roph

ylac

tic g

roup

: US$

1,245

.83

per p

atie

nt

Neu

rosu

rger

yEm

ohar

e et

al.,

2014

20

12CM

AN

ACo

st fo

r int

ra-w

ound

van

com

ycin

: US$

12.4

6 pe

r pat

ient

Dire

ct m

edic

al c

ost i

n th

e gr

oup

with

out i

ntra

-wou

nd v

anco

myc

in: U

S$2,

879.

02 p

er p

atie

nt

Lew

is et

al.,

2017

20

15CM

AN

ATh

e di

rect

cos

t for

PPS

As: U

S$88

7.50

per p

atie

ntCo

st-s

avin

g fo

r Non

-PPS

As: U

S$93

,194.

63 p

er p

atie

nt

Card

ioth

orac

ic su

rger

yD

hadw

al e

t al.,

2007

20

04CM

AN

ACo

st fo

r pro

phyl

actic

ant

ibio

tics:

- In

the

grou

p w

ith a

sing

le p

roph

ylac

tic a

ntib

iotic

: US$

540.1

8 pe

r pat

ient

- In

the

grou

p w

ith c

ombi

natio

n pr

ophy

lact

ic a

ntib

iotic

s: US

$425

.95

per p

atie

ntTo

tal h

ospi

tal c

osts

:-

In th

e gr

oup

with

a si

ngle

pro

phyl

actic

ant

ibio

tic: U

S$22

,130.

53 p

er p

atie

nt

- In

the

grou

p w

ith c

ombi

natio

n pr

ophy

lact

ic a

ntib

iotic

s: US

$17,6

09.2

4 pe

r pat

ient

Prevention of surgical site infections

3

60

Stud

y, y

ear o

f pu

blic

atio

n

Cost

in

dex

year

Cost

an

alys

is

met

hod

Cost

pe

rspe

ctiv

eA

djus

ted

cost

s in

US$

at 2

015-

infla

tion

rate

Josh

i et a

l., 20

16

2013

CMA

NA

Med

ian

cost

with

out S

SI: U

S$15

,502

.72

per p

atie

ntM

edia

n ad

ditio

nal c

ost f

or S

SI tr

eatm

ents

: US$

7,835

.59

per p

atie

ntCo

st fo

r the

GCS

: US$

128.

96 p

er p

atie

ntTo

tal a

nnua

l add

ition

al c

osts

in re

duci

ng S

SI in

cide

nce

by 5

0%

- w

ithou

t GCS

: US$

70,5

23.9

0 pe

r pat

ient

- w

ith G

CS: U

S$11

5,924

.59

per p

atie

nt

Obs

tetr

ic g

ynec

olog

ical

surg

ery

Ellio

tt e

t al.,

2010

20

08CM

AN

ACo

sts f

or a

ntib

iotic

s: -

In th

e gr

oup

with

a si

ngle

pro

phyl

actic

ant

ibio

tic: U

S$10

.61 p

er p

atie

nt-

In th

e gr

oup

with

com

bina

tion

prop

hyla

ctic

ant

ibio

tics:

US$1

6.52

per

pat

ient

Cour

ville

et a

l., 20

12

2007

CMA

NA

The

pric

e of

rifa

myc

in: U

S$1.5

8 pe

r pat

ient

M

ean

cost

for S

SI tr

eatm

ents

: US$

482.

59 p

er p

atie

nt

Onc

olog

ic su

rger

yPa

til e

t al.,

2011

20

07CM

AN

ACo

sts i

n th

e gr

oup

with

a si

ngle

ant

ibio

tic:

- Pr

ophy

lact

ic a

ntib

iotic

cos

ts: U

S$7.2

2 pe

r pat

ient

- Po

st-s

urgi

cal a

ntib

iotic

cos

ts: U

S$79

.76 p

er p

atie

nt-

Tota

l ant

ibio

tics c

osts

: US$

86 p

er p

atie

ntCo

sts i

n th

e gr

oup

with

a c

ombi

natio

n of

pro

phyl

actic

ant

ibio

tics:

- Pr

ophy

lact

ic a

ntib

iotic

cos

ts: U

S$12

.13 p

er p

atie

nt-

Post

-sur

gica

l ant

ibio

tics c

osts

: US$

82.7

9 pe

r pat

ient

- To

tal a

ntib

iotic

s cos

ts: U

S$94

.92 p

er p

atie

nt

Gul

luog

lu e

t al.,

2013

20

10CM

AN

ACo

sts f

or S

SI tr

eatm

ents

:-

In th

e gr

oup

with

pro

phyl

actic

ant

ibio

tics:

US$9

.18 p

er p

atie

nt-

In th

e gr

oup

with

out p

roph

ylac

tic a

ntib

iotic

s: US

$21.9

3 pe

r pat

ient

El-M

ahal

law

y et

al.,

2013

20

13CM

AN

AD

irect

cos

t in

the

grou

p w

ith p

enic

illin

G so

dium

and

gen

tam

icin

: US$

3.26

per

pat

ient

Dire

ct c

ost i

n th

e gr

oup

with

clin

dam

ycin

and

am

ikac

in: U

S$17

.39

per p

atie

nt

CMA,

cos

t m

inim

izat

ion

anal

ysis;

GCS

, Gen

tam

icin

col

lage

n sp

onge

s; IC

ER, i

ncre

men

tal c

ost-

effec

tiven

ess

ratio

; NA,

not

ava

ilabl

e; P

PSAs

, pro

long

ed p

roph

ylax

is sy

stem

ic a

ntim

icro

bial

s; T1

, sy

stem

ic a

ntib

iotic

s + p

lain

cem

ent +

con

vent

iona

l ven

tilat

ion;

T2,

non

-sys

tem

ic a

ntib

iotic

s + p

lain

cem

ent +

lam

inar

airfl

ow; T

3, s

yste

mic

ant

ibio

tics +

pla

in c

emen

t + la

min

ar a

irflow

; T4,

non

-sy

stem

ic a

ntib

iotic

s +

antib

iotic

-impr

egna

ted

cem

ent +

con

vent

iona

l ven

tilat

ion;

T5,

sys

tem

ic a

ntib

iotic

s +

antib

iotic

-impr

egna

ted

cem

ent +

con

vent

iona

l ven

tilat

ion;

T6,

sys

tem

ic a

ntib

iotic

+

antib

iotic

-impr

egna

ted

cem

ent +

lam

inar

airfl

ow; T

7, sy

stem

ic a

ntib

iotic

s +

antib

iotic

-impr

egna

ted

cem

ent +

ven

tilat

ion

+ bo

dy e

xhau

st s

uit;

T8, s

yste

mic

ant

ibio

tics

+ an

tibio

tic-im

preg

nate

d ce

men

t + la

min

ar v

entil

atio

n +

body

exh

aust

suit;

TH

A, to

tal h

ip a

rthr

opla

sty;

TKA

, tot

al k

nee

arth

ropl

asty

; US$

, the

Uni

ted

Stat

es D

olla

rs.

Chapter 3

61

Tabl

e 3.

4. C

ompa

rison

of s

elec

ted

stud

ies

on r

epor

ting

of S

SI c

lass

ifica

tion,

SSI

rat

e, s

tatis

tical

sig

nific

ance

, tim

ing

SSI i

dent

ified

, and

leng

th o

f ho

spita

lizat

ion

Stud

y, y

ear o

f pu

blic

atio

nSS

I cl

assi

ficat

ion

SSI r

ate*

Stat

istic

al

sign

ifica

nce

Tim

ing

SSI

iden

tified

Leng

th o

f ho

spita

lizat

ion

(day

s)G

ener

al su

rger

yCh

audh

uri e

t al.,

2006

Supe

rfici

al

Met

roni

dazo

le: 1

1(44

%)

Cefu

roxi

me

and

met

roni

dazo

le: 3

(12%

)p

valu

e: 0

.9, <

0.00

01 a

nd

<0.0

3 at

wee

k 1,

2 an

d 4

resp

ectiv

ely

Wee

k 1,2

, and

4N

A

Wils

on e

t al.,

2008

Supe

rfici

al,

deep

, org

an

spac

e

Erta

pene

m: 6

2(18

.3%

) Ce

fote

tan:

104

(31.1

%)

CI95

% a

bsol

ute

diffe

renc

e: -1

9.5-

6.5

Wee

k 4

Erta

pene

m: 9

; Ce

fote

tan:

11.6

Mat

sui e

t al.,

2014

NA

Cefa

zolin

: 4(0

.8%

) W

ithou

t pro

phyl

actic

ant

ibio

tic: 1

9(3.

7%)

p va

lue:

0.0

01D

ay 1

and

or d

ay

2 po

stop

erat

ive

Cefa

zolin

: 3.6

9 N

o an

tibio

tic: 4

.07

Sing

h et

al.,

2014

Supe

rfici

al a

nd

deep

SSI

An a

ssum

ptio

n of

SSI

-risk

for t

he tr

iclo

san

coat

ed su

ture

s tre

atm

ent:

5-20

%N

A30

-90

days

NA

Ozd

emir

et a

l.,201

6Su

perfi

cial

, de

ep a

nd

orga

n sp

ace

Com

bina

tion

of in

trave

nous

pro

phyl

axis

(cef

azol

in a

nd m

etro

nida

zole

) and

ora

l pr

ophy

laxi

s (m

etro

nida

zole

and

gen

tam

icin

): 16

(35.

6%)

Intra

veno

us p

roph

ylax

is on

ly (m

etro

nida

zole

and

gen

tam

icin

): 32

(71.1

%)

p va

lue<

0.00

130

day

sIn

trave

nous

onl

y: 14

.2Co

mbi

natio

n of

in

trave

nous

and

ora

l pr

ophy

laxi

s: 8.1

Ort

hopa

edic

Ellio

tt e

t al.,

2010

Supe

rfici

al a

nd

deep

/join

tVa

ncom

ycin

gro

up: 2

(0.4

%) i

nfec

ted

by M

RSA

and

41(9

.1%) i

nfec

ted

by o

ther

sCe

phal

ospo

rin g

roup

: 7(1

.6%

) inf

ecte

d by

MRS

A an

d 32

(7.4

%) i

nfec

ted

by o

ther

sN

A30

day

sN

A

Cour

ville

et a

l., 20

12D

eep

Prob

abili

ty a

mon

g M

upiro

cin-

treat

ed c

arrie

rs: 1

.3%

Prob

abili

ty a

mon

g no

n-M

upiro

cin

and

non-

carri

ers:

0.58

%N

ATi

me

horiz

on:

1 ye

arN

A

Mer

ollin

i et a

l., 20

13D

eep

Incr

emen

tal S

SI in

cide

nce:

-

In n

on-p

roph

ylac

tic a

ntib

iotic

gro

up o

ver t

he p

roph

ylac

tic g

roup

: 230

ca

ses

- In

add

-on

antib

iotic

-impr

egna

ted

cem

ent o

ver a

ntib

iotic

pro

phyl

axis:

pr

even

ted

46 c

ases

NA

Tim

e ho

rizon

: 30

year

sN

A

Theo

logi

s et a

l., 20

14N

AIn

trave

nous

ant

ibio

tics a

nd v

anco

myc

in p

owde

r: 4(

2.6%

)In

trave

nous

ant

ibio

tic o

nly:

7(10

.9%

)p

valu

e: 0

.0190

day

sN

A

Prevention of surgical site infections

3

62

Stud

y, y

ear o

f pu

blic

atio

nSS

I cl

assi

ficat

ion

SSI r

ate*

Stat

istic

al

sign

ifica

nce

Tim

ing

SSI

iden

tified

Leng

th o

f ho

spita

lizat

ion

(day

s)G

rave

s et a

l., 20

16D

eep

T0: 1

887

case

sT1

: 870

cas

esT2

: 670

cas

esT3

: 721

cas

esT4

: 950

cas

esT5

: 406

cas

esT6

: 666

cas

esT7

: 905

cas

esT8

: 112

6 ca

ses

CI95

%:

T0: 1

253-

2621

T1: 3

45-1

655

T2: 9

0-19

37T3

: 192

-158

9T4

: 286

-205

9 T5

: 90-

964

T6: 1

01-2

017

T7: 7

7-24

99T8

: 143

-282

7

Tim

e ho

rizon

: 30

day

s for

non

-im

plan

t and

1

year

for i

mpl

ant

proc

edur

es

NA

Ceba

llos e

t al.,

2016

Supe

rfici

al a

nd

deep

Prop

hyla

ctic

ant

ibio

tic: 6

2(16

.2%

)N

on-p

roph

ylac

tic a

ntib

iotic

: 44(

38.3

%)

NA

NA

NA

Neu

rosu

rger

yEm

ohar

e et

al.,

2014

Supe

rfici

al

(stu

dy g

roup

: 5

(5%

); co

ntro

l gr

oup:

5(2

%));

D

eep

(stu

dy

grou

p: 0

; co

ntro

l gr

oup:

7(3%

))

Cefa

zolin

and

van

com

ycin

: 0 o

ut o

f 96

Cefa

zolin

: 7(3

.4%

)N

A20

-22

mon

ths

NA

Lew

is et

al.,

2017

Supe

rfici

al a

nd

deep

PPSA

s: 2(

1.9%

)N

on-P

PSAs

: 1(1

.3%

)D

eep

SSI: p

=1.0

0Su

perfi

cial

SSI

: p=

0.77

90 d

ays

PPSA

s and

non

-PPS

As: 5

Card

ioth

orac

ic su

rger

yD

hadw

al e

t al.,

2007

Supe

rfici

al,

deep

, org

an

spac

e

Rifa

mpi

cin

+ ge

ntam

icin

+ v

anco

myc

in: 8

(9.2

%)

Cefu

roxi

me:

25(

25.3

%)

NA

Day

90

Trip

le a

ntib

iotic

s: 9.1

Si

ngle

ant

ibio

tic: 1

2

Josh

i et a

l., 20

16D

eep

and

supe

rfici

al

ster

nal w

ound

in

fect

ion

18(1

.4%

) dia

gnos

ed a

s SW

I in

a tw

o-ye

ar p

erio

dN

AN

AW

ards

: 5 (n

on-S

WI)

and

12.7

(SW

I)IC

U: 2

.5 (n

on-S

WI)

and

3 (S

WI)

Chapter 3

63

Stud

y, y

ear o

f pu

blic

atio

nSS

I cl

assi

ficat

ion

SSI r

ate*

Stat

istic

al

sign

ifica

nce

Tim

ing

SSI

iden

tified

Leng

th o

f ho

spita

lizat

ion

(day

s)O

bste

tric

gyn

ecol

ogic

al su

rger

yAl

ekw

e et

al.,

2008

Dee

pCe

ftria

xone

: 7(7

%)

Ampl

icox

+ g

enta

mic

in +

met

roni

dazo

le: 8

(8%

)p

valu

e: 0

.788

Day

3

Sing

le a

ntib

iotic

: 6.3

3;

Trip

le a

ntib

iotic

s: 6.

22

Kosu

s et a

l., 20

10Su

perfi

cial

and

de

epCe

ftria

xone

+ ri

fam

ycin

SV:

0 o

ut o

f 596

Ceftr

iaxo

ne: 1

2(2%

) p

valu

e <0

.05

Day

2, 5

, 40

7

Onc

olog

ic su

rger

y

Patil

et a

l., 20

11

NA

Sing

le a

ntib

iotic

: 11(

47.8

%)

Com

bina

tion

of a

ntib

iotic

s: 7(

25%

) N

AN

ASi

ngle

ant

ibio

tic: 3

6 Co

mbi

natio

n of

an

tibio

tics:

33

Gul

luog

lu e

t al.,

2013

Supe

rfici

alAm

pici

llin/

sulb

acta

m: 9

(4.8

%)

Non

-pro

phyl

actic

ant

ibio

tics:

25(1

3.7%

)N

AD

ay 3

0N

A

El-M

ahal

law

y et

al

., 201

3N

APe

nici

llin

G so

dium

+ g

enta

mic

in: 1

1(11

%)

Clin

dam

ycin

+ a

mik

acin

: 8(8

%)

p va

lue:

0.47

NA

NA

*the

pro

vide

d pe

rcen

tage

s wer

e th

e pe

rcen

tage

s with

in th

e gr

oup

CI, c

onfid

ent

inte

rval

; ICU

, int

ensiv

e ca

re u

nit;

i.v.,

intr

aven

ous;

LoS,

leng

th o

f sta

y; N

A, n

ot a

vaila

ble;

PPS

As, p

rolo

nged

pro

phyl

axis

syst

emic

ant

imic

robi

als;

SWI,

ster

nal w

ound

infe

ctio

n; T

0,

No

syst

emic

ant

ibio

tics

+ pl

ain

cem

ent +

con

vent

iona

l ven

tilat

ion;

T1,

syst

emic

ant

ibio

tics

+ pl

ain

cem

ent +

con

vent

iona

l ven

tilat

ion;

T2,

non

-sys

tem

ic a

ntib

iotic

s +

plai

n ce

men

t + la

min

ar

airfl

ow; T

3, s

yste

mic

ant

ibio

tics

+ pl

ain

cem

ent +

lam

inar

airfl

ow; T

4, n

on-s

yste

mic

ant

ibio

tics

+ an

tibio

tic-im

preg

nate

d ce

men

t + c

onve

ntio

nal v

entil

atio

n; T

5, s

yste

mic

ant

ibio

tics

+ an

tibio

tic-

impr

egna

ted

cem

ent +

con

vent

iona

l ven

tilat

ion;

T6, s

yste

mic

ant

ibio

tic +

ant

ibio

tic-im

preg

nate

d ce

men

t + la

min

ar a

irflow

; T7,

syst

emic

ant

ibio

tics +

ant

ibio

tic-im

preg

nate

d ce

men

t + v

entil

atio

n +

body

exh

aust

suit;

T8,

sys

tem

ic a

ntib

iotic

s + a

ntib

iotic

-impr

egna

ted

cem

ent +

lam

inar

ven

tilat

ion

+ bo

dy e

xhau

st su

it

Prevention of surgical site infections

3

64

Timing of Antibiotic Prophylactic InterventionsThe starting time of antibiotics in prophylactic administrations was different ranging from an hour

before the surgical procedure to the time of skin incision. Five studies explicitly stated the time

of starting the prophylactic antibiotics.21,26,27,30,34 Chaudhuri et al.,26 and Wilson et al.,21 reported

the administration of the agents 30 minutes preoperatively for cefuroxime, metronidazole, and

cefotetan. Rifampicin in the study conducted by Dhadwal et al.,26 was administered orally an hour

before incision, followed by vancomycin post-induction of anesthesia. Additionally, intravenous

cefazolin sodium was injected before skin incision. Elongation of antibiotic prophylaxis was also

expounded in the studies, for instance, being explicitly analyzed by Alekwe et al.,25 Chauduri et

al.,26 Dhadwal et al.27

Reports of the Microorganisms Causing SSIFrom 7 included studies, this review generated a list of 24 bacteria that were reported as causing

SSIs at the site of surgery on the cranium, thorax, abdomen, and thoracolumbar spine.22,24,27,29,33,34,36

The predominant species that have been reported to be found for SSIs were gram-negative

bacteria. The most common pathogen reported among studies was Escherichia coli isolates,

accounting for 6.7-50% of incidence in general surgery, orthopedic, cardiothoracic surgery and

cesarean section.24,27,29,34 More importantly, Staphylococcus aureus was the second most prevalent

which was dominant among gram-positive bacteria causing SSIs.22,27,34,36 Anaerobic bacteria

were also reported, with an isolated case of Bacteroides fragilis as a rare bacteria, accounting for

approximately 13% of the SSI causes among cesarean section procedures.29 We compiled the

results of the pattern of bacterial causation of SSIs in Table 3.5.

Quality Assessments of the Included StudiesThe range of CHEC scores in the included studies was from a low of 8 to a high of 18.5.27,38 The

quality assessment scores of studies regarding general surgery ranged from 10 to 12.21,26,30,31,34

Among studies on orthopedic surgery and neurosurgery, the quality ranged between 12 and

18.5.18,19,23,24,33,35,38,39 Two cardiothoracic studies scored 8 and 11.5 points for CHEC items.27,28 Two

obstetric and gynecological studies were scored at 10.5 and 11.25,29 Furthermore, two oncologic

surgery studies obtained quality scores of 9.5 and 12.5.20,22,36 From the CHEC items, related to

incremental analysis concerns mostly and sensitivity analysis. The quality assessments of each

article are reported in Table 3.6.

Chapter 3

65

Table 3.5. Characteristics of pathogens, prophylactic antibiotic, mean cost and SSI incidence in each surgical procedure

Type of surgery, reference Pathogen (%) Prophylactic

antibioticMean cost,

US$*SSI incidence,

%General surgery(Chaudhuri et al., 2006; Matsui et al., 2014; Ozdemir et al., 2016; Singh et al., 2014; Wilson et al., 2008)- Colorectal surgery- Excision of pilonidal

sinuses- Laparoscopic

cholecystectomy

Escherichia coli (25)Klebsiella pneumonia (50)Staphylococcus aureus (25)

CefazolinCefotetan Ertapenem GentamicinMetronidazole Triclosan.

791.59 – 54,841.32

0.8-71.1

Orthopedic(Ceballos et al., 2017; Courville et al., 2012; Elliott et al., 2010; Graves et al., 2016; Merollini et al., 2013; Theologis et al., 2014)- Deformity

reconstruction- Hip arthroplasty- Hip replacement- Knee arthroplasty- Lower limb

amputation

Citrobacter freundii (6.7) Corynebacterium afermentan (6.7) Corynebacterium jeikeium (6.7)Enterobacter cloacae (6.7)Escherichia coli (6.7)MRSA (39.9)Proteus mirabilis (13.3)Pseudomonas mirabilis (13.3)Staphylococcus epidermidis (6.7)

CefazolinCefotaxime CefoxitinCefuroxime CephalotinMupirocinVancomycin

1,245.83 – 120,989.52

0.5-38.3

Neurosurgery(Emohare et al., 2014; Lewis et al., 2016)- Cranial surgery- Posterior spinal

surgery- Subdural and

subgaleal drains

Enterobacteriacea (33.3)Klebsiella pneumonia (33.3)Propionibacterium acnes (33.3)

CefazolinVancomycin

887.79 – 2,879.02 0-3.4

Cardiothoracic surgery(Dhadwal et al., 2007; Joshi et al., 2016)- Cardiac surgery- Coronary artery bypass

Staphylococcus aureus (8.7)Bacteroides fragilis (4.3)Enterobacter cloacae (2.9)Enterobacteriaceae (30.4)Enterococcus faecalis (14.5)Escherichia coli (24.6)Klebsiella pneumonia (3.0)Pseudomonas aeruginosa (7.2)Proteus mirabilis (3.0)Serratia marcescens (1.4)

GentamicinRifampicinVancomycin

7,835.59 – 22,130.53 1.4-25.3

Obstetric gynecological surgery(Alekwe et al., 2008; Kosus et al., 2010)- Cesarean section Bacteroides fragilis (12.5)

Escherichia coli (50)Enterococci (25)Streptococci spp.s Group B (12.5)

AmpicillinCeftriaxoneGentamicinMetronidazoleRifamycin

482.59 0-8

Oncologic surgery(El-Mahallawy et al., 2013; Gulluoglu et al., 2013; Patil et al., 2011)- Bladder cancer surgery- Breast cancer surgery- Head and neck onco-

surgeries- Rectal cancer surgery- Stomach cancer

surgery

Acinectobacter haemolyticus (2.7)Staphylococcus aureus (32.4)Streptococci (16.2)Staphylococcus epidermidis (35.1)Various gram negatives (13.6)

AmikacinAmpicillinCefazolinCefprozilCiprofloxacinClindamycinGentamicinMetronidazoleMoxifloxacinPenicillin G

Not adequately informed

4.8-47.8

*Adjusted mean cost in US$ at 2015-inflation rateMRSA, Methicillin-resistance Staphylococcus aureus

Prevention of surgical site infections

3

66

DISCUSSION

Guidance for the reporting of economic and clinical studies in the specific field of infectious

disease and antibiotic use is urgently needed. Choosing the use of prophylactic antibiotics

especially for SSIs should take into account the local epidemiological data of the pathogens

and antimicrobial susceptibility. The microbial etiology of SSIs and antibiotic resistance are often

missing from reports of the mid-and long-term economic impact of antibiotic use. For the

economic part, the minimum requirements of the established CHEC checklist can assist in the

reporting of economic studies. Also, a different checklist from the Consolidated Health Economic

Evaluation Reporting Standards (CHEERS) statement has been performed to assess the quality

of the economic study.17,40 However, the CHEERS checklist only considers the completeness of

reporting and does not evaluate the quality. Another checklist which was developed by Caro et

al.,41 was merely to conceptualize model-based studies. Hence, the CHEC checklist seems most

applicable and appropriate for quality assessment in this review.

The diversity in the definition of SSIs in terms of the period to identify the SSIs potentially generates

under-reporting of the diseases’ occurrence. Despite the definition from the CDC 13, other definitions

were addressed by Peel and Taylor,42 from the Surgical Infections Society Study Group (SIGS) and

Ayliffe et al.,43 from the National Prevalence Survey (NPS) which considered grouping wound infection

based on the cause of infection, the time of appearance, and the severity of infection. For the time

of the appearance of infection, they divided this into three categories, namely early, intermediate,

and late based on whether the infection appeared in a 30-day period, in a period of between 1 and

3 months, and over 3 months post-surgery, respectively. By these definitions, the cost is accurately

predicted especially for the potentially extensive financial burden of late-occurrence SSIs. Twelve

included studies (60%) defined the time for the appearance of SSIs within diverse follow-up intervals

for trial-based studies and time horizons for model-based studies.

Obviously, the clinical outcome depends not only on prophylactic antibiotics which is prior to

surgical procedures but also whether minimal intervention is concerned comprising limited tissue

damage, which has the effect of accelerating wound healing.44 Other influential issues that were

identified for the costs of the management of surgical patients include surgical techniques, skilled

surgeons being available, types of diseases, and the for-profit or not-profit nature of healthcare

system services involved.45,46 The desired economic impacts of the proper use of prophylactic

antibiotics in SSIs prevention are shorter lengths of stay, lower resistance rates, and ultimately, the

reduction of costs. Some evidence showed a positive relationship between the infection rate and

length of stay, and the reason given was that inpatients are at a high risk of nosocomial infection

with often antibiotic- and multi-resistant microorganisms.47–51 Costs for a day of hospital stay and re-

hospitalizations, especially in the short-term, are virtually however fully fixed.52 An illustrative example

concerns a prospective study with a hospital perspective that included direct medical costs by

calculation based on the length of hospital stay in nosocomial infections after head and neck cancer

surgery.53 Of the included studies, 9 (45%) comprised length of hospitalization in their evaluation.

Moreover, costs due to antimicrobial resistance, included as indicating the secondary costs for

Chapter 3

67

Tabl

e 3.

6. Q

ualit

y as

sess

men

t of e

ach

indi

vidu

al st

udy

acco

rdin

g to

Con

sens

us o

n H

ealth

Eco

nom

ic C

riter

ia (C

HEC

)

No.

Item

s

Chaudhuri et al

Wilson et al

Matsui et al

Singh et al

Ozdemir et al

Elliott et al

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Prevention of surgical site infections

3

68

advanced medications to overcome the resistance rates, can be expected to eventually become

variable costs.52 The timing administration of prophylactic antibiotics is essential to evaluate the

clinical effectiveness, resistance, and costs. A previous RCT in London stated that the administration

of prophylactic antibiotics within two hours prior to incision had the lowest risk of SSIs.54 Regarding

the frequency of the drug administration, one included study showed that prolonged prophylactic

use after 24 hours post-surgery did not show any benefit in cost and SSI prevention.33

With regards to new antibiotics, the pricing process has a significant influence on the

calculation of the economic outcomes, and thus bias potentially may come from trial-based

economic studies that are sponsored by the pharmaceutical industry. The industry can affect

the way in which results are reported.55,56 Disclosure of either funding contributions or conflicts

of interest in all the works and the findings of each study is a recommended strategy to identify

potential bias.57 Half of the included studies explicitly included a statement of conflict of interest.

It is essential to adjust the costs for antibiotics especially for patented drugs that could decrease

significantly in price when the patent period expires. Only 7(35%) included studies reported the

costs of the antibiotics including the price of a single dose.

In economic evaluation, the outcome parameters are holistic including costs, clinical

effectiveness, and utility. Hence, a narrow or restricted perspective fosters the omission of some

essential costs and outcomes. Half of the included studies did not explicitly state the perspective,

hence here may be cost measurement omission bias.58 For both trial and model-based studies, the

societal perspective has a broader view and its use is recommended in economic evaluation.59 The

included study by Singh et al.,60 showed that the societal perspective had a 10 times higher cost

compared with healthcare and payer perspectives since not only direct costs but also productivity

loss is considered comprehensively in a societal perspective.14 Of the included studies only 3(15%)

took into account the societal perspective with DALY and QALY as utility units. Most of the included

studies (75%) performed CMA as the method to analyze the costs for SSIs. Obviously, CMA was

used and implemented to address the costs due to the presence of SSIs such as in two studies

in cesarean section and orthopedics which reported the median cost for SSIs at US$4,091 and

US$108,782, respectively.61,62 High burden of post-surgical procedures with SSIs were also present

for nosocomial pneumonia. The additional direct medical cost was considered to increase from

EUR19,000 for SSIs to EUR35,000 for post-surgical complications.53 Furthermore, in clinical outcome

measurements, there is some evidence that systemic prophylactic antibiotics have a significant

impact on minimizing the incidence of SSIs and medical costs in high-risk patients, especially in

major surgical procedures including oncologic surgery63, cardiothoracic64, cesarean section65, and

orthopedic surgery66. To achieve high efficacy, a current strategy is a prophylactic combination

added locally to the standard prophylaxis, especially in deep surgical sites, for instance, using

intra-wound vancomycin67 or gentamicin68. A meta-analysis showed that implantable gentamicin-

collagen reduced either superficial or deep wound infection effectively, even though the mortality

rate was not significantly different.69 The use of a local or intra-wound antibiotic as an add-on

treatment can be predicted as more effective since the site-target concentration of antibiotics

with local treatment is higher than that without local antibiotics. In contrast, Eklund et al.,70

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stated that there was no statistically significant difference in SSI rates between an add-on local

gentamicin group and the group without local prophylaxis.

The scope of cost analysis is critical when evaluating the relevant costs and the patient’s

expectations on clinical outcomes and safety. To achieve successful treatments especially in the

use of antibiotics, antimicrobial susceptibility and the pattern of pathogens causing SSIs should

be taken into account. Under-reported unsusceptible antibiotics in the group of high SSI rates can

potentially produce bias especially in the interpretation of the treatment outcomes. Therefore,

failure in clinical improvement from surgical wounds should consider the local epidemiology

susceptibility of antibiotics. None of the included studies reported on antimicrobial susceptibility.

Notably, regarding SSIs the importance of correct and early diagnosis cannot be stressed enough.

Here, microbiological diagnostics are paramount in decisions for specific antibiotic treatment.

Treating infection in the most effective method with the correct antibiotics is important with

respect to the treatment, but also with respect to the development of antibiotic resistance.71 An

integrated stewardship program, such as the AID stewardship (Antibiotic, Infection Prevention, and

Diagnostic Stewardship) is crucial since it targets all different aspects of infection management.

This theragnostic approach involves a combination of diagnostics and therapeutics considering

the interdisciplinary staff in the complexity of infection management. The role of diagnostic

stewardship is especially gaining momentum right now to achieve a personalized approach

in infection management.72–74 Therefore, this review comprehensively takes into account all

stewardship aspects on each surgical procedure in terms of the effectiveness of prophylactic

therapeutics, diagnostics to determine SSIs’ pathogens, patient safety, antibiotic resistance, the

timing of prophylaxis and further impact on costs.

We are aware that this review has limitations. Notably, the study may be less representative

of other important procedures such as urological, ophthalmological, organ transplantations,

implantable devices, and dental surgery. Nearly 15% of 441 patients undergoing kidney

transplantation in a hospital in the US developed SSIs. In the 2013 annual report, almost 18,000

patients have carried out kidney transplantation procedures 75. It indicated a high number of

potential SSIs coming from the procedures and obviously, a need exists to perform an analysis

on the cost and effectiveness of the use of prophylactic antibiotics. Of 208 eligible studies, only 3

studies referred to SSIs in urology; nevertheless, these studies did not meet the further inclusion

criteria. Using different definitions to determine the infections potentially leads to underreporting

of SSIs and the location or types of SSIs, even in community health services. Of the included

studies, only one reported incidence of the SSIs based on the types.19 The reporting of updated

data related to microbiological results is fruitful, even though it may be more difficult to determine

the definite cause of SSI at particular sites of the incision from the results. None of the included

studies considered procedures in children or pediatric surgery which has a higher risk of SSIs and

different pathogen patterns. Moreover, because of major differences in the incidence of antibiotic

resistance between the US and Europe, outcome studies need to be interpreted with caution.

Finally, this review used the CHEC as a rigorous method to assess the quality of the articles and can

be used as a baseline for guidelines for further economic evaluations.16

Prevention of surgical site infections

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CONCLUSIONS

Overall, we describe novel findings from reviewing the economic evaluations of studies concerning

prophylactic antibiotic uses for SSI prevention in general surgery, orthopedic surgery, neurosurgery,

cardiothoracic surgery, obstetric and gynecological surgery, and oncologic surgery. Preoperative

prophylactic antibiotics administered either locally or systemically are considered in some studies

and for specific interventions at preventing SSIs. The quality in reporting of economic evaluation

indicates that the included studies need to be improved, especially with respect to issues related

to antimicrobial susceptibility, pathogens causing SSIs, cost perspectives, incremental analysis

and sensitivity analysis of the costs. Notably, even though surgical prophylactic antibiotics are

not the only factor inducing antimicrobial resistance, the local pattern of antimicrobial resistance

and antimicrobial susceptibility are fruitful information used to select the proper prophylactic

antibiotics. The valuable information in terms of cost, updated causes of SSIs and from this review

can be considered in the clinical implementation in the proper use of prophylactic antibiotics to

reduce costs and to prevent SSIs and further antimicrobial resistance.

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72. Dik, J. H. et al. Integrated Stewardship Model Comprising Antimicrobial, Infection Prevention, and Diagnostic Stewardship (AID Stewardship). Journal of clinical microbiology 55, 3306–3307 (2017).

73. Dik, J.-W. H., Friedrich, A. W., Nathwani, D. & Sinha, B. Combating the Complex Global Challenge of Antimicrobial Resistance: What can Antimicrobial Stewardship Contribute? Infectious disease reports 9, 7158 (2017).

74. Messacar, K., Parker, S. K., Todd, J. K. & Dominguez, S. R. Implementation of Rapid Molecular Infectious Disease Diagnostics: the Role of Diagnostic and Antimicrobial Stewardship. J. Clin. Microbiol. 55, 715–723 (2017).

75. National Institutes of Health 2015. 2013 Annual Data Report. National Institute of Diabetes and Digestive and Kidney Institute. Available at: https://www.niddk.nih.gov/health-information/health-statistics/kidney-disease. (Accessed: 30th May 2018)

76. Itani, K. M. F. et al. Ertapenem versus cefotetan prophylaxis in elective colorectal surgery. N. Engl. J. Med. 355, 2640–2651 (2006).

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Supplement 3.1. Search strategy using PubMed database

PICO Strategy strings The number of articles

P “Surgical Procedures, Operative”[Mesh]) OR surger*[tiab] OR surgical[tiab] OR operation*[tiab] OR operative*[tiab] 3,783,815

I“Anti-Infective Agents”[Mesh] OR “Anti-Infective Agents” [Pharmacological Action] OR “Antibiotic Prophylaxis”[Mesh] OR antimicrob*[tiab] OR antibiotic*[tiab]

1,675,192

O1 “Surgical Wound Infection”[Mesh]OR surgical site infection*[tiab] OR surgical wound infection*[tiab] OR SSI*[tiab] 39,143

O2 “Costs and Cost Analysis”[Mesh] OR cost*[tiab] OR econom*[tiab] OR financ*[tiab] OR Pharmacoeconomic*[tiab] 810,532

P+I+C+O1+O2

(“Surgical Procedures, Operative”[Mesh]) OR surger*[tiab] OR surgical[tiab] OR operation*[tiab] OR operative*[tiab]) AND (“Anti-Infective Agents”[Mesh] OR “Anti-Infective Agents” [Pharmacological Action] OR “Antibiotic Prophylaxis”[Mesh] OR antimicrob*[tiab] OR antibiotic*[tiab]) AND (“Surgical Wound Infection”[Mesh]OR surgical site infection*[tiab] OR surgical wound infection*[tiab] OR SSI*[tiab]) AND (“Costs and Cost Analysis”[Mesh] OR cost*[tiab] OR econom*[tiab] OR financ*[tiab] OR Pharmacoeconomic*[tiab])

1,079

Filter From 1 January 2006 to 31 August 2017 644

Supplement 3.2. Search strategy using EMBASE database

PICO Strategy strings The number of articles

P ‘surgical patient’/exp OR ‘surgical patient’ OR ‘surgery’/exp OR ‘surgery’ OR surger*:ab,ti OR surgical:ab,ti OR operation*:ab,ti OR operative*:ab,ti 6,024,616

I ‘antibiotic prophylaxis’/exp OR ‘antiinfective agent’/exp OR ‘antibiotic agent’/exp OR antimicrob*:ab,ti OR antibiotic*:ab,ti 2,877,127

O1 ‘surgical infection’/exp OR ‘surgical site infection*’:ab,ti OR ‘surgical wound infection*’:ab,ti OR ssi*:ab,ti 44,898

O2‘cost effectiveness analysis’/exp OR ‘cost’/exp OR ‘pharmacoeconomics’/exp OR ‘economic evaluation’/exp OR cost*:ab,ti OR econom*:ab,ti OR financ*:ab,ti OR pharmacoeconomic*:ab,ti

1,228,677

P+I+C+O1+O2

(‘surgical patient’/exp OR ‘surgery’/exp OR (surger* OR surgical OR operation* OR operative*):ab,ti) AND (‘antibiotic prophylaxis’/exp OR ‘antiinfective agent’/exp OR ‘antibiotic agent’/exp OR (antimicrob* OR antibiotic*):ab,ti) AND (‘surgical infection’/exp OR ‘surgical site infection*’:ab,ti OR ‘surgical wound infection*’:ab,ti OR ssi*:ab,ti) AND (‘cost effectiveness analysis’/exp OR ‘cost’/exp OR ‘pharmacoeconomics’/exp OR ‘economic evaluation’/exp OR cost*:ab,ti OR econom*:ab,ti OR financ*:ab,ti OR pharmacoeconomic*:ab,ti)

1,750

Filter From 1 January 2006 to 31 August 2017 1,417

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Supplement 3.3. PRISMA checklist

Section/topic # Checklist item Reported on page #

TITLE

Title 1 Prevention of Surgical Site Infections: A Systematic Review of Cost Analysis in the Use of Prophylactic Antibiotics

Title

ABSTRACT

Structured summary

2 Introduction: The preoperative phase is an important period in which to prevent surgical site infections (SSIs). Prophylactic antibiotic use helps to reduce SSI rates, leading to reductions in hospitalization time and cost. In clinical practice, besides effectiveness and safety, the selection of prophylactic antibiotic agents should also consider the evidence with regard to costs and microbiological results. This review assessed the current research related to the use of antibiotics for SSI prophylaxis from an economic perspective and epidemiology of microbiological findings. Methods: A literature search was carried out through PubMed and Embase databases from 1 January 2006 to 31 August 2017. The relevant studies which reported the use of prophylactic antibiotics, SSI rates and costs were included for analysis. The causing pathogens for SSIs were categorized by sites of the surgery. The quality of reporting on each included study was assessed with the “Consensus on Health Economic Criteria” (CHEC).Results: We identified 208 eligible full-text studies reporting costs related to prophylactic antibiotics or SSIs. Three quarters (n=157) used cost-minimization analyses as the method of economic evaluation. Twenty of these 208 studies were included and were assessed subsequently, with the reporting quality scored on the CHEC list averaging 13.03 (8-18.5). Of these, 14 were trial-based studies, and the others were model-based studies. The SSI rates ranged from 0 to 71.1% with costs amounting to US$480-22,130. Twenty-four bacteria were identified as causative agents of SSIs. Gram-negatives were the dominant causes of SSIs especially in general surgery, neurosurgery, cardiothoracic surgery, and obstetric cesarean sections.Conclusions: The fruitful information from some updated trial-based and model-based studies can be considered in the clinical implementation of the proper use of prophylactic antibiotics to prevent SSIs and antimicrobial resistance especially in terms of the cost and patterns of microbial causes of SSIs. Nevertheless, the findings of economics and microbiology from the included studies have reported diverse results.Keywords: prophylaxis, surgical site infections, cost, bacteria”

Abstract

INTRODUCTION

Rationale 3 The preoperative phase is considered the most crucial period of a surgical procedure in which the goal is to reduce the bacterial load surrounding the incision area. Using antibiotics prior to surgical incision is considered to be effective in preventing SSIs, which are among the most common preventable post-surgery complications among healthcare-associated infections (HAIs) (Mangram et al., 1999; Umscheid et al., 2011). A parenteral prophylaxis agent spectrum with corresponding potential bacteria on particular sites of surgery has been recommended recently to reduce SSI rates efficiently (Berrios-Torres et al., 2017). In contrast, some preoperative procedures, such as hair removal and mechanical bowel preparation are considered today to be inefficient at reducing SSIs (Anderson et al., 2014; Leaper et al., 2008). In the US, SSIs were identified in approximately 1.9% of 849,659 surgical procedures in 43 states from 2006 to 2008 (Mu et al., 2011). The economic burden of SSIs should be taken into account in the use of prophylactic antibiotics.

Introduction

Objectives 4 The aim of this study is to present recent evidence and analyze the methodologies used in economic evaluations. In addition, the study comprehensively analyzes the quality of the included studies and local epidemiology of pathogen-causing SSIs.

Introduction

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METHODS

Protocol and registration

5 This review was registered in PROSPERO with number CRD42017076589 Materials and Methods

Eligibility criteria 6 We developed criteria to identify the eligible studies which contained economic analysis and followed the defined PICO-approach (Patient or Problem, Intervention, Control, and Outcomes). Concerning the patient (P), all types of surgical procedures were included. There was no restriction on age or gender. For both the intervention (I) and comparison (C), this review included studies concerning the utilization of antibiotic prophylaxis administered intravenously, orally, or locally to prevent SSI. Other terms of post-surgical infections such as wound infections and sternal wound infections (SWIs) were included. We excluded studies mainly evaluating comparisons of the use of antiseptic, pharmaceutical care interventions or guideline adherence issues. For the outcomes (O), we included studies evaluating both SSI rates and cost.

Materials and Methods (study selection)

Information sources

7 “We searched the updated relevant evidence from PubMed and EMBASE databases from 1 January 2006 to 31 August 2017. “

Materials and Methods (search strategy)

Search 8 “The search strategy and the number of articles from PubMed and EMBASE are presented in Supplement 3.1 and Supplement 3.2, respectively.”

Supplement 3.1 and Supplement 3.2

Study selection 9 The search used search terms or phrases represented in Medical Subject Headings (MeSH) with the operator ‘tiab’ for PubMed. Subsequently, the terms or phrases used in PubMed were translated to the EMBASE database by using strings and the symbols ‘ab,ti’. To refine the result, we employed a search strategy using the Boolean operator ‘OR’ within sequences of terms with close or similar meanings and ‘AND’ for one or more sequences of terms that contained completely different meanings. Whole terms and phrases for either PubMed or Embase were identified by two persons (AKRP and KS) who dealt with the search strategy.

Materials and Methods (study selection)

Data collection process

10 Two authors (AKRP and DS) independently assessed all included papers. Any disagreements between those authors were discussed with a third author (JWD) until the discrepancies were resolved by consensus.

Materials and Methods (data extraction)

Data items 11 Fields of the extracted data included the authorship, year of publication, journal, country, type of surgery, wound categorization, gender, age, sample size, outcomes, prophylactic antibiotics, SSI rates, the timing for the prophylactic strategy, follow-up and length of stay. To address the outcome from a microbiology perspective, we extracted the pathogens based on the sites of the surgery, antimicrobial susceptibility, and resistance rates. Furthermore, we grouped the types of SSIs based on the definitions and classifications of SSIs from the Centers for Disease Control and Prevention (CDC) (Horan et al., 1992).

Materials and Methods (data extraction)

Risk of bias in individual studies

12 We used the Consensus on Health Economic Criteria (CHEC) list to assess the quality of reporting of the health economic outcomes, including potential bias in individual studies (Evers et al., 2015).

Materials and Methods (quality assessments)

Summary measures

13 For the cost types, we took into account cost perspectives with components of (1) direct costs such as costs for prophylactic antibiotics, hospitalization, side-effects and antimicrobial resistance, and (2) indirect costs including costs of loss of productivity. We made costs comparable among individual studies using currency conversions to US$ and corrections for inflation rates. We calculated inflation rates based on the 2015 annual GDP growth index in the World DataBank for each respective country (The World Bank, 2015). If the individual article did not state the actual year for the cost analyses, we made the assumption that the year of the cost estimate was the same as the last year of data collection.

Materials and Methods (cost analysis and data synthesis)

Synthesis of results

14 - -

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Risk of bias across studies

15 - -

Additional analyses

16 To address the outcome from a microbiology perspective, we extracted the pathogens based on the sites of the surgery, antimicrobial susceptibility, and their resistance rates. Furthermore, we grouped the types of SSIs based on the definitions and classifications of SSIs from the Centers for Disease Control and Prevention (CDC) (Horan et al., 1992).

Materials and Methods (data extraction)

RESULTS

Study selection 17 This review initially identified a total of 644 and 1,417 articles from PubMed and Embase respectively. A comprehensive listing of the searches in both PubMed and Embase can be found in Supplement 3.1 and Supplement 3.2. After removing duplications, we screened 1,529 titles and abstracts. Subsequently, we excluded 1,321 articles for the reasons listed in the Materials and Methods section. Eventually, we assessed 208 eligible full-text studies of which we excluded 118 because of being reviews, having incomplete data related to costs and lack of presenting on the outcomes of prophylactic antibiotic uses and SSI incidence. A total of 20 articles remained according to the inclusion criteria and were extracted systematically for further analyses (Alekwe et al., 2008; Chaudhuri et al., 2006; Courville et al., 2012; Dhadwal et al., 2007; Emohare et al., 2014; Gulluoglu et al., 2013; Joshi et al., 2016; Kosus et al., 2010; Matsui et al., 2014; Merollini et al., 2013; Patil et al., 2011; Theologis et al., 2014; Wilson et al., 2008). A flow chart of the search is shown in Figure 3.1.

Results and Figure 3.1.

Study characteristics

18 “General characteristics of 208 eligible and 20 included articles are presented in Table 3.1. Baseline overviews of country, types of surgery, gender, age, number of subjects, types of prophylactic antibiotics, outcomes, design of the included studies are presented in Table 3.2.”

Table 3.1 and Table 3.2

Risk of bias within studies

19 The range of CHEC scores in the included studies was from a low of 8 to a high of 18.5 (Dhadwal et al., 2007; Graves et al., 2016). The quality assessment scores of studies regarding general surgery ranged from 10 to 12 (Chaudhuri et al., 2006; Matsui et al., 2014; Ozdemir et al., 2016; Singh et al., 2014; Wilson et al., 2008). Among studies on orthopedic surgery and neurosurgery, the quality ranged between 12 and 18.5 (Ceballos et al., 2017; Courville et al., 2012; Elliott et al., 2010; Emohare et al., 2014; Graves et al., 2016; Lewis et al., 2016; Merollini et al., 2013; Theologis et al., 2014). Two cardiothoracic studies scored 8 and 11.5 points for CHEC items (Dhadwal et al., 2007; Joshi et al., 2016). Two obstetric and gynecological studies were scored at 10.5 and 11 (Alekwe et al., 2008; Kosus et al., 2010). Furthermore, two oncologic surgery studies obtained quality scores of 9.5 and 12.5 (El-Mahallawy et al., 2013; Gulluoglu et al., 2013; Patil et al., 2011). From the CHEC items, issues related to incremental analysis and sensitivity analysis were absent from most studies. The quality assessments of each article are reported in Table 3.6.

Results (quality assessments of included studies) and Table 3.6

Results of individual studies

20 “Comparisons of included studies on reporting of cost index, methods of cost analysis, cost perspective, and adjusted cost in US$ at the 2015-inflation rate are presented in Table 3.3.“

Table 3.3

Synthesis of results

21 -

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Risk of bias across studies

22 With regards to new antibiotics, the pricing process has a significant influence on the calculation of the economic outcomes, and thus bias potentially comes particularly from trial-based economic studies that are sponsored by the pharmaceutical industry. The industry can affect the way in which results are reported (Bell et al., 2006; John-Baptiste and Bell, 2010). It is essential to adjust the costs for antibiotics especially for patented drugs that could decrease significantly in price when the patent period expires. Only 7(35%) included studies reported the costs of the antibiotics including the price of a single dose. Disclosure of either funding contributions or conflicts of interest in all the works and the findings of each study is a recommended strategy to identify bias (Palumbo et al., 2004). Half of the included studies explicitly included the statements of conflict of interest. In the economic evaluation, the outcome parameters are holistic including costs, clinical effectiveness, and utility. Hence, a narrow or restricted perspective fosters the omission of some essential costs and outcomes. Half of the included studies did not explicitly state the perspective, hence here may be cost measurement omission bias (Evers et al., 2005a).

Discussion

Additional analysis

23 From 7 included studies, this review generated a list of 24 bacteria that were reported as causing SSIs at the site of surgery on the cranium, thorax, abdomen, and thoracolumbar spine (Dhadwal et al., 2007; El-Mahallawy et al., 2013; Gulluoglu et al., 2013; Kosus et al., 2010; Lewis et al., 2016; Ozdemir et al., 2016; Theologis et al., 2014). The predominant species that have been reported to be found for SSIs were gram-negative bacteria. The common pathogen reported among studies was Escherichia coli isolates, accounting for 6.7-50% of incidence in general surgery, orthopedic, cardiothoracic surgery and cesarean section (Dhadwal et al., 2007; Kosus et al., 2010; Ozdemir et al., 2016; Theologis et al., 2014). More importantly, Staphylococcus aureus was the second most prevalent which was dominant among gram-positive bacteria causing SSIs (Dhadwal et al., 2007; El-Mahallawy et al., 2013; Gulluoglu et al., 2013; Ozdemir et al., 2016). Anaerobic bacteria were also reported, with an isolated case of Bacillus fragilis as a rare bacteria, accounting for approximately 13% of the SSI causes among cesarean section procedures (Kosus et al., 2010). We compiled the results of the pattern of bacterial causation of SSIs in Table 3.4.

Results (reports of the microbes causing SSI)

DISCUSSION

Summary of evidence

24 Obviously, CMA was simply used and implemented to address the costs due to the presence of SSIs such as in two studies in cesarean section and orthopedics which reported the median cost for SSIs at US$4,091 and US$108,782, respectively (Olsen et al., 2008; Thakore et al., 2015). The values were in line with the findings from the included studies which amounted to between US$482 and US$120,989. The high burden of post-surgical procedures when SSIs are concomitantly present with nosocomial pneumonia is also a complication post-surgery. The additional direct medical cost was considered to increase from EUR19,000 for SSIs to EUR35,000 for both post-surgical complications (Penel et al., 2008). Furthermore, in clinical outcome measurements, there is some evidence that systemic prophylactic antibiotics have a significant impact on minimizing the incidence of SSIs and medical costs in high-risk patients, especially in major surgical procedures including oncologic surgery (Jones et al., 2014), cardiothoracic (Lador et al., 2012), cesarean section (Smaill and Grivell, 2014) and orthopedic surgery (Brown et al., 2004). To achieve high efficacy, a current strategy is a prophylactic combination added locally to the standard prophylaxis, especially in deep surgical sites, for instance, using intra-wound vancomycin (Xiong et al., 2014)or gentamicin (Friberg et al., 2005). A meta-analysis showed that implantable gentamicin-collagen reduced either superficial or deep wound infection effectively, even though the mortality rate was not significantly different (Kowalewski et al., 2015). The use of a local or intra-wound antibiotic as an add-on treatment can be predicted as more effective since the site-target concentration of antibiotics with local treatment is higher than that without local antibiotics. In contrast, Eklund et al., stated that there was no statistically significant difference in SSI rates between an add-on local gentamicin group and the group without local prophylaxis (Eklund et al., 2005).

Discussion

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Limitations 25 We are aware that this review may have limitations. For generalizability, the study is less representative for other important procedures such as urological, ophthalmological, or dental surgery. Using different definitions to determine SSI leads to underreporting of SSIs, even in community health services. The reporting of updated data related to microbiological results is fruitful, even though it may be more difficult to determine the definite cause of SSI at particular sites of the incision from the results. Because of major differences in the incidence of antibiotic resistance between the US and Europe, outcome studies need to be interpreted with caution. Finally, this review used the CHEC as a rigorous method to assess the quality of the articles and can be used as a baseline for guidelines for further economic evaluations (Evers et al., 2015).

Discussion

Conclusions 26 Overall, we describe novel findings from reviewing the economic evaluations of studies concerning prophylactic antibiotic uses for SSI prevention in general surgery, orthopedic surgery, neurosurgery, cardiothoracic surgery, obstetric and gynecological surgery, and oncologic surgery. Preoperative prophylactic antibiotics administered either locally or systemically are considered in some studies and for specific interventions at preventing SSIs. The quality in reporting of economic evaluation indicates that the included studies need to be improved for further research, especially with respect to issues related to antimicrobial susceptibility, pathogens causing SSIs, cost perspectives, incremental analysis and sensitivity analysis of the costs. Notably, the valuable information in terms of cost, updated causes of SSIs and local antimicrobial susceptibility from this review can be considered in the clinical implementation in the proper use of prophylactic antibiotics to reduce costs and to prevent SSIs and further antimicrobial resistance.

Conclusions

FUNDING

Funding 27 The work was supported by a Directorate General of Higher Education (DIKTI) scholarship, Ministry of Research, Technology and Higher Education of the Republic of Indonesia [No.224/D3.2/PG/2016]; and the Faculty of Medicine, Universitas Airlangga [No. 305/UN3.5/SDM/2016]; and Groningen University Institute for Drug Exploration, University Medical Center Groningen, The Netherlands.

Funding

From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097

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CHAPTER 4The impacts of deep surgical site

infections on readmissions, length of stay, and costs: A matched case-control

study in an academic hospital in the Netherlands

Abdul Khairul Rizki Purba

Christian F. Luz

Riyanti Retno Wulandari

B.T.F. van der Gun

Jan-Willem Dik

Alex W. Friedrich

Maarten J. Postma

Submitted

82

ABSTRACT

Background: Deep surgical site infections (dSSIs) may cause potential readmissions, prolonged

lengths of stay (LoS), and costs related to deteriorating health.

Objectives: We aimed to evaluate the impacts of dSSIs regarding hospital readmissions, prolonged

LoS, and estimated costs.

Methods: We designed and applied a matched case-control observational study using the

electronic health records at the University Medical Center Groningen in the Netherlands. We

compared patients with dSSI and non-SSI, matched on the basis of having similar procedures.

A prevailing topology of surgeries categorized as clean, clean-contaminated, contaminated, and

dirty was applied.

Results: Out of a total of 12,285 patients, 393 dSSI were identified as cases, and 2,864 patients

without SSIs were selected as controls. A total of 343 dSSI patients (87%) and 2,307 (81%) controls

required hospital readmissions. The median LoS was 7 days (P25-P

75: 2.5-14.5) for dSSI patients and

5 days (P25

-P75

: 1-9) for controls (p-value: <0.001). The estimated mean cost per hospital admission

was €9,016 (SE+343) for dSSI patients and €5,409 (SE+120) for controls (p<0.001). Independent

variables associated with dSSI were patient’s age >65 years (OR: 1.334; 95%CI: 1.036-1.720), the

use of prophylactic antibiotics (OR: 0.424; 95%CI: 0.344-0.537), and neoplasms (OR: 2.050; 95%CI:

1.473-2.854).

Conclusions: dSSI is associated with increased costs and prolonged LoS and slightly increased

readmission rates. Elevated risks were seen for elderly patients and those with neoplasms.

Additionally, a protective effect of prophylactic antibiotics was found.

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INTRODUCTION

Surgical site infections (SSIs) present a high risk of prolonged lengths of stay (LoS) and

readmissions.1–5 In the United States, SSIs reportedly occurred in 1.9% of surgical procedures.6

These infections incur costs, ranging between US$480 and US$22,130 per patient.7 In addition, the

European Centre for Disease Prevention and Control (ECDC) in 2011-2012 reported that SSI was the

most common healthcare-associated infection with an incidence of 156.5 per 100,000 population.8

Most concern exists about specific sites of infections, such as deep SSI (dSSI) involving infection

of soft tissues of fascia and muscles with a high risk of development of sepsis as a complication.1,9

In addition, a correlation has been found between the type of surgery, categorized according to

the prevailing typology of clean, clean-contaminated, contaminated and dirty surgeries, and the

risk of contracting an SSI.10–12 Operation procedures with clean-contaminated and dirty have been

reported being associated with a high burden of dSSI.13–15

Among European countries, the Netherlands has implemented an integrative stewardship

program that has been effectively performed in many Dutch hospitals to manage hospital

infections including dSSI. The program is applied for antimicrobial use, infection prevention, and

diagnostic stewardship, involving a multidisciplinary team with infectious diseases, infection

control practitioners as well as clinical microbiologists and hospital pharmacists.16,17 From the ECDC

2016 and 2017 reports, SSI incidence in the Netherlands is shown to have decreased from 2.2 to 1.0

per 100 surgeries, respectively.8 In general, the burden of healthcare-associated infections on the

European population has been estimated at 501 disability-adjusted life years (DALYs) per 100,000

general population annually.8 In the Netherlands, dSSI was responsible for 3,200 DALYs per year

for colectomies and 1,200 DALYs per year for total hip arthroplasties, with the national financial

burdens for each of these conditions estimated at €29 million and €10 million, respectively.18

A dSSI surveillance performed for the Netherlands (‘PREZIES’; 1996-2004), documented dSSI

rates at 3.7% in 93,511 surgical procedures, with the highest dSSI rate in the procedures of colon

resections (6.7%).19,20 An evidence found that in contrast to superficial SSIs, dSSIs contribute

an additional mean LoS of 2-2.6 days.21 dSSIs are considered a high burden in the group of

hospitalization infection. 21,22 To deal with the burden of dSSI, various modalities have been

implemented, ranging from the use of prophylactic antibiotic before surgical incision to the

subsequent antibiotic treatment of dSSI, including empiric treatment that being installed based

on national/local guidelines.23,24 In the practical setting of the implementation of the strategies,

it is important to assess the impacts of further dSSI complications, such as readmissions, the

emergence of resistant bacteria, and additional costs. Here, we evaluated the impacts of dSSIs in

terms of readmission rates, hospitalization costs, and LoS in a matched case-control study in an

academic hospital in The Netherlands. In addition, we investigated predictors for the development

of dSSI.

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METHODS

Patients dataWe performed a matched case-control observational study using the electronic health records

at the University Medical Center Groningen (UMCG), the largest hospital in the Northern

Netherlands, comprising of 1339 beds. The ethical review for this study was waived due to the

fact that it concerns a retrospective database study (registration number M19110329). We included

patients whose surgical procedures were conducted between 2014 and 2016. Data on clinical

characteristics, comorbidities, costing codes, and occurrence of superficial SSIs, dSSIs, and organ/

space SSIs diagnoses of anonymized patients were collected. The infection was confirmed by

the presence of purulent drainage, abscesses, or other evidence of local infection from deep soft

tissues (fascial and muscles) at the incision site. Symptoms experienced by dSSI patients could

include localized pain, tenderness, and fever (>38oC).9 A surgeon or an attending physician at the

hospital conducted individual dSSI diagnoses following the ECDC criteria. The ECDC protocol for

SSI surveillance defines a dSSI case as an infection acquired within 30 days of non-implant surgery

or up to a year after implant surgery.25

We matched the surgical specializations and the surgical technique used in patients with dSSI

and non-SSI. We categorized the types of surgeries using the ECDC protocol, based on clean (W1),

clean-contaminated (W2), contaminated (W3) and dirty wound surgeries (W4).25 Clean surgery is

defined as a surgical procedure that does not involve the alimentary, respiratory, or genitourinary

tracts. Surgical procedures that do not involve penetrating trauma fall under this category.25,26

Clean-contaminated surgeries, such as appendectomies, hysterectomies, or surgical procedures

relating to oropharyngeal cancer, entail entry into the alimentary, respiratory, or genitourinary

tracts without any usual contamination.25,26 Contaminated surgeries include coronary bypass

or hip replacement where there is gross spillage during the sterile procedure,27 or where there

is contact with gallbladder, urine, or the gastrointestinal lumen. Dirty or infected surgeries are

characterized by abscesses in visceral organs or infected organs or tissues.26,28,29 All of the surgical

procedures included in the study are listed in the supplementary material.

Outcome measurementsWe considered readmission, LoS in days, and costs of dSSI complications in our analysis.

Readmission associated with dSSIs was defined as a subsequent admission within the same

hospital that occurred within a 30-day period following the initial hospital discharge.30–32 LoS data

was sourced directly from the hospital discharge records for each admission. Costs were based on

the method within the Dutch healthcare systems labelled “the Dutch diagnoses related groups

(DRGs) (in Dutch: “Diagnose Behandeling Combinaties” [DBCs]) within specialized hospital care.

This is different from the common established DRGs that are used widely worldwide. These groups

are based upon a combination of treating specialty, diagnosis, and procedures and are uniformly

scored throughout the country. We applied hospitals reference prices of these groups for the

period 2014-2016.33–35 The World Health Organization (WHO) considers the use of prophylactic

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antibiotics as an important measure in SSI prevention.36 We therefore additionally evaluated the

impacts of prophylactic antibiotics on the outcome of dSSIs. These antibiotics were administered

to the patients within a period of 60 minutes prior to an incision being made.

Statistical analysesData analyses were performed using IBM SPSS statistics version 25. We presented descriptive

data as counts and percentages. Chi-square tests were applied to analyze statistical differences

in proportions between case and control groups. We performed univariate and multivariate

analyses of the cases to determine the predictors of a dSSI outcome including potentially relevant

predictors sex, age, the use of prophylactic antibiotics before the initial incision, types of surgery,

surgery sites, organ transplantation, and comorbidities. In the multivariate logistic regression

model, we minimized the degrees of freedom and excluded variables with a number of events

below 10 by merging age-related covariates into two categories (<65 vs. >65 years) and by the

categorizing four types of surgery W1 through W4. Each risk was presented as an odds ratio (OR)

together with a 95% confidence interval (CI). The variables that met the criteria of the 95% CI not

including 1 were considered significant and included in the analyses.

We assessed the impact of dSSI on the LoS outcome with a time to event analysis, the event

being the discharge of the patient. First, a Kaplan-Meier test and Log-Rank test were performed to

assess the differences in LoSs, measured as the times to hospital discharge between dSSI patients

and those in the control group within the 120-days time frame. Differences in mean costs were

analyzed using a two-sided t-test. Because the data on costs were highly skewed, we performed

bootstraps for 1,000 samples. Second, we assessed the predictors for the LoS outcome using a

Cox-regression analysis with categorical backward selection on variables of having dSSI, sex, age,

types of surgery, and comorbidities. Hazard ratios (HRs) lower than 1 indicated extended LoS,

while HR higher than 1 indicated shorter LoS.37 Statistical differences were considered significant

at a p-value < 0.05.

RESULTS

We examined a total of 12,285 registered patients who were admitted and underwent various

surgical procedures between 2014 and 2016 in all surgical departments. Of those, infections were

reported for 1,624 (13.2%) patients while no infections were reported 10,661 patients (86.8%). An

SSI rate was documented at 5.3% of the total surgical procedures, while dSSI was diagnosed in 393

(3.2%) that were included in the analyses as cases. There was no report of patients with organ/

space SSIs. The control group comprised 2,864 patients (23%) who fulfilled the matching criteria

relating to surgical procedures. The selection of the patients investigated in this study is shown

in Figure 4.1.

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Figure 4.1. Flow-chart depicting the process of selecting deep SSI and control. There was no report of patients with organ/space SSIs

Fifty-two percent of the patients were men with an average age of 52.9(+17.9) years compared to

females with a mean age of 51.7(+16.9) years. Out of 393 cases and 2,864 controls, 343 (87%) and

2,307 (81%) patients, respectively, were readmitted within 30 days of their surgical procedures.

Most initial admissions (32%) and readmissions (33%) concerned abdominal surgeries. Seventy-

nine percent of patients with neoplasms (281 out of 356) were readmitted with neoplasms being

the most frequently observed comorbidity (10.9%). The baseline characteristics of the patients are

summarized in Table 4.1. Incidences of dSSI were lower among patients who received prophylactic

antibiotics prior to undergoing surgery compared with such incidences among those who did not

receive prophylactic treatment (38% vs 62%, respectively, p<0.001).

The median LoS for all patients was 5 days (P25

-P75

: 2-10). Patients with dSSIs stayed longer in the

hospital compared to those without SSI (Log-Rank p-value <0.001). The median LoS was 7 days

(P25

-P75

: 2.5-14.5) for dSSI patients and 5 days (P25

-P75

: 1-9) for non-SSI patients. Kaplan-Meier plots of

LoS differences between dSSI and non-SSI patients are depicted in Figure 2. Having dSSI and being

65 years or older were the two independent factors associated with prolonged LoS, with HRs of

0.742 (95%CI: 0.679-0.809), and 0.809 (95%CI: 0.750-0.873), respectively. The multivariate logistic

regression model for prolonged LoS outcome are presented in Table 4.2.

Chapter 4

87

Tabl

e 4.

1. B

asel

ine

char

acte

ristic

s of

pat

ient

s w

ith d

SSI a

nd n

on-S

SI d

urin

g in

itial

adm

issi

ons

and

read

mis

sion

s

Char

acte

ristic

sIn

itial

surg

ical

adm

issi

on, n

(%)

Read

mit

ted,

n(%

)A

ll pa

tient

s(n

=3,2

57)

dSSI

(n=3

93)

Non

-SSI

(n

=2,8

64)

p-va

lue

All

patie

nts

(n=2

,650

)dS

SI (n

=343

)N

on-S

SI

(n=2

,307

)p-

valu

e

Sex

Mal

e1,5

12(4

6.4)

202(

51.4)

1,310

(45.

7)0.

035

1,256

(47.4

)18

3(53

.4)1,0

73(4

6.5)

0.01

8Fe

mal

e1,7

45(5

3.6)

191(

48.6

)1,5

54(5

4.3)

1,394

(52.

6)16

0(46

.6)

1,234

(53.

5)A

ge<6

5 ye

ars

2,46

6(75

.7)

269(

68.4)

2,197

(76.

7)<0

.001

2,03

0(76

.6)

234(

68.2

)1,7

96(7

7.9)

<0.0

01>6

5 ye

ars

790(

24.3

)12

4(31

.6)

666(

23.3

)62

0(23

.4)10

9(31

.8)

511(

22.1)

Ant

ibio

tic p

roph

ylax

isN

o1,5

62(4

8.0)

242(

61.6

)1,3

20(4

6.1)

<0.0

011,2

52(4

7.2)

207(

60.3

)1,0

45(4

5.3)

<0.0

01Ye

s1,6

95(5

2.0)

151(

38.4)

1,544

(53.9

)1,3

98(5

2.8)

136(

39.7

)1,2

62(5

4.7)

Type

s of s

urge

ryCl

ean

1,571

(48.

2)18

0(45

.8)

1,391

(48.

6)0.

329

1,301

(49.1

)16

1(46

.9)1,1

40(4

9.4)

0.39

2Cl

ean-

cont

amin

ated

572(

17.6

)55

(14.

0)51

7(18

.1)0.

056

400(

15.1)

46(1

3.4)

354(

15.3

)0.

351

Cont

amin

ated

940(

28.9)

130(

33.1)

810(

28.3

)0.

056

788(

20.7

)10

9(31

.8)

679(

29.4)

0.37

5D

irty

174(

5.3)

28(7

.1)14

6(5.1

)0.

094

161(

6.1)

27(7

.9)13

4(5.

8)0.1

36Lo

catio

n of

surg

ery

Hea

d an

d ne

ck32

9(10

.1)83

(21.1

)24

6(8.

6)<0

.001

308(

11.6

)79

(23.

0)22

9(9.9

)<0

.001

Uppe

r-ext

rem

ity82

(2.5

)25

(6.4)

57(2

.0)

<0.0

0157

(2.2

)23

(6.7

)34

(1.5

)<0

.001

Thor

ax55

8(17

.1)42

(10.

7)51

6(18

.0)

<0.0

0138

0(14

.3)

28(8

.2)

352(

15.3

)<0

.001

Abdo

men

1,055

(32.

4)15

3(38

.9)90

2(31

.5)

0.00

386

5(32

.6)

130(

37.9)

735(

85.0

)0.

026

Spin

e79

1(24

.3)

28(7

.1)76

3(26

.6)

<0.0

0167

2(25

.4)26

(7.6

)64

6(28

.0)

<0.0

01Lo

wer

-ext

rem

ity44

2(13

.6)

62(1

5.8)

380(

13.3

)0.1

7336

8(13

.9)57

(16.

6)31

1(13

.5)

0.117

Org

an tr

ansp

lant

atio

n10

2(3.1

)7(

6.9)

95(3

.30)

0.101

95(3

.6)

6(1.7

)89

(3.9)

0.05

0Co

mor

bidi

ties

Neo

plas

m35

6(10

.9)62

(15.

8)29

4(10

.3)

<0.0

0128

1(10

.6)

55(1

6.0)

226(

9.8)

0.00

1D

iabe

tes m

ellit

us20

(0.6

)12

(3.1)

8(0.

3)<0

.001

20(0

.8)

12(3

.5)

8(0.

3)<0

.001

Card

iova

scul

ar d

iseas

es19

6(6.

0)12

(3.1)

184(

6.4)

0.01

111

2(4.

2)10

(2.9)

102(

4.4)

0.25

0

The impacts of deep surgical site infections

4

88

Figure 4.2. Kaplan-Meier curve of the lengths-of-stay of dSSI patients and controls with non-SSIs (LogRank, p<0.001).

Note: There is no any hospital discharge after day-82 among dSSI patients

Table 4.2. Predictive factors for length of stay

Variables B SE Wald p-value HR Lower 95%CI HR

Upper 95%CI HR

Having dSSINo Ref RefYes -0.299 0.045 44.683 <0.001 0.742* 0.679 0.809SexMale Ref RefFemale 0.049 0.030 2.624 0.105 1.050 0.990 1.115Age<65 years Ref Ref>65 years -0.212 0.039 29.747 <0.001 0.809* 0.750 0.873Antibiotic prophylaxisNo Ref RefYes 0.092 0.031 8.615 0.003 1.096* 1.031 1.166Types of surgeryClean Ref RefClean-contaminated 0.015 0.046 0.112 0.738 1.016 0.928 1.112Contaminated 0.105 0.037 8.053 0.005 1.110* 1.033 1.193Dirty 0.291 0.069 17.600 <0.001 1.338* 1.168 1.533ComorbiditiesNeoplasm 0.187 0.045 17.328 <0.001 1.206* 1.104 1.317Cardiovascular diseases -0.046 0.084 0.299 0.585 0.955 0.881 1.125

Note: *Statistically significantAbbreviations: dSSI = deep surgical site infection, CI = confident interval, HR = hazard ratio, Ref = reference

Chapter 4

89

The mean cost of clean surgery was €4,391 per patient, with respective mean costs of €5,505

and €4,247 for dSSI and non-SSI patients (a difference of €1,258; p<0.001). The mean cost of

clean-contaminated surgeries per patient was estimated at €6,788, with respective mean costs

of €12,479 and €6,182 for dSSI and non-SSI patients (a twofold difference; p<0.001). The average

hospitalization cost was highest for contaminated surgeries with at an average of €8,219 per

patient, with €13,170 for dSSI and €7,424 for non-SSI (equivalent to 1.8 times higher; p<0.001). Dirty

surgery showed the lowest mean cost (€3,027) among all types of surgical procedures. Overall,

the average cost for all cases was €5,844 (+121) per patient, with respective mean costs of €9,016

(+343) and €5,409 (+120) for dSSI and non-SSI (cost difference of €3,607; p<0.001). Table 2 lists the

hospitalization costs per patient for each type of surgery.

Table 4.3. Hospitalization cost per patient categorized by the types of surgeryCosts, mean (SE) All cases dSSI cases Non-SSI cases Cost difference p-value

Clean surgery €4,391.11(109.21) €5,505.08(321.26) €4,246.95(113.06) €1,258.13(341.41) <0.001

Clean-contaminated surgery €6,787.76(447.29) €12,478.61(1,420.59) €6,182.34(466.91) €6,296.27(1,494.24) <0.001

Contaminated surgery €8,218.74(231.74) €13,170.11(1,019.01) €7,424.07(200.30) €5,746.03(644.93) <0.001

Dirty surgery €3,027.80(298.48) €5,497.86(1,027.58) €2,554.09(281.70) €2,943.76(783.10) <0.001

Note: SE = standard error

In the multivariate analyses, we excluded the diabetes mellitus covariate as the number of patients

in the control group was too small (n=8). There were two independent variables turning out as

factors for dSSI: age >65 (OR: 1.334; 95%CI: 1.036-1.720) and neoplasm (OR: 2.050; 95%CI: 1.473-

2.854). The use of prophylactic antibiotics before the initial incision showed a protective effect

against dSSI (OR: 0.424; 95%CI: 0.344-0.537). The results of the univariate and multivariate analyses

are summarized in Table 4.4.

Table 4.4. Predictive factors for deep surgical-site infections

Characteristics Univariate analysis Multivariate analysisCrude OR 95%CI Adjusted OR 95%CI

SexMale Ref RefFemale 0.797* 0.645-0.984 0.835 0.670-1.034Age<65 years Ref Ref>65 years 1.521* 1.209-1.913 1.977* 1.542-2.534Antibiotic prophylaxisNo Ref RefYes 0.533* 0.430-0.662 0.459* 0.366-0.575Types of surgeryClean Ref RefClean-contaminated 0.822 0.598-1.130 0.793 0.564-1.114Contaminated 1.240 0.974-1.579 1.101 0.847-1.431Dirty 1.482 0.961-2.285 1.367 0.879-2.127ComorbiditiesNeoplasm 1.637* 1.217-2.203 1.466* 1.063-2.021Cardiovascular diseases 0.459* 0.253-0.831 0.372* 0.197-0.702

Note: *Statistically significantAbbreviations: CI = confident interval, OR = odds ratio, Ref = reference

The impacts of deep surgical site infections

4

90

DISCUSSIONS

Our findings indicated that the costs, LoS and readmission rates of dSSI cases were higher than

those of non-SSI cases in all types of surgeries. Clean surgeries associated with the head and

neck, thorax, extremities, and spine were performed most frequently in this hospital, followed

by contaminated and dirty surgeries in which the abdominal region was commonly the targeted

site. Our findings documented that having dSSI and being aged >65 were the two independent

factors for extended LoS. Notably, elderly patients (>65 years) and patients with neoplasm or

cardiovascular diseases would have a higher risk of dSSI, while patients receiving prophylactic

antibiotics would have a lower risk of dSSI. This finding supports enhancing developed facilities

for managing infection prevention procedures in the Netherlands.

The hospital under investigation has developed an integrated stewardship with a theragnostic

approach, which has been conceptualized in a multi-faceted model of antimicrobial (A), infection

prevention (I), and diagnostic (D) stewardship, labeled AID model. AID involves trained staff,

covering diverse specialties, are dedicated to controlling hospital-acquired infections and

stemming further antimicrobial resistance.16,17,38 The dSSI rates in our study were documented in

the range of PREZIES findings that were between 0.2% and 5.9% of all surgical procedures.20 In a

systematic review, the global incidence of SSIs ranged from 0% to 70%.7 The rates of SSI depend on

the surgery types and locations as well as prophylactic treatment.7,39 WHO and ECDC also revealed

that the use of prophylactic antibiotics prior to initial incision reflects a proper and successful

modality to decrease SSI incidence.25,40 This illustrates the crucial moment of the preoperative

phase to prevent SSIs by adherence to adequate use of prophylactic antibiotics relating to their

selection, optimal dosing, and timing.7,41 Related monitoring relating of postoperative measures

focuses on the prolonged use of prophylactic antibiotics and the timing of drain removal.40

Our findings show that the hospitalization cost for dSSI was higher compared with non-SSI. The

burden of additional costs incurred for dSSI cases is plausibly reflected in prolonged hospitalization,

as well as readmissions.21,42–44 As a comparative reference, respective SSI and non-SSI costs of €1,011

and €1,167 per patient day may serve as reported by a German hospital geographically close to

the hospital in Groningen. The reported LoS for those patients ranged between 34.4 and 16.5 days

with total hospitalization costs per SSI and non-SSI patients of €36,261 and €13,356, respectively.45,46

In addition to the impact of SSIs on prolonged LoS, a previous study in cardiac surgery showed

that SSIs evidently had a significant impact on LoS and costs as indicated by 3.8-fold and 5.8 fold

increases, respectively, as compared with these measures for non-SSI patients.47

Approximately 87% of dSSI patients in the present study were readmitted compared with

81% of non-SSI patients. Readmission were often due to the presence of comorbidities such as

neoplasm, diabetes, and cardiovascular diseases. Evidently, readmission contributes to high costs.

Wick et al., estimated that the cost of surgical readmission after colorectal surgery in the United

States was US$8,885 per stay with a mean LoS of 8 days.48 A previous study found that more

than 50% of SSI cases were readmitted for revision surgery because of wound infections at the

surgery sites and other post-surgical complications such as bleeding, dehydration, renal failure,

Chapter 4

91

embolism, cardiovascular events, and ileus.1,3 Other studies indicate that approximately 76%-97%

of SSI patients are readmitted at least once for revision surgery or wound debridement.49,50 Post-

surgical readmission could be substituted by performing a home visit or providing outpatient

care within an earlier 30-day medical follow-up after surgical discharge, possibly alleviating the

costs. Jencks et al., reported that the post-surgery monitoring for infections effectively reduced

readmission rates.51 Additionally, the timing of unanticipated readmissions after surgical discharge,

which is mainly contingent on the emergence of crises relating to serious complications or existing

comorbidities, requires further investigation.1 Moreover, in our study, dSSI patients aged 65 years

or above were significantly at higher risk for readmissions. This finding is in line with that of Kaye et

al., who reported that the LoS for hospitalization and post-surgery readmission of elderly patients

with SSIs was three times greater than that for non-SSI patients. Moreover, the mean cost incurred

by older patients was US$ 43,970, which was double the cost incurred by non-SSI patients.52

In clean surgeries, such as thyroidectomies, the prophylactic use of antimicrobial agents is

unlikely to be necessary.53 Reported SSI rates for clean surgeries ranged between 0% and 3%.14,54

The incidence of SSI in clean-contaminated surgeries is reported to range between 3% and 11%,13,14

while the SSI rate for contaminated surgeries is approximately 15% of all surgical procedures.14

Additionally, a high SSI rate of at least 20% has been reported for laparoscopies performed in

cases of perforated appendicitis, which are considered dirty surgeries.15 In line with our findings, a

previous systematic review and an original study reported that the risk of SSI is high for orthopedic

and general surgery involving the thorax, abdomen, spine, and lower extremities.1,7 The reason

for these findings may be that the risk of SSI is also contingent on the performance of individual

procedures that facilitate the invasion of underlying pathogens. Furthermore, our findings showed

that the total hospitalization costs for dirty procedures were estimated higher compared to those

for contaminated surgery. This could be explained that contaminated surgeries mostly involved

gastrointestinal tract and deep organ which was the surgeon performing major procedures. On

the other hand, dirty surgeries predominantly concerned cases of drainage of the abscess.

To the best of our knowledge, this is the first study to combine a comprehensive analysis

of the impacts of dSSI on the hospitalization costs, and LoS. However, it has some limitations.

First, we used data sourced from one academic hospital only and this hospital’s DBC coding for

inpatient reimbursement, which may have led to the exclusion of some patients who received

medication from the community or from general practitioners. Second, we applied a payer-based

analytical perspective and did not consider indirect costs, specifically those associated with

productivity losses. Third, this study did not cover the preferences of patients with dSSI and post-

surgery, for example, regarding the quality of life outcomes.55 Fourth, in this study, we also found

a few patients with bacteremia. However, numbers were too small to perform any analyses on

the correlation between dSSI and bacteremia. Fifth, the generalization of the findings to other

countries, for example, in the context of low-middle income countries may require adjustments

relating to clinical-economic impacts and local healthcare systems. Moreover, other parameters in

SSI prevention bundles, such as shaving procedure, disinfections, operating room category, door

opening frequency, were not taken into account in the analyses since the lack of relevant data.

The impacts of deep surgical site infections

4

92

Overall, however, the findings of this study endorsed the implementation of infection prevention

measures with quintessential AID stewardship within a hospital setting.

CONCLUSIONS

The impacts of dSSI manifests itself not only during the course of the disease but also in its

consequences, as reflected in readmission rates, extended LoS, and additional costs. Identified

independent variables for dSSI risk, notably patients’ older age and neoplasm comorbidity, should

intensively be monitored, and prophylactic antibiotics should definitely be considered in such

cases. In addition, patients having dSSI and being 65 years and over both had an association with

prolonged LoS. Further research should be directed towards evaluating patients’ characteristics

in relation to dSSI impacts on an individual level rather than our group-based approach to allow a

personalized strategy in infection prevention.

Abbreviations:AID: Antimicrobial, infection prevention, and diagnostic

AMS: Antimicrobial stewardship

CI: Confidence interval

DBC: Diagnose Behandeling Combinatie

DGS: Diagnostic stewardship

dSSI: Deep surgical site infection

EUCAST: European Committee on Antimicrobial Susceptibility Testing

GNB: Gram-negative bacteria

HR: Hazard ratio

ICU: Intensive care unit

LoS: Length of Stay

MRSA: Methicillin-Resistant Staphylococcus aureus

OR: Odds ratio

SE: Standard error

SSI: Surgical-site infection

UMCG: University Medical Center Groningen

Chapter 4

93

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28. Simmons BP. Guideline for prevention of surgical wound infections. Am J Infect Control. 1983;11(4):133-143.29. Mangram AJ, Horan TC, Pearson ML, Silver LC, Jarvis WR. Guideline for Prevention of Surgical Site Infection, 1999. Centers

for Disease Control and Prevention (CDC) Hospital Infection Control Practices Advisory Committee. Am J Infect Control. 1999;27(2):97-132; quiz 133-134; discussion 96.

30. Hansen LO, Young RS, Hinami K, Leung A, Williams M V. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. doi:10.7326/0003-4819-155-8-201110180-00008

31. McPhee JT, Nguyen LL, Ho KJ, Ozaki CK, Conte MS, Belkin M. Risk prediction of 30-day readmission after infrainguinal bypass for critical limb ischemia. J Vasc Surg. 2013;57(6):1481-1488. doi:10.1016/j.jvs.2012.11.074

32. Saunders RS, Fernandes-Taylor S, Rathouz PJ, et al. Outpatient follow-up versus 30-day readmission among general and vascular surgery patients: a case for redesigning transitional care. Surgery. 2014;156(4):949-956. doi:10.1016/j.surg.2014.06.041

33. UMCG passantenprijzen 2014. https://docplayer.nl/12632899-Umcg-passantenprijzen-2014-dbc-zorgproducten-verzekerde-zorg-01-06-2014.html. Accessed November 11, 2019.

34. UMCG passantenprijzen 2015. https://docplayer.nl/15645564-Umcg-passantenprijzen-2015-dbc-zorgproducten-verzekerde-zorg-01-01-2015.html. Accessed November 11, 2019.

35. UMCG passantenprijzen 2016. https://docplayer.nl/28813274-Umcg-passantenprijzen-2016-dbc-zorgproducten-verzekerde-zorg-per.html. Accessed November 11, 2019.

36. World Health Organization. Global guidelines on the prevention of surgical site infection. https://www.who.int/gpsc/ssi-prevention-guidelines/en/. Published 2016. Accessed January 1, 2020.

37. Spruance SL, Reid JE, Grace M, Samore M. Hazard ratio in clinical trials. Antimicrob Agents Chemother. 2004;48(8):2787-2792. doi:10.1128/AAC.48.8.2787-2792.2004

38. Dik J-WH, Sinha B, Lokate M, et al. Positive impact of infection prevention on the management of nosocomial outbreaks at an academic hospital. Future Microbiol. 2016;11:1249-1259. doi:10.2217/fmb-2016-0030

39. Cosgrove MS. Infection control in the operating room. Crit Care Nurs Clin North Am. 2015;27(1):79-87. doi:10.1016/j.cnc.2014.10.004

40. World Health Organization. Global Guidelines for the Prevention of Surgical Site Infection. Geneva; 2018.41. Bratzler DW, Dellinger EP, Olsen KM, et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Heal

Pharm. 2013;70(3):195-283.42. Badia JM, Casey AL, Petrosillo N, Hudson PM, Mitchell SA, Crosby C. Impact of surgical site infection on healthcare

costs and patient outcomes: a systematic review in six European countries. J Hosp Infect. 2017;96(1):1-15. doi:10.1016/j.jhin.2017.03.004

43. Kashimura N, Kusachi S, Konishi T, et al. Impact of surgical site infection after colorectal surgery on hospital stay and medical expenditure in Japan. Surg Today. 2012;42(7):639-645.

44. Mahmoud NN, Turpin RS, Yang G, Saunders WB. Impact of surgical site infections on length of stay and costs in selected colorectal procedures. Surg Infect (Larchmt). 2009;10(6):539-544. doi:10.1089/sur.2009.006

45. Graf K, Ott E, Vonberg R-P, et al. Surgical site infections--economic consequences for the health care system. Langenbeck’s Arch Surg. 2011;396(4):453-459. doi:10.1007/s00423-011-0772-0

46. Graf K, Ott E, Vonberg R-P, Kuehn C, Haverich A, Chaberny IF. Economic aspects of deep sternal wound infections. Eur J Cardiothorac Surg. 2010;37(4):893-896. doi:10.1016/j.ejcts.2009.10.005

47. Kusachi S, Kashimura N, Konishi T, et al. Length of stay and cost for surgical site infection after abdominal and cardiac surgery in Japanese hospitals: multi-center surveillance. Surg Infect (Larchmt). 2012;13(4):257-265. doi:10.1089/sur.2011.007

48. Wick EC, Shore AD, Hirose K, et al. Readmission rates and cost following colorectal surgery. Dis Colon Rectum. 2011;54(12):1475-1479. doi:10.1097/DCR.0b013e31822ff8f0

49. Trick WE, Scheckler WE, Tokars JI, et al. Modifiable risk factors associated with deep sternal site infection after coronary artery bypass grafting. J Thorac Cardiovasc Surg. 2000;119(1):108-114. doi:10.1016/s0022-5223(00)70224-8

50. Borger MA, Rao V, Weisel RD, et al. Deep sternal wound infection: risk factors and outcomes. Ann Thorac Surg. 1998;65(4):1050-1056. doi:10.1016/s0003-4975(98)00063-0

51. Jencks SF, Williams M V, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. doi:10.1056/NEJMsa0803563

52. Kaye KS, Anderson DJ, Sloane R, et al. The effect of surgical site infection on older operative patients. J Am Geriatr Soc. 2009;57(1):46-54. doi:10.1111/j.1532-5415.2008.02053.x

53. Lu Q, Xie S-Q, Chen S-Y, Chen L-J, Qin Q. Experience of 1166 thyroidectomy without use of prophylactic antibiotic. Biomed Res Int. 2014;2014:758432. doi:10.1155/2014/758432

54. Saeedinia S, Nouri M, Azarhomayoun A, et al. The incidence and risk factors for surgical site infection after clean spinal operations: A prospective cohort study and review of the literature. Surg Neurol Int. 2015;6:154. doi:10.4103/2152-7806.166194

55. Gheorghe A, Moran G, Duffy H, Roberts T, Pinkney T, Calvert M. Health Utility Values Associated with Surgical Site Infection: A Systematic Review. Value Heal. 2015;18(8):1126-1137.

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SUPPLEMENTARY MATERIAL

Types of surgical procedures according to types of surgery typesTypes of surgery Surgical procedures

Clean surgery Craniotomy, fistel extirpation, implantation, lipoma removal, ganglioma removal, nose reparative surgery, skin transplantation, skin transposition, tracheostomy, ventricle shunt insertion, ventriculocisternostomy, amputation, distal humerus fracture repositioning, extensor-tonolysis surgery, fascia palmaris excision, fasciectomy, flexor tenolysis surgery, intracondylary fracture repositioning, oclerani fracture reparative surgery, panaritium exploration, proximal-ulnaris osteosynthesisimaterial, skin transposition, subcapital facture humerus reparative surgery, tendovaginitis-stenosans surgery, tumor excision, ulnar resection, acromioclavulary luxation surgery, aorta ascendens replacement, aortic protheses implantation, aorta valve replacement, arthroscopy, coronary artery bypass graft (CABG), heart catheterization, heart transplantation, lobectomy, lumpectomy, lung transplantation, mammae amputation, mammae protheses implantation, mammae reconstruction, mammae-transplantation, mastectomy, mastopexy, mitral valve implantation, osteomyelitis sternum drainage, re-thoracotomy, shoulder rupture repair, skin transplantation, sternum fixation and refixation, thorax deviation excision, caput-collum femoral prosthesis, total hip prostheses, discusinterlumbalis excision, lumbosacral spondylosyndesis surgery, lumbosacral osteosynthesis material removal, lumbospondylosyndesis surgery, open lumbal repositioning, spine fracture repositioning, spondylodiscitis exploration, thoracal spondylosyndesis surgery, femoro-cruris vein bypass, tibia amputation, endarectomy femoral artery, exarticulation procedure, femoralis prosthesis, femorointramedular fixation, iliaco-femoralis artery bypass, knee arthrotomy, knee-arthritis drainage, knee-osteomyelitis surgery, kneeprosthesis, lymphadenectomy, pesudoarthrosis with transplantation, repair of femoral artery aneurysm, talocrural-arthrodosys, tibia osteomyelitis surgery, total knee arthroplasty, tumor extirpation.

Clean-contaminated surgery

Abdominal cancer surgery, abdominoperineal extirpation, adhesiolysis procedure, ablatio mammae, appendectomy, caudo-pancreas resection, cholecystectomy, hemihepatectomy, hepatic resection, ileocecal resection, ileostoma, ileostomy, intraperitoneal catheterization, liver transplantation, laparotomy, partial hepatic resection, retroperitoneal excision, transversectomy, vulvectomy.

Contaminated surgery Amputation, appendectomy, colostoma, dermolipectomy, entero-enterostomy regio small intestine, hemicolectomy, laparotomy, oesophagus resection and gastric reconstruction, pancreatic duodenectomy, partial gastroexcision, partial intestine resection, pylorus pancreaticoduodenectomy, primary anastomosis with ileal resection, primary anastomosis with jejunal resection, proeflaparotomy, pyloromytomy, rectal resection, rectosigmoid anastomosis, rectosigmoid resection, relaparotomy, sigmoid resection and colostoma, subtotal colostomy, total gastro excision, total pancreas resection (including duodenum).

Dirty surgery Abscess drainage, amputation with gangrenous lower extremity, abscess, and fistel mammae drainage, hip drainage, abscess-fistel incision drainage, ankle-osteomyelitis drainage, abscess subdural drainage.

The impacts of deep surgical site infections

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97

3Part

Empirical antibiotics for hospitalized community-

acquired pneumonia

98

99

CHAPTER 5Multidrug-resistant infections among hospitalized adults with community-acquired pneumonia in an Indonesian

tertiary referral hospital

Abdul Khairul Rizki Purba

Purwantyastuti

Armen Muchtar

Laksmi Wulandari

Alfian Nur Rosyid

Priyo Budi Purwono

Tjip S van der Werf

Alex W. Friedrich

Maarten J. Postma

Infect Drug Resist, 2019(12): 3663-3675. doi: 10.2174/IDR.S217842.

100

ABSTRACT

Objectives: To evaluate the clinical and microbiological appearance among hospitalized

pneumonia patients focusing on resistance and risk factors for mortality in a referral hospital.

Patients and methods: The study was an observational retrospective study on patients with CAP

from 2014 to 2016 at Dr. Soetomo referral hospital of Surabaya, Indonesia. All positive cultures with

antimicrobial susceptibility results from blood and respiratory specimens were included. Drug-

susceptible pathogens and MDR organisms were also assessed in terms of clinical characteristics,

day-3 clinical improvement, and 14-day mortality.

Results: Of 202 isolates, 181 possessed antimicrobial susceptibility data. S. pneumoniae was

the most prevalent pathogen causing CAP (18.3%). Most patients were empirically treated with

ceftriaxone (n=75; 41.4%). Among beta-lactam antibiotics, the susceptibility to the third-generation

cephalosporins remained relatively high, between 67.4% and 82.3%, compared with the other

beta-lactams such as amoxicillin/clavulanate and ampicillin/sulbactam (a sensitivity rate of 36.5%

and 47.5, respectively). For carbapenem antibiotics, imipenem and meropenem susceptibility was

69.6% and 82.3% respectively. Approximately 22% of isolates were identified as MDR that showed

significant differences in clinical outcomes of 14-day mortality rates (p<0.001). Notably, patients

with day-3 improvement had a lower risk of mortality (OR= 0.06; 95%CI= 0.02-0.19).

Conclusions: One-fifth of causative agents among hospitalized CAP cases were identified as MDR

organisms. The pathogens of MDR and non-MDR CAP remain susceptible to the third-generation

cephalosporins. Together with additional consideration of culture findings and the Pneumonia

Severity Index (PSI) assessment, a 3-day clinical assessment is essential to predict the prognosis of

14-day mortality.

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INTRODUCTION

Community-acquired pneumonia (CAP) is mostly due to bacterial infections which are specifically

recognized as community-acquired bacterial pneumonia (CABP).1 All guidelines agree that at

least one empiric antibiotic is needed especially for hospitalized patients.2–5 The international

association, Community-Acquired Pneumonia Organization (CAPO) reported that between 2001

and 2011 mortality rates of the infection reached 7.3%, 9.1%, and 13.3%, in North America, Europe,

and South America respectively.6 In 2013, the incidence rates of CAP in low-middle-income

countries (LMICs) such as Indonesia was 4.5% and remained high at 4% in 2018.7,8 No available

published data relate to the mortality of the disease in the country.

In order to achieve the appropriate therapeutics, updated epidemiology of antimicrobial

resistance is required to support therapeutic guidelines. International associations such as

the British and American Thoracic Societies (BTS and ATS) have indicated that gram-positive

bacteria are the most widespread causes of CAP.3,5 Nevertheless, the guidelines reflected studies

published in 2003 from high-income countries where Streptococcus pneumoniae was identified

as the dominant pathogen causing CAP9, and beta-lactam antibiotics were recommended as the

preferred treatment.2,3,5

Studying CAP among LMICs, etiology of the disease was generally problematic. Less restriction

of antibiotic use in the community and the differences in the healthcare systems in LMICs may

impact on the existence of MDR pathogens.10,11 Indiscriminate use of antimicrobials, not guided by

microbiological guidance, generally results in the emergence of antimicrobial resistance, both for

individual patients and at the community level. The World Health Organization (WHO) has labeled

the use of anti-infectives with a high warning in the global report on surveillance of antimicrobial

resistance.12 Antibiotic resistance leads to long hospitalization periods, treatment failure, and a

high economic burden.13,14

Local epidemiology may vary by country, and therefore local protocols and guidelines should

be based on local prevalence and susceptibility data, which will guide the appropriate use of

antibiotics, thereby improving outcomes, reducing the duration of hospitalization and preventing

the emergence of antimicrobial resistance with inherent increased costs. The local epidemiology

of CAP etiology could support stakeholders to develop strategies on prescribing to control the

resistance in the community and in hospitals. The major gap between the guidelines’ review and

the local patterns in terms of the pathogens causing CAP may drive several healthcare centers to

implement the use of different antibiotics as alternative treatments to the resistance of community

infections.15 Notably, Acinetobacter baumannii infections associated with CAP contributed to

multidrug resistance (MDR) and has led to high mortality in Asia Pacific countries.16–18 In Indonesia,

the data on recent CAP etiology and MDR is limited. This study aims to analyze the etiology of CAP

and MDR-CAP, with a focus on the rate of antibiotic resistance and the risk factors for CAP-related

mortality in an Indonesian tertiary referral hospital.

Multidrug-resistant infections among hospitalized-CAP

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MATERIAL AND METHODS

Study design and ethical approvalWe performed a retrospective observational study involving adult patients newly admitted to

hospital with CAP. We collected the data from Dr. Soetomo Hospital, a large tertiary referral and

academic hospital with approximately 1,514 beds in East Java, Indonesia. The study proposal

was submitted to the research and development center of Dr. Soetomo Hospital. The study was

approved by the ethical committee of Dr. Soetomo Hospital, Surabaya, Indonesia, with letter no.

480/Panke.KKE/X/2014). The committee decided that the study did not need a review in terms

of patient consent because of the retrospective observational design. The study complies with

the agreement on Indonesia research conduct and the Declaration of Helsinki (Ethical Principles

for Medical Research Involving Human Subjects version 2013).19 The data was obtained from the

medical record department with patient anonymity and confidentiality maintained.

Patients and treatmentThe data was gathered from the inpatient registry database with an International Classification

of Diseases (ICD) code of 10 J.18.x from 2014 to 2016. The inclusion criteria of the study included

all inpatients aged 20 years or above with CAP as a primary diagnosis. The respiratory tract

sputum or blood samples were collected before the start of empirical antimicrobial treatment.

We only included patients who met the diagnosis based on the national guidelines for CAP from

the Indonesian Society of Respirology.20 The diagnosis was based on new pulmonary infiltrates

on the chest radiograph, progressive cough, purulent sputum, fever (>38oC), and at least two

additional symptoms consisting of increased dyspnea, pleuritic pain, leukocytosis (>10,000/mm3)

or leukopenia (<4,500/mm3), lung consolidation suggested by dullness to percussion of the chest,

and abnormal chest auscultation findings including crepitations, crackles, or rhonchi. We excluded

patients who had received parenteral antibiotics 48 hours before hospitalization, those with

negative cultures, and those hospitalized in other healthcare facilities more than 14 days within

30 days before the current hospital admission. Regarding CAP diagnosis, a pulmonologist made

a visit at the first 24-48 hours of admission to clarify the diagnosis. Therefore, we also excluded

patients who died within 24 hours after admission. In the hospital, patients received empirical

antibiotics according to a guideline of the Indonesian Society of Respirology for CAP within 24h

of admission. To ensure adequate identification of etiology among CAP patients who had culture

samples obtained after empirical antibiotic administrations, and also excluded any patients

whose culture samples were obtained more than 48 hours after admission. The description of the

management of hospitalized CAP patients is presented in Table 5.1.

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Table 5.1 Indonesian guideline for CAP patients20

Patient care The strategies:Wards One of the following options:

1. Beta-lactam iv + beta-lactamase inhibitor iv2. The second and third generation of cephalosporins iv3. Respiratory fluoroquinolone ivMacrolide (additional antibiotic when atypical infections identified)

Intensive care No pseudomonal infection:1. The third-generation of cephalosporin iv + macrolideWhen a pseudomonal infection presents, one of the following options:1. anti-pseudomonal cephalosporin iv2. Carbapenem iv + anti-pseudomonal antibiotic iv3. Aminoglycoside ivIf there is an atypical infection, using the following three-drug combination:anti-pseudomonal cephalosporin iv (or carbapenem iv) + macrolide (or respiratory fluoroquinolone iv) + aminoglycoside iv

Note: Adapted from Indonesian Society of Respirology, Guideline for diagnosis and management of community-acquired pneumonia in Indonesia [Perhimpunan Dokter Paru Indonesia. Pneumonia Komuniti: pedoman diagnosis dan penatalaksanaan di Indonesia]. 2003. Available from https://www.klikpdpi.com/

Microbiological evaluationBacterial culture from patients’ sputum and blood samples collected within the first 24h of

admission was tested for microbiological evaluation. In terms of quality, the sputum was

considered to be acceptable where it contained >25 granulocytes and <10 squamous epithelial

cells per low-power field (x10).21 The eligible sputum specimen was subsequently submitted to

species identification and susceptibility testing. We assessed the susceptibility to the available

antimicrobial agents in the hospital including amoxicillin-clavulanate (AMC), ampicillin-sulbactam

(SAM), ticarcillin-clavulanate (TIC), piperacillin-tazobactam (PIP), cefazolin (CFZ), ceftazidime

(CAZ), cefoperazone-sulbactam (CFP, trimethoprim-sulfamethoxazole (STX), ciprofloxacin (CIP),

levofloxacin (LVX), moxifloxacin (MXF), imipenem (IPM), and meropenem (MEM). Testing of

amikacin (AMK) and gentamicin (GEN) susceptibilities were conducted only for Gram-negative

bacteria (GNB). In particular, vancomycin susceptibility was tested on Gram-positive bacteria

(GPB) only. Pathogens were defined as multidrug-resistant (MDR) if the organisms were resistant

to at least one single agent in three or more groups of antimicrobial agents.22 The antimicrobial

susceptibility pattern was reported as sensitive (S), intermediate-susceptible (I), or resistant (R) for

each isolated species based on the microbiology department of the hospital using the Clinical

and Laboratory Standards Institute (CLSI) criteria.23

Clinical evaluation To explore the impact of MDR infections compared to non-MDR infections, we compared

baseline demographics, physical examination, laboratory and radiology findings, comorbidities,

pneumonia severity index (PSI) scores, the need for intensive care, the empirical antimicrobial

treatment given, length of stay (LoS), clinical improvement on day-3, and 14-day mortality. On day-

3 of hospital admission, we assessed the following clinical symptoms comparing with baseline

on admission: mental status; respiratory rate (n: 12-24/min); heart rate (n:<100 beats/min); systolic

Multidrug-resistant infections among hospitalized-CAP

5

104

blood pressure (cut-off >90 mmHg); arterial oxygen saturation (cut-off: >90%); oral intake ability;

temperature (<38.5oC); and leucocyte count (3.5-10.5 x109/L).3 PSI is a validated scoring system

representing the baseline physiologic parameters and pre-existing comorbidities adding up a

total score of 19 factors; the total score is categorized into five classes: class I (<51), class II (51-70),

class III (71-90), class IV (91-130), and class V (>130).24

Statistical analysisThe statistical analyses were performed using SPSS (SPSS 23, University of Groningen, Netherlands).

For categorical data, chi-square (or Fisher exact test with more than 20% cells with expected values

less than 5) were used. For continuous variables, the distribution of data was first tested. Data with

normal distribution were provided as mean and standard deviation (SD). Otherwise, the data were

expressed as median with 25th and 75th percentiles. The differences among the empirical antibiotics

on all analyses were considered statistically significant at p-value <0.05. Multivariate analysis was

used to determine whether there was an independent association of three risk factors of 14-day

mortality. First, the host factors analyzed were gender, age (60 or above), cardiovascular disease,

neoplasm, diabetes mellitus (DM), liver diseases, renal insufficiency, since those comorbidities

were independent risk factors of mortality.25,26 Also, PSI class 3 or above, and day-3 improvement

were integrated assessments considered in the analyses. Second, the pathogen factor of drug-

susceptible or MDR. Third, the treatment: combinations of empirical antimicrobials compared to a

single antimicrobial agent. Each risk factor was presented as an odds ratio (OR) with a confidence

interval (CI) of 95% where the value of 95%CI not including 1 indicated no statistical difference.

RESULTS

Pathogen characteristics and antimicrobial susceptibility Two hundred and two bacterial isolates were collected from 181 patients. Each patient had one result of antimicrobial susceptibility testing. The identified causative agents are shown in Table 5.2. Most culture specimens were collected from the respiratory tract (97.5%). The dominant pathogen was S. pneumoniae (18.3%) followed by S. viridans (17.8%), A. baumannii (13.9%), K. pneumoniae (13.4%), P. aeruginosa (9.4%), Enterobacter spp. (5.4%), E. coli (5%), S.

aureus (4.5%). Isolates of H. influenzae (4.5%), M. tuberculosis (4%), S. non-haemolyticus (2%), and Coagulase-Negative Staphylococci (1%) were identified as mixed pathogens. Of all identified

bacteria, 44 were MDR organisms (22%), of which A. baumannii demonstrated to be the most prevalent pathogen among MDR isolates (6.4%) (Table 5.2). Ciprofloxacin and amoxicillin/clavulanic had the lowest potential efficacy of antibiotics against MDR organisms (Figure 5.1). In general, with reference to all pathogens (n=181), the third-generation cephalosporins had fair

sensitivity at 67.4%, 70.2%, 70.7%, and 82.3% for cefotaxime, ceftriaxone, ceftazidime, cefoperazone

respectively. Vancomycin appeared susceptible to all GPB. Likewise, among GNB, susceptibility

was 84.2% for amikacin and 78.9% for gentamicin (Table 5.3).

Chapter 5

105

Figure 5.1 Resistance, intermediate, and sensitivity rates of multidrug-resistant agents causing hospitalized community-acquired pneumonia.

The impact of MDR infections on clinical manifestationA total of 181 patients satisfied the study criteria. Patients were predominantly male (64.6%) with

a mean age of 56.5 years. Predominant complaints during hospital admission were dyspnea

(98.3%) and fever (96.1%). Another common clinical presentation was cough and chest discomfort,

documented at 73.5% and 21%, respectively. The most common comorbidity was diabetes

mellitus (28.2%) followed by neoplasm (25.4%), cardiovascular disease (11.6%), renal insufficiency

(17.1%) and hepatic disorder (7.2%) (Table 5.4).

Within non-MDR infections, most patients clinically manifested with PSI class III (49.6%). In contrast, patients with MDR infections were mostly in PSI class IV (43.2%). Of 44 patients with MDR, 22.7% needed intensive care, which was a significantly higher proportion than those with non-MDR (13.1%). Also, the most common antibiotics for empirical treatment either as single or combined use were ceftriaxone (49.2%), ceftazidime (39.8%), and levofloxacin (27.6%). The use of an empirical antibiotic combination was higher in patients with MDR (34.1%) compared to non-MDR infections (10.9%).

Bivariate comparisons of patient characteristics and the clinical outcomes between non-MDR and MDR infections are presented in Table 5.4. The clinical characteristics and clinical

outcomes were significantly different with respect to neoplasm (17.5% vs 50%), DM (24.1% vs 40.9%),

PSI class I to V (p-value=0.003), day-3 improvement (55.5% vs 11.4%) and 14-day mortality (21.9%

vs 26.8%). The median duration of hospitalization between the two groups was not significantly

different (11.5 vs 12.6 d).

Multidrug-resistant infections among hospitalized-CAP

5

106

Table 5.2 Etiology characteristics (n=202 isolates)

Bacterial agents N Percentage (%) Blood culture

Sputum culture MDR-CAP

Single-agentA. baumannii 27 13.4 27 13Enterobacter spp 10 5.0 10 3E. coli 10 5.0 10 1K. pneumoniae 25 12.4 25 9P. aeruginosa 18 8.9 18 8S. aureus 9 4.5 9 2S. non-haemolyticus 4 2.0 4S. pneumoniae 26 12.9 26 1S. viridans 31 15.3 31Mixed-agentsA. baumannii 1 0.5 1

+ M. tuberculosis 1 0.5 1Enterobacter spp 1 0.5 1 1

+ H. influenzae 1 0.5 1K. pneumoniae 1 0.5 1 1

+ M. tuberculosis 1 0.5 1K. pneumoniae 1 0.5 1 1

+ H. influenzae 1 0.5 1P. aeruginosa 1 0.5 1 1

+ Pantoe agglomerans 1 0.5 1S. pneumoniae 1 0.5 1

+ Cronobacter sakazakii 1 0.5 1S. pneumoniae 4 2.0 4

+ H. influenzae 4 2.0 4S. pneumoniae 4 2.0 4 1

+ M. tuberculosis 4 2.0 4S. pneumoniae 2 1.0 2

+ Staphyloccus spp (coagulase negative) 2 1.0 2S. viridans 2 1.0 2 1

+ M. tuberculosis 2 1.0 2S. viridans 3 1.5 3 1

+ H. influenzae 3 1.5 3

Chapter 5

107

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(50.

0)6

(60.

0)4

(40.

0)4

(40.

0)2

(20.

0)1

(10.

0)3

(30.

0)1

(10.

0)2

(20.

0)3

(30.

0)2

(20.

0)1

(10.

0)0

1(1

0.0)

NA

Ente

roba

cter

. spp

(n=1

1)S

10 (90.

9)9

(81.

8)2

(18.

2)2

(18.

2)5

(45.

5)7

(63.

6)2

(18.

2)8

(72.

7)7

(63.

6)8

(72.

7)10 (90.

9)6

(54.

5)7

(63.

6)10 (90.

9)4

(36.

4)7

(63.

6)10 (90.

9)N

A

I0

00

03

(27.3

)1 (9.1)

02

(18.

2)0

00

1 (9.1)

1 (9.1)

05

(45.

5)2

(18.

2)0

NA

R1 (9.1)

2(1

8.2)

9(8

1.8)

9(8

1.8)

3(2

7.3)

3(2

7.3)

9(8

1.8)

1 (9.1)

4(3

6.4)

3(2

7.3)

1 (9.1)

4(3

6.4)

3(2

7.3)

1 (9.1)

2(1

8.2)

2(1

8.2)

1 (9.1)

NA

K. p

neum

onia

e (n

=27)

S25 (92.

6)22 (81.5

)7

(25.

9)7

(25.

9)6

(22.

2)16 (59.

3)11

(40.

7)17 (63.

0)18 (66.

7)18 (66.

7)22 (81.5

)21 (77.8

)8

(29.

6)21 (77.8

)17 (63.

0)19 (70.

4)22 (81.5

)N

A

I0

01 (3.7

)1 (3.7

)7

(25.

9)1 (3.7

)2 (7.4

)2 (7.4

)0

01 (3.7

)3

(11.1)

2 (7.4

)0

7(2

5.9)

3(11

.1)1 (3.7

)N

A

R2(

7.4)

5(1

8.5)

19 (70.

4)19 (70.

4)14 (51.9

)10 (37.0

)14 (51.9

)8

(29.

6)9

(33.

3)9

(33.

3)4

(14.

8)3

(11.1)

17 (63.

0)6

(22.

2)3

(11.1)

5(1

8.5)

4(1

4.8)

NA

P. ae

rugi

nosa

(n=1

9)S

14 (73.

7)13 (68.

4)2

(10.

5)4

(21.1

)5

(26.

3)9

(47.4

)6

(31.6

)16 (84.

2)11 (57.9

)10 (52.

6)15 (78.

9)8

(42.1

)7

(36.

8)13 (68.

4)9

(47.4

)12 (63.

2)17 (89.

5)N

A

I0

00

05

(26.

3)2

(10.

5)3

(15.

8)1 (5.3

)0

1 (5.3

)0

1 (5.3

)3

(15.

8)0

7(3

6.8)

3(1

5.8)

0N

A

R5

(26.

3)6

(31.6

)17 (89.

5)17 (89.

5)9

(47.4

)8

(42.1

)10 (52.

6)2

(10.

5)8

(42.1

)8

(42.1

)4

(21.1

)10 (52.

6)9

(47.4

)6

(31.6

)3

(15.

8)4

(21.1

)2

(10.

5)N

A

S. a

ureu

s (n=

9)S

NA

NA

5(5

5.6)

7(7

7.8)

8(8

8.9)

5(5

5.6)

8(8

8.9)

7(7

7.8)

6(6

6.7)

6(6

6.7)

7(7

7.8)

8(8

8.9)

1(11

.1)7

(77.8

)7

(77.8

)5

(55.

6)5

(55.

6)9

(100

)I

NA

NA

1(11

.1)0

01

(11.1)

00

1(11

.1)2

(22.

2)0

03

(33.

3)1

(11.1)

2(2

2.2)

3(3

3.3)

3(3

3.3)

0

RN

AN

A3

(33.

3)2

(22.

2)1

(11.1)

3(3

3.3)

1(11

.1)2

(22.

2)2

(22.

2)1

(11.1)

2(2

2.2)

1(11

.1)5

(55.

6)1

(11.1)

01

(11.1)

1(11

.1)0

Multidrug-resistant infections among hospitalized-CAP

5

108

Bact

eria

AM

K,

n(%

)G

EN,

n(%

)A

MC,

n(

%)

SAM

, n(

%)

TIC,

n(%

)PI

P,

n(%

) CF

Z,

n(%

)CA

Z,

n(%

)CT

X,

n(%

)CR

O,

n(%

)CF

P,

n(%

)SX

T,

n(%

)CI

P,

n(%

)LV

X,

n(%

)M

XF,

n(%

)IP

M,

n(%

)M

EM,

n(%

)VA

N,

n(%

)S.

pne

umon

iae

(n=3

7)S

NA

NA

26 (70.

3)26 (70.

3)27 (73.

0)30 (81.1

)26 (70.

3)26 (70.

3)28 (75.

7)30 (81.1

)26 (70.

3)28 (75.

7)27 (73.

0)30 (81.1

)27 (73.

0)32 (86.

5)35 (94.

6)9

(100

)I

NA

NA

2 (5.3

)2 (5.3

)4

(10.

8)2 (5.4

)2 (5.3

)3 (8.1)

3 (8.1)

2 (5.4

)4

(10.

8)0

2 (5.4

)0

4(1

0.8)

2 (5.3

)2 (5.3

)0

RN

AN

A9

(24.

3)9

(24.

3)6

(16.

2)5

(13.

3)9

(24.

3)8

(21.6

)6

(16.

2)5

(13.

5)7

(18.

9)9

(24.

3)8

(21.6

)7

(18.

9)6

(16.

2)3 (8.1)

00

S. vi

ridan

s (n=

36)

SN

AN

A15 (41.7

)20 (55.

6)28 (77.8

)27 (75.

0)26 (72.

2)27 (75.

0)27 (75.

0)29 (80.

6)30 (83.

3)25 (69.

4)23 (63.

9)30 (83.

3)22 (61.1

)29 (80.

6)30 (83.

3)9

(100

)I

NA

NA

8(2

2.2)

8(2

2.2)

4(11

.1)4

(11.1)

8(2

2.2)

6(1

6.7)

5(1

3.9)

4(11

.1)4

(11.1)

1 (2.8

)7

(19.

4)0

6(1

6.7)

4(11

.1)4

(11.1)

0

RN

AN

A13 (36.1

)8

(22.

2)4

(11.1)

5(1

3.9)

2 (5.6

)3 (8.3

)4

(11.1)

3 (8.3

)2 (5.6

)10 (27.8

)6

(16.

7)6

(16.

7)8

(22.

2)3 (8.3

)2 (5.6

)0

All o

rgan

isms (

n=18

1)S

80(8

4.2)

a75

(78.

9) a

66 (36.

5)86 (47.5

)90 (49.

7)11

6(6

4.1)

95 (52.

5)12

8(7

0.7)

122

(67.4

)12

7(7

0.2)

149

(82.

3)12

3(6

8.0)

85 (47.0

)13

7(7

5.7)

108

(59.

7)12

6(6

9.6)

149

(82.

3)86 (100

)b

I0 a

1(1

.1) a

16 (8.8

)15 (8.3

)33 (18.

2)14 (7.7

)22 (12.

2)16 (8.8

)12 (6.6

)11 (6.1)

9 (5.0

)8 (4.4

)28 (15.

5)2 (1.1)

47 (26.

0)27 (14.

9)15 (8.3

)0 b

R15

(15.

8) a

19(2

0.0)

a99 (54.

7)80 (44.

2)58 (32.

0)51 (28.

2)64 (35.

4)37 (20.

4)47 (26.

0)43 (23.

8)23 (12.

7)50 (27.6

)68 (37.6

)42 (23.

2)26 (14.

4)28 (15.

5)17 (9.4

)0 b

Not

es: a Am

ong

95 G

ram

-neg

ativ

e iso

late

s inc

ludi

ng A

. bau

man

nii, E

. col

i, Ent

erob

acte

r. sp

p, K

. pne

umon

iae,

and

P. ae

rugi

nosa

b Am

ong

86 G

ram

-pos

itive

isol

ates

incl

udin

g S.

aur

eus,

S. p

neum

onia

e, S.

virid

ans,

and

Stap

hylo

cocc

us n

on-h

aem

olyt

icus

Abb

revi

atio

ns: A

MC:

am

oxic

illin

-cla

vula

nate

; AM

K: a

mik

acin

; CAZ

: cef

tazi

dim

e; C

FP: c

efop

eraz

one;

CFZ

: cef

azol

in; C

IP: c

ipro

floxa

cin;

CRO

: cef

tria

xone

; CTX

: cef

otax

ime;

G

EN: g

enta

myc

in; I

: int

erm

edia

te; I

PM: i

mip

enem

; LVX

: lev

oflox

acin

; MEM

: mer

open

em; M

XF: m

oxifl

oxac

in; N

A: n

ot a

pplic

able

; PEN

: Pen

icill

in; P

IP: p

iper

acill

in-t

azob

acta

m; R

: re

sista

nt; S

: sen

sitiv

e; S

AM: a

mpi

cilli

n-su

lbac

tam

; SXT

: trim

etho

prim

-sul

fam

etho

xazo

le; T

IC: t

icar

cilli

n-cl

avul

anat

e; V

AN: v

anco

myc

in

Chapter 5

109

Table 5.4 Comparisons of clinical characteristics between non-MDR and MDR

Clinical characteristics All patients (n=181)

Non-MDR (n=137)

MDR (n=44) p-value

GenderMale, n (%) 117(64.6) 84(61.3) 33(75.0) 0.099Female, n (%) 64(35.4) 53(38.7) 11(25.0)Age (years), mean (SD) 56.5(12.8)Chief complaints at hospital admissionFever, n (%) 174(96.1) 130(94.9) 44(100.0) 0.137Cough, n (%) 133(73.5) 100(75.2) 33(24.8) 0.793Dyspnea, n (%) 178(98.3) 134(97.8) 44(100.0) 0.431Chest discomfort, n (%) 38(21.0) 26(19.0) 12(27.3) 0.240RR (/min), median (P

25-P

75) 26(22-28) 24(22-28) 26(22.5-28) 0.210

Body temperature (oC), median (P

25-P

75) 37.0(36.7-37.8) 37.0(36.7-37.8) 37.0(36.7-37.7) 0.756

Blood leucocytes (per mm3), median (P

25-P

75) 14,865(11,450-18,650) 15,000(11,500-18,200) 14,075(11,155-19,700) 0.750

SBP (mmHg), median (P25

-P75

) 120(110-130) 110(110-130) 120(110-140) 0.253DBP (mmHg), median (P

25-P

75) 70(70-80) 70(70-80) 75(70-80) 0.929

Arterial blood gaspH, median (P

25-P

75) 7.44(7.40-7.49) 7.44(7.40-7.49) 7.43(7.39-7.50) 0.721

pCO2 (mmHg), median (P

25-P

75) 36.0(31.0-45.7) 37.0(31.0-47.0) 35.0(30.6-39.6) 0.149

pO2 (mmHg), median (P

25-P

75) 76.1(67.0-98.4) 78.0(68.0-101.5) 76.0(61.0-95.7) 0.152

Base excess, median (P25

-P75

) 1.1(-2.0-5.8) 1.8(-1.8 to 6.0) 0.3(-3.3 to 4.5) 0.192HCO

3, median (P

25-P

75) 25.2(22.2-30.3) 25.7(22.4-30.5) 24.9(21.7-28.6) 0.292

SO2, median (P

25-P

75) 96.0(94.0-98.1) 96.0(94.0-98.1) 96.3(92.2-98.3) 0.509

Pleural effusion, n (%) 26(14.4) 19(13.9) 7(15.9) 0.737Co-morbiditiesCardiovascular diseases, n (%) 21(11.6) 15(10.9) 6(13.6) 0.628Neoplasm, n (%) 46(25.4) 24(17.5) 22(50.0) <0.001*Diabetes mellitus, n (%) 51(28.2) 33(24.1) 18(40.9) 0.031*Hepatic disorder, n (%) 13(7.2) 9(6.6) 4(9.1) 0.392Renal insufficiency, n (%) 31(17.1) 22(16.1) 9(20.5) 0.501PSI classClass I, n (%) 14(7.7) 13(9.5) 1(2.3) 0.003*Class II, n (%) 22(12.2) 20(14.6) 2(4.5)Class III, n (%) 84(46.4) 68(49.6) 16(36.4)Class IV, n (%) 48(26.5) 29(21.2) 19(43.2)Class V, n (%) 13(7.2) 7(5.1) 6(13.6)Intensive care 28(15.5) 18(64.3) 10(22.7) 0.126Empirical antibioticsCeftazidime, n (%) 56(30.9) 44(32.1) 12(27.3) 0.506Ceftriaxone, n (%) 75(41.4) 63(46.0) 12(27.3)Levofloxacin, n (%) 20(11.0) 15(10.9) 5(11.4)Ceftazidime + levofloxacin, n (%) 16(8.8) 9(6.6) 7(15.9)Ceftriaxone + levofloxacin, n (%) 14(7.7) 6(4.4) 8(18.2)Clinical follow-upLength of stay, median (P

25-P

75) 12.0(8.0-16.0) 11.5(8.0-15.8) 12.6(9.0-16.4) 0.374

Day-3 improvement, n (%) 81(44.8) 76(55.5) 5(11.4) <0.001*14-day mortality rates, n (%) 55(30.4) 30(21.9) 25(56.8) <0.001*

Notes: *statistically significant, p < 0.05Abbreviations: DBP: diastolic blood pressure; max: maximum; med: median; min: minimum; MDR: multidrug-resistant; PSI: pneumonia severity index; RR: respiratory rate; SBP: systolic blood pressure

Multidrug-resistant infections among hospitalized-CAP

5

110

The risk factors of mortalityMultivariate analysis of variables considered relevant to the outcome of 14-day mortality is

presented in Table 5.5. Among patient factors, patients with neoplasm (OR=2.76; 95%CI=1.03-

7.36) and those with PSI class III or above (OR=9.19; 95%CI=1.51-55.89) had a significantly increased

risk of mortality. Clinical improvement at day-3 appeared to provide protection, with decreased

mortality; OR=0.06; 95%CI=0.02-0.19.

DISCUSSION

Our study suggests that CAP in the study area is not only caused by GPB but also frequently by GNB.

The pathogens generally remained sensitive to third-generation cephalosporins which are also

recommended by the local guideline. Microbiological culturing of sputum and blood provided

clinically relevant information concerning the identity of pathogens with their susceptibility to

antimicrobials. Clearly, amoxicillin and penicillin even if combined with a beta-lactamase inhibitor

are no longer effective in our setting. Our results support a strategy to avoid these agents for

patients admitted to hospital with CAP, particularly in LMICs. Empirical treatment for CAP should

indeed be guided by culture data that are locally obtained and susceptibility testing.27,28

In our study, S. pneumoniae was the most common pathogen, with conserved penicillin

susceptibility. A study on S. pneumoniae infections in 13 Asian countries reported that the incidence

of the pathogen was high at 29.2% among CAP in Pan-Asia.29 Mixed pathogens are an important

consideration since they may lead to delayed response or even a lack of clinical improvement.

Like the systematic review conducted on studies in Asia, our findings also revealed mixed

infections with S. pneumoniae and M. tuberculosis or H. influenzae.30,31 In contrast to community-

acquired viridans streptococcal pneumonia, our study pointed out that the organism had low

sensitivity to amoxicillin/clavulanate acid. The mechanism of resistance to penicillin among S.

viridans isolates seems to be through alteration of the penicillin-binding proteins (PBPs), especially

among patients with underlying diseases.32 The change on the site of PBPs generates inadequate

binding not only for penicillin but also for other β-lactams including cephalosporins.33,34 S. viridans

organisms in our study might also represent normal microbial flora as colonization in the upper-

respiratory tract.35,36 However, invading to lower-respiratory tract or bloodstream, S. viridans could

lead to serious infections. In previous clinical reports, S. viridans could cause complications of

parapneumonic effusion or empyema in patients with CAP.37–39 One of the important organisms

commonly encountered among those causing pneumonia is Methicillin-resistant Staphylococcus

aureus (MRSA).40 Community-acquired MRSA (CA-MRSA) has emerged as an important pathogen

for CAP. In several hospitals in Indonesia, an identification test of MRSA for pneumonia patients has

not been routinely conducted considering the cost and the results of a previous study reporting

the low prevalence of CA-MRSA among patients admitted to the hospital.41

We identified A. baumannii as a causative agent for CAP with high antimicrobial resistance.

GNB has been determined as the dominant pathogen causing CAP in Indonesia and other

Chapter 5

111

countries of Asia.30,31,42 Outbreaks of A. baumannii are currently responsible for community and

nosocomial-infectious diseases such as in South Asia where the species has been observed

as a cause of pneumonia since 1989.43 Acinetobacter species are commonly encountered as

colonizing organisms in the upper-respiratory and gastrointestinal tracts.44 Therefore, MDR

Acinetobacter is problematic, especially in immunocompromised hosts. Of 28 Acinetobacter

infections in our study, around 60% were highly resistant to ciprofloxacin. Similarly, the results

from a previous study investigated the resistance mechanism of 75 Acinetobacter species from

Walter Reed Army Medical Center (WRAMC). Among the respiratory specimens, 80% of isolates

were identified as being resistant to ciprofloxacin and cefepime.45 In addition, we found that E. coli

had poor sensitivity to penicillins. Most of the isolates were highly sensitive to third-generation

cephalosporins, fluoroquinolones, and carbapenems. A previous study in Indonesia found that

8% of E. coli were resistant to ciprofloxacin commonly through independent selection among

resistant mutants.46 Notably, K. pneumoniae presented as the highest prevalent GNB in 7 Asian

countries with a low resistance rate to cefuroxime and ceftriaxone.47 K. pneumoniae in Indonesia

should be considered as a threat for potential outbreaks as 15% of adults, and 7% of children

tested carried this organism.48 Previous evidence regarding CAP etiology in Semarang, the sixth

biggest city in Indonesia, has reported results in line with this study. The study found that the

prevalence of K. pneumonia was the most commonly identified among bacteria causing CAP.

MDR K. pneumoniae, E. coli, and Enterobacter spp. expressed extended-spectrum b-lactamases (ESBLs). These enzymes inactivate penicillins and cephalosporins leading to limited treatment options with currently available antimicrobial agents.24

Multidrug-resistant infections among hospitalized-CAP

5

112

Tabl

e 5.

5 M

ultiv

aria

te a

naly

sis o

f ris

k fa

ctor

s for

mor

talit

y am

ong

CAP

patie

nts

Varia

ble

CAP

Mor

talit

yU

niva

riate

ana

lysi

sM

ultiv

aria

te a

naly

sis

No

(n=1

26)

Yes (

n=55

)O

R95

%CI

p-va

lue

aOR

95%

CIp-

valu

eH

ost f

acto

rsM

ale

82(6

5.1)

35(6

3.6)

0.93

90.

485-

1.817

0.85

20.

483

0.190

-1.2

290.1

27Ag

e>60

40(3

1.7)

26(4

7.3)

1.928

1.008

-3.6

880.

047

1.482

0.58

5-3.

751

0.40

7Ca

rdio

vasc

ular

dise

ase

9(7.1

)12

(21.8

)3.

628

1.428

-9.21

60.

007

2.40

10.

684-

8.42

20.1

71N

eopl

asm

21(1

6.7)

25(4

5.5)

4.167

2.05

3-8.

458

<0.0

012.

755*

1.031

-7.3

610.

043

Dia

bete

s mel

litus

24(1

9.0)

27(4

9.1)

4.09

82.

054-

8.177

<0.0

012.

098

0.78

0-5.

642

0.142

Live

r dise

ase

7(5.

6)6(

10.9)

2.08

20.

666-

6.50

90.

208

3.80

00.

633-

22.8

100.1

44Re

nal i

nsuffi

cien

cy18

(14.

3)13

(23.

6)1.8

570.

837-

4.123

0.128

1.917

0.59

2-6.

201

0.27

7PS

I cla

ss >

392

(73.

0)53

(96.

4)9.7

932.

262-

42.4

070.

002

9.188

*1.5

10-5

5.89

10.

016

Day

-3 im

prov

emen

t77

(61.1

)4(

7.3)

0.05

00.

017-

0.147

<0.0

010.

055*

0.01

6-0.1

90<0

.001

Age

nt fa

ctor

MD

R-ba

cter

ial i

nfec

tions

19(1

5.1)

25(4

5.5)

4.69

32.

282-

9.65

1<0

.001

1.259

0.47

1-3.

361

0.64

6Tr

eatm

ent f

acto

rAn

tibio

tic c

ombi

natio

n14

(11.1)

16(2

9.1)

3.28

21.4

68-7

.338

0.00

42.

424

0.71

7-8.1

960.1

54

Not

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113

The clinical relevance of GNB findings from respiratory specimens among pneumonia patients is

usually debated as it might reflect colonization rather than pulmonary infection. Low awareness of

infection prevention and high transmission between patients and the community is challenging

in LMICs. The prevalence of GNB is lower in some regions especially in Europe, the US, and Canada

except in the context of hospital-acquired pneumonia;49–51 notably different from reports from

Asian countries, as reflected by recommendations made by the ATS and BTS in their respective

guidelines.3,5,47

The crucial concern of CAP management in most guidelines is P. aeruginosa infection, which

carries a poor prognosis and high mortality.2,3,5,20 In our study, P. aeruginosa remained sensitive to

anti-pseudomonal β-lactam antibiotics such as ceftazidime and cefoperazone. Comparing our

results with other LMICs, our findings were similar to a Nigerian study on 232 pneumonia patients

with 77% and 75.5% having isolates sensitive to ceftazidime and levofloxacin, respectively.52 For

Egypt, a study on CAP revealed that P. aeruginosa had the highest resistance to levofloxacin (56.5%)

followed by ciprofloxacin and piperacillin/tazobactam which rated at 47.8%.53

Malignancy as an underlying disease was earlier reported to be associated with high mortality

(27%) among CAP patients.54 Neoplastic disease is scored +30 in the PSI scoring system.55 A

prediction value of PSI has been used widely to estimate mortality. PSI class III or above indicates

that the risk of death is high, and the patients need hospitalization. We used PSI categorization

since this system includes 19 comprehensive aspects. According to ATS/IDSA guidelines, patients

started on empirical antimicrobial therapy who show clinical improvement within the first three

days could safely be switched from intravenous to oral antibiotics.3,56 In our study, we explored

whether the day-3 evaluation would be a critical time point to evaluate the efficacy of empirical

treatment and to estimate patients’ risk of mortality. An assessment of clinical response at day

4 of patients with community-acquired bacterial pneumonia (CABP) was also suggested by the

Food and Drug Administration (FDA) guidance.57 Inline, our findings recommend a combination

assessment of clinical response in the first three days as an additional value to PSI scoring where

both assessments were investigated as independent risk factors for mortality among patients

with pneumonia. Moreover, the successful treatment response to empirical treatment could help

to switch to oral antimicrobial treatment on day 3, with additional information that will then be

available from culture and susceptibility data from the Microbiology Laboratory.

Despite the results obtained in the study, there were several limitations. First, only patients

with a positive culture were included. Thus the results may not be representative for all patients

especially those in whom culturing was either not tried, or failed to yield causative organisms.

Second, we did not include antibiotics given after culture results became available especially in

critically ill patients where the selection of antimicrobial drugs and the dosages may have impacted

the clinical outcomes, including mortality. Third, we conducted the study at a single center, albeit

a large hospital in Indonesia; extrapolation of our results needs confirmation in other centers on

Java or even Indonesia and beyond. Also, it generated the small size of isolates tested to develop

the antimicrobial susceptibility pattern. However, the data is absolutely needed for clinician in the

use of empirical therapy of antibiotics in CAP that admitted to the hospital. Forth, our exclusion

Multidrug-resistant infections among hospitalized-CAP

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of patients who died within 24 hours might have caused bias, with the most severely ill patients

potentially having an early fatal outcome. Notwithstanding, in the absence of a specified and

verified diagnosis, valid inclusion seemed impossible. A further limitation of our study concerns

the fact that it is not impossible that some CAP diagnoses were misclassified hospital-/ventilator-

acquired pneumonia. Given the involvement of the pulmonologist in specifying and verifying the

diagnosis in an early stage, we do not expect many (or even any) misclassifications in this respect.

However, the study provides updated information about the local pattern of resistance to

antimicrobials among MDR-CAP. The presence of MDR organisms in the community is an indicator

of the complex hindrances faced in the implementation of the national health system. Besides

the high transmission of pathogens in the tropical environment, free access to antibiotics in the

community among LMICs could be the main cause of MDR.

The study supports the notion that the use of antibiotics in the community urgently needs

to be restricted to control the emergence of further resistance. Private sectors and governments

need to monitor the pattern of pathogens and the resistance to antibiotics regularly. Our report

adds important information needed to select empirical antimicrobial treatment for CAP, including

the coverage of GNB infections for LMICs like Indonesia.

CONCLUSIONS

S. pneumoniae was the predominant pathogen of hospitalized CAP. GNB were common as well,

and these organisms should likewise be considered and covered in empirical treatment. A.

baumannii and K. pneumoniae were common and carried a high risk for MDR-CAP. Concerning

the implementation of the local guideline where β-lactam antibiotics are used for empirical

treatments in CAP patients, the pathogens generally remain highly susceptible to the third-

generation cephalosporins. Rapid and advanced microbiological diagnostics are required to

monitor further drug resistance emergence and to ensure that empirical therapy remains effective

for CAP. This data should be incorporated in the design for local guidelines for the empirical

treatment of CAP. Eventually, we recommend assessing clinical response to therapy within the

first three days follow up as this has an important prognostic value that adds to the PSI scoring

system and microbiological evaluation.

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43. Krisanapan S, Naphathorn P, Kaewprom P. Community acquired Acinetobacter pneumonia: report of two cases. Southeast Asian J Trop Med Public Health. 1989;20(3):497-498.

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45. Hujer KM, Hujer AM, Hulten EA, et al. Analysis of antibiotic resistance genes in multidrug-resistant Acinetobacter sp. isolates from military and civilian patients treated at the Walter Reed Army Medical Center. Antimicrob Agents Chemother. 2006;50(12):4114-4123. doi:10.1128/AAC.00778-06

46. Kuntaman K, Lestari ES, Severin JA, et al. Fluoroquinolone-resistant Escherichia coli, Indonesia. Emerg Infect Dis. 2005;11(9):1363-1369. doi:10.3201/eid1109.041207

47. Song JH, Oh WS, Kang CI, et al. Epidemiology and clinical outcomes of community-acquired pneumonia in adult patients in Asian countries: a prospective study by the Asian network for surveillance of resistant pathogens. Int J Antimicrob Agents. 2008;31(2):107-114. doi:10.1016/j.ijantimicag.2007.09.014

48. Farida H, Severin JA, Gasem MH, et al. Nasopharyngeal carriage of Klebsiella pneumoniae and other Gram-negative bacilli in pneumonia-prone age groups in Semarang, Indonesia. J Clin Microbiol. 2013;51(5):1614-1616. doi:10.1128/JCM.00589-13

49. Hyllienmark P, Martling C-R, Struwe J, Petersson J. Pathogens in the lower respiratory tract of intensive care unit patients: impact of duration of hospital care and mechanical ventilation. Scand J Infect Dis. 2012;44(6):444-452. doi:10.3109/00365548.2011.645504

50. Papan C, Meyer-Buehn M, Laniado G, Nicolai T, Griese M, Huebner J. Assessment of the multiplex PCR-based assay Unyvero pneumonia application for detection of bacterial pathogens and antibiotic resistance genes in children and neonates. Infection. 2018;46(2):189-196. doi:10.1007/s15010-017-1088-y

51. Verhamme KMC, De Coster W, De Roo L, et al. Pathogens in early-onset and late-onset intensive care unit-acquired pneumonia. Infect Control Hosp Epidemiol. 2007;28(4):389-397. doi:10.1086/511702

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53. Resistance D. Community acquired pneumonia among adult patients at an Egyptian university hospital : bacterial etiology , susceptibility profile and evaluation of the response to initial empiric antibiotic therapy. 2018:2141-2150.

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55. Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ KW. A prediction rule to identify low-risk patients with community- acquired pneumonia. N Engl J Med. 1997:243-250.

56. Ramirez JA, Srinath L, Ahkee S, Huang A, Raff MJ. Early switch from intravenous to oral cephalosporins in the treatment of hospitalized patients with community-acquired pneumonia. Arch Intern Med. 1995;155(12):1273-1276.

57. Eckburg PB, Friedland HD, Llorens L, et al. Day 4 Clinical Response of Ceftaroline Fosamil Versus Ceftriaxone for Community-Acquired Bacterial Pneumonia. 2012;20(4):254-260.

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CHAPTER 6Cost-effectiveness of culture-based

versus empirical antibiotic treatments for hospitalized adults with community-

acquired pneumonia in Indonesia: A real-world patient-database study

Abdul Khairul Rizki Purba

Purwantyastuti Ascobat

Armen Muchtar

Laksmi Wulandari

Jan-Willem Dik

Annette d’Arqom

Maarten J. Postma

Clinicoecon Outcomes Res, 2019(11): 729-739. doi: 10.2147/CEOR.S224619.

120

ABSTRACT

Objectives: This study analyzes the cost-effectiveness of culture-based treatment (CBT) versus

empirical treatment (ET) as a guide to antibiotic selection and uses in hospitalized patients with

community-acquired pneumonia (CAP).

Patients and methods: A model was developed from the individual patient data of adults with

CAP hospitalized at an academic hospital in Indonesia between 2014 and 2017 (ICD-10 J.18x). The

directed antibiotic was assessed based on microbiological culture results in terms of the impact on

hospital costs and life expectancy (LE). We conducted subgroup analyses for implementing CBT

and ET in adults under 60 years, elderly patients (≥ 60 years), moderate-severe CAP (PSI class III-V)

cases, and ICU patients. The model was designed with a lifetime horizon and adjusted patients’

ages to the average LE of the Indonesian population with a 3% discount each for cost and LE. We

applied a sensitivity analyses on 1,000 simulation cohorts to examine the economic acceptability

of CBT in practice. Willingness to pay (WTP) was defined as 1 or 3 times the Indonesian GDP per

capita (US$ 3,570).

Results: CBT would effectively increase the patients’ LE and be cost-saving (dominant) as well.

The ET group’s hospitalization cost had the greatest influence on economic outcomes. Subgroup

analyses showed that CBT’s dominance remained for Indonesian patients aged under 60 years or

older, patients with moderate-severe CAP, and patients in the ICU. Acceptability rates of CBT over

ET were 74.9% for 1xWTP and 82.8% for 3xWTP in the base case.

Conclusions: Both sputum and blood cultures provide advantages for cost-saving and LE gains

for hospitalized patients with CAP. CBT is cost-effective in patients of all ages, PSI class III or above

patients, and ICU patients.

Keywords: microbiological culture, empirical treatment, life expectancy, cost-effectiveness,

community-acquired pneumonia

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INTRODUCTION

Community-acquired pneumonia (CAP) is an infectious disease with a clinical severity ranging

from mild to life-threatening. CAP has high mortality rates and is costly to treat.1 In the US in

2012, the total direct medical costs related to CAP were estimated at US$ 7,220 to US$ 11,443.2 In

European countries, the cost was estimated to be between €1,201 and €1,586 per patient. These

high costs have been partially attributed to multiple antibiotic resistance.3

Established international guidelines of the British Thoracic Society (BTS), American Thoracic

Society (ATS), and National Institute for Health and Care Excellence (NICE) concur that empirical

treatment of CAP with antibiotics is urgent considering the elevated mortality rates, the culture

timing, and unspecified results from clinical and radiology methods for determining bacterial

infections.4–6 Additionally, abstaining from microbiological evaluation in clinical practices for

empirical treatment of CAP patients potentially leads to antibiotic resistance and overuse,

which indirectly contributes to hospitalization costs.7–9 At present, the implementation of rapid,

advanced molecular microbiology methods for CAP diagnostics has largely been developed.10

The extended use of these promising novel approaches, however, remains unavailable and costly

in low-middle income countries (LMICs), which carry higher burdens of the disease. Controversies

surrounding the recommendation for the use of microbiology tests in mild-moderate hospitalized

cases and outpatient CAP management have been described by previous research since clinical

improvement could be achieved by empirical antibiotics alone.11,12 The evaluation of sputum

cultures from respiratory samples is recommended for inpatient CAP treatment, aiming to select

appropriate antibiotics and prevent further antibiotic resistance.4,6,13

When immediately employed, early directed therapy after evaluating culture results is an

essential strategy to prevent high rates of antibiotic resistance. The implementation of culture

analysis in developing countries with a high prevalence of CAP, such as Indonesia, should

be considered alongside the assessment of culture analysis impact on costs and patient life

expectancy (LE). Since 2014, Indonesia has employed a new universal health coverage program,

which the health ministry contracted to a single public insurance company (Badan Penyelenggara

Jaminan Sosial or BPJS). The universal health coverage also involves the implementation of a

national social security system in managing expenditure related to reimbursement and costing

in the national healthcare system, inclusive of cost-effectiveness considerations. Hitherto, the

lack of evidence for cost-effectiveness has hampered the implementation of culture analysis as

a diagnostic tool guiding the management of CAP. Therefore, the culture analysis approach has

rarely been adopted in hospitals or in communities. Considering the current limitations of the

evidence regarding the value of culture analysis prior to empirical-based antibiotic administration

in CAP patients, we performed a cost-effectiveness analysis on the implementation of culture-

based treatment (CBT) compared to empirical treatment only (ET) in hospitalized CAP patients.

Cost-effectiveness of culture-based treatment

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MATERIAL AND METHODS

Decision-tree model overviewA decision-tree model was designed from individual patient data of hospitalized CAP patients in

Indonesia. The model is shown in Figure 6.1. We built the model based on a payer perspective.

The decision-tree compared two options: (i) patients with empirical antibiotic treatment (ET)

during hospitalization and (ii) patients with CBT for whom medications were adjusted based

on microbiological evaluation in terms of the culture and antimicrobial susceptibility tests. The

culture analysis was conducted on samples from respiratory tracts (sputum) and blood draws. One

package was produced for positive cultures demonstrating antimicrobial susceptibility after the

identification of the microorganisms. We assumed that the level of competencies and advances

of the laboratory staff were equal in handling the specimens and reporting the results. The CBT

group were treated with empirical antibiotics based on the Indonesian guidelines at 24 hours

post-admission. Thereafter, culture results were provided, and the patients were treated with

the directed therapy involving a second round of antibiotics.14 The second round of antibiotics

could be different from the previous empirical treatment (antibiotic change or AC) or be the same

as (no antibiotic change or NC). We included in the AC group patients with negative cultures

with no microorganism growth, for whom the physicians subsequently stopped the use of

antibiotics. In the ET group, we assumed that the treatment using antibiotics was empirical during

hospitalization. The termination states of the decision-tree for both CBT and ET were presented as

recovery or death. Patients with no sputum available for culture tests were assumed to undergo

empirical therapy during hospitalization, and we included such patients in the ET group. As there

was no specific indication to the physician examining the culture of patients’ sputum, we assumed

that the decision to perform ET or CBT was random.

Figure 6.1 Decision-tree model of the culture-based treatment (CBT) and empirical treatment (ET) groups in the management of CAP.

Data inputIndividual patient data were derived from 351 retrospective records of adult patients admitted

on referral to the academic hospital of Dr. Soetomo, Indonesia, from 2014 to 2017. This hospital

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is a central hospital for the eastern part of Indonesia and has roughly 1,514 beds. The Center of

Research and Development of Dr. Soetomo Hospital, under the ethical committee, approved the

study proposal with no. 480/Panke.KKE/X/2014. The committee decided that the study did not

require a review in terms of patient consent since the study was performed with retrospective

observational design. We obtained the clinical data from the Department of Medical Record and

all relevant costs from the Finance Department, Dr. Soetomo Hospital. The data were anonymous

and confidentially. The study met the agreement with Indonesian research conduct and the

Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subject version

2013).15

We included hospitalized CAP cases that had both blood and sputum culture with a pneumonia

severity index (PSI) class from I to V. We input data of patients whose average age was 57.6 years

(min-max: 19-91 years); they were predominantly males (78.3%). We performed subgroup analyses

for the implementation of CBT on patients under 60 and aged ≥60, PSI class III and above, and in

the intensive care unit (ICU). We defined the age of 60 as the breakpoint for the aging population

in Indonesia, according to the United Nations Population Fund (UNFPA 2014).16 We addressed PSI

class III in subgroup analyses since this cutoff increases the risk of 30-day mortality by ten times

compared to class II.17

Calculation of the outcomesWe assessed the LE outcome in our model by adjusting the patients’ ages to the average LE for

the Indonesian population between 2010 and 2015 using data provided by the United Nations

Department of Economic and Social Affairs, Population Division, 2017.18 The average LE for each

group of these ages for men and women is separately presented in Figure 6.2. The model-

time horizon was the lifetime where recovery patients would have LE greater than their ages.

Considering the discount rate launched by the Health Ministry of Indonesia, we applied a 3%

discount each for costs and LE.19

Figure 6.2 Average remaining life expectancy of Indonesian males and females for the patient model (calculated from the United Nations Department of Economic and Social Affairs, Population Division, 2017. World Population Prospects).17

Cost-effectiveness of culture-based treatment

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Hospitalization costs were valued in US$ with 2016 adjustments. The conversion rate to an Indonesia

Rupiah (IDR) was equal to US$ 13,308.33 according to the Organization for Economic Cooperation

and Development (OECD).20 The costs from data were formulated as C, which is calculated from

the average of the individual cost of administration (Ca), pharmacy costs (Cp), laboratory and

radiology (Cl) expenses, cost of bed (Cb), and medical staff costs (Cm). The incremental cost-

effectiveness ratio (ICER) in terms of total hospitalization costs and LE was calculated with the

following Formula 6.1 below. Notably, in case the numerator was negative (cost-saving), and the

denominator was positive (improving LE), then the ICER indicated dominant.

ICER =

138

Department of Economic and Social Affairs, Population Division, 2017.18 The average LE for

each group of these ages for men and women is separately presented in Figure 6.2. The model-

time horizon was the lifetime where recovery patients would have LE greater than their ages.

Considering the discount rate launched by the Health Ministry of Indonesia, we applied a 3%

discount each for costs and LE.19

Figure 6.2 Average remaining life expectancy of Indonesian males and females for the patient model (calculated from the United Nations Department of Economic and Social Affairs, Population Division, 2017. World Population Prospects).17

Hospitalization costs were valued in US$ with 2016 adjustments. The conversion rate

to an Indonesia Rupiah (IDR) was equal to US$ 13,308.33 according to the Organization for

Economic Cooperation and Development (OECD).20 The costs from data were formulated as

C, which is calculated from the average of the individual cost of administration (Ca), pharmacy

costs (Cp), laboratory and radiology (Cl) expenses, cost of bed (Cb), and medical staff costs

(Cm). The incremental cost-effectiveness ratio (ICER) in terms of total hospitalization costs

and LE was calculated with the following Formula 6.1 below. Notably, in case the numerator

was negative (cost-saving), and the denominator was positive (improving LE), then the ICER

indicated dominant.

ICER = ∆[(∑ (%!&%"&%#&%$&%%))(&'!()!*'+('&]

,-

∆[∑ *./(%!#'),%!#'- &∑ *./(2'%!#')

,2'%!#'- ]

Formula 6.1 Incremental cost-effectiveness ratio (ICER) calculation based on Ca, Cp, Cl, Cb,

and Cm

Formula 6.1 Incremental cost-effectiveness ratio (ICER) calculation based on Ca, Cp, Cl, Cb, and Cm

Table 6.1 shows the costs and LE employed gamma and exponential distribution, respectively.

According to the World Health Organization – Choosing Interventions for Cost-Effectiveness

criterion (WHO-CHOICE), a particular intervention is highly cost-effective when the ICER is less

than the willingness-to-pay (WTP), defined as the Indonesian cost-effectiveness threshold of gross

domestic product (GDP) per capita. If the ICER is between 1 and 3 times the GDP per capita, then

the intervention is cost-effective.21 This definition was also applied in a previous study which was

performed in Indonesia.22 For these thresholds, we applied the Indonesian GDP per capita in 2016

at US$ 3,570.23

Sensitivity analysesWe performed one-way tornado sensitivity analyses using TreeAge Pro 2019 to address the

robustness of the model ICERs on the changes of the upper and lower limits of the parameter

values. The parameters included in the test were costs and the probability of death being

averted. The outcome of LE was conclusive as it was based on the data presented by WHO for

the Indonesian population.18 We also conducted random probabilistic sensitivity analyses using a

Monte Carlo simulation with 1,000 random cohorts on the respective corresponding distributions.

We addressed a cost-effectiveness acceptability curve to draw relationships between ICER and

the Indonesian affordability threshold. Indeed a relationship between the probabilistic sensitivity

analysis (PSA) and univariate analysis exists in terms of input. Notably, we used the range for the

input in the PSA and in the univariate analysis.

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Table 6.1 The total hospitalization costs and life-expectancy parameters for the decision-tree modelParameters Mean SD Minimum Maximum DistributionHospitalization costs (US$)AC group

All cases 3,055.36 1,324.61 1,265.10 5,879.22 Gamma (5.320;0.002)Aged < 60 2,868.93 1,210.45 1,256.10 5,612.69 Gamma (5.618;0.002)Aged ≥ 60 3,284.81 1,435.54 1,270.37 5,879.22 Gamma (5.236;0.002)PSI class ≥ III 3,125.82 1,391.43 1,270.37 5,815.62 Gamma (4.741;0.002)ICU 4,180.82 1,434.50 1,755.12 5,879.22 Gamma (8.494;0.002)

NC groupAll cases 2,984.84 1,364.44 1,281.48 5,725.82 Gamma (4.786;0.002)Aged < 60 2,821.38 1,441.64 1,320.01 5,679.82 Gamma (3.830;0.001)Aged ≥ 60 3,163.16 1,272.82 1,281.48 5,725.82 Gamma (6.176;0.002)PSI class ≥ III 2,932.67 1,291.13 1,281.48 5,725.82 Gamma (5.159;0.002)ICU 3,392.25 1,434.90 1,534.96 5,560.95 Gamma (5.592;0.002)

ET groupAll cases 4,091.45 2,086.94 1,757.86 10,723.91 Gamma (3.844;0.001)Aged < 60 3,387.53 1,197.75 1,759.19 6,661.80 Gamma (7.999;0.002)Aged ≥ 60 6,992.35 1,875.02 1,770.64 10,411.34 Gamma (13.907;0.002)PSI class ≥ III 4,092.31 2,158.37 1,757.86 10,723.91 Gamma (3.595;0.001)ICU 5,666.88 2,235.09 1,769.25 10,411.34 Gamma (6.428;0.001)

Life-expectancy (years)AC group

All cases 22.11 12.07 3.47 53.26 Exponential (0.5;22.11)Aged < 60 28.97 9.34 18.89 53.26 Exponential (0.5;18.89)Aged ≥ 60 10.05 4.34 3.47 17.77 Exponential (0.5;10.05)PSI class ≥ III 16.43 9.04 3.74 43.93 Exponential (0.5;16.43)

ICU 16.28 7.70 7.03 27.05 Exponential (0.5;16.28)NC group

All cases 22.80 12.33 3.74 49.65 Exponential (0.5;22.80)Aged < 60 29.63 9.60 18.89 49.65 Exponential (0.5;29.63)Aged ≥ 60 10.67 5.16 3.74 17.77 Exponential (0.5;10.67)PSI class ≥ III 18.43 11.24 3.74 40.53 Exponential (0.5;18.43)

ICU 25.17 8.70 7.77 39.31 Exponential (0.5;25.17)ET group

All cases 20.31 10.71 3.74 57.99 Exponential (0.5;20.31)Aged < 60 27.68 9.23 18.89 57.99 Exponential (0.5;27.68)Aged ≥ 60 9.27 3.99 3.74 17.77 Exponential (0.5;9.27)PSI class ≥ III 15.97 7.41 3.74 40.53 Exponential (0.5;15.97)ICU 16.76 7.14 3.47 30.29 Exponential (0.5;16.76)

Notes: Group AC: antibiotic change after culture; group NC: no antibiotic change after culture; group ET: empirical antibiotics only

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RESULTS

Baseline characteristic of the patientsConcerning the change of antibiotics after culture analysis among patients in the CBT group, we

determined that the AC and NC probabilities were 55.8% and 44.2%, respectively. The probability

of antibiotic changes was based on microbiological evaluations and was not influenced by age or

severity. This was subsequently analyzed with beta distributions. Patients’ baseline characteristics

of the patients and the probabilities of antibiotic changes and deaths being averted in the AC, NC,

and ET groups are presented in Table 6.2.

Table 6.2 Baseline variables and probabilities for the inputs on the decision-tree model and univariate analysis from patient data

Variable Value DistributionBaseline (N=351)

Age in years, mean (SD) 57.6(15.2)Male, n (%) 275(78.3)Aged ≥ 60, n (%) 109(30.1)PSI class ≥ III, n (%) 208(59.3)ICU, n (%) 85(24.2)

Probability of antibiotic changesProbability of antibiotic change after culture (AC) 0.558 Beta (87,69)Probability of no-antibiotic change after culture (NC) 0.442 Beta (69,87)

Death averted in the AC group All cases 0.793 Beta (69,18)Aged < 60 0.917 Beta (44,4)Aged ≥ 60 0.641 Beta (25,14)PSI class ≥ III 0.769 Beta (40,12)ICU 0.667 Beta (8,4)

Death averted in the NC group All cases 0.725 Beta (50,19)Aged < 60 0.889 Beta (32,4)Aged ≥ 60 0.546 Beta (18,15)PSI class ≥ III 0.718 Beta (28,11)ICU 0.556 Beta (5,4)

Death averted in the ET group All cases 0.579 Beta (133,82)Aged < 60 0.577 Beta (60,44)Aged ≥ 60 0.270 Beta (10,27)PSI class ≥ III 0.581 Beta (68,49)ICU 0.391 Beta (25,39)

Notes: Group AC: antibiotic change after culture; group NC: no antibiotic change after culture; group ET: empirical antibiotics only

Cost-effectiveness analysesThe results of cost-effectiveness analyses are shown in Table 6.3. The implementation of CBT

over ET from a payer perspective was dominant, where CBT was cost-saving (US$ 1,066,885) and

improved LE (247 years) in all cases. In subgroup analyses, CBT was the most cost-saving, with an

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incremental cost of US$ 3,828,006 for adults 60 years or older. Moreover, in the elderly group (aged

≥ 60), in terms of health effects, it was found that 637 life-years were saved by CBT versus ET. Even

in the other categorizations, CBT performed better for both hospitalization costs and LE outcomes

for patients with PSI class ≥ III or ICU patients, and the cost-effectiveness outcomes were cost-

saving (dominant) compared to ET.

Table 6.3 Cost-effectiveness ratio for ET versus CBT in 1,000 patients with CAP

Group Cost (US$) LE (years) Incremental costs (US$)

Incremental LE(years) CEA outcomes

All casesET 4,074,326 1,252 Reference ReferenceCBT 3,007,441 1,499 -1,066,885 247 Dominant

Aged < 60ET 3,456,992 1,118 Reference ReferenceCBT 2,889,390 1,861 -567,602 742 Dominant

Aged ≥ 60ET 7,050,151 554 Reference ReferenceCBT 3,222,145 1,191 -3,828,006 637 Dominant

PSI class ≥ IIIET 4,050,898 1,189 Reference ReferenceCBT 3,068,381 1,479 -982,516 290 Dominant

ICUET 5,635,566 842 Reference ReferenceCBT 3,843,146 1,257 -1,792,420 432 Dominant

Abbreviation: CEA, cost-effectiveness analyses; ET, empirical treatment; ICU, intensive care unit; LE, life expectancy; US$, US dollar; PSI, pneumonia severity index; CBT, culture-based treatment

Univariate and probabilistic sensitivity analysesHospitalization costs for the ET group are shown on the top of the Tornado diagrams (Figure

6.3), which indicate that they had the strongest effect on the ICERs out of all parameters.

When hospitalization costs for the ET group increased, the ICERs decrease. In contrast, when

hospitalization costs for AC and NC groups (CBT) increased, the ICERs correspondingly increased.

The parameters of death averted in the CBT and ET groups were only slightly influential on ICER

calculations, especially in patients with moderate-severe CAP (PSI class III-V). Tornado diagram

evaluation is also provided in calculation results in Supplement 6.1.

The results of probabilistic sensitivity analyses on ICER values with 1,000 cohorts (Monte-Carlo

simulations) between CBT and ET are shown in Figure 6.4. Correspondingly, Figure 6.5 shows the

acceptability probabilities at varying WTP levels. In all cases, the probabilities of cost-effectiveness

acceptability in 1xWTP and 3xWTP were 75% and 83%, respectively. In the subgroup analyses of

patients all ages with moderate to severe CAP (PSI class ≥ III), and in the ICU, CBT would be a cost-

effective or even cost-saving approach compared to 1xGDP per capita (US$ 3,570). CBT for patients

60 years or older represented the most cost-effective option, with acceptability values between

81% for 1xWTP and 89% for 3xWTP. The results of the calculations of Monte-Carlo simulations and

cost-effectiveness acceptability are available in Supplement 6.2 and Supplement 6.3, respectively.

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Figure 6.3 Tornado one-way sensitivity analyses of ICER per LE comparing CBT against ET groups.

Note: Black and grey bars indicate high and low limits, respectively. PSI: pneumonia severity index.

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Figure 6.4 Monte-Carlo simulations of the cost-effectiveness analyses of CBT versus ET groups using 1,000 simulations.

Note: The circle indicates a 95% confident surface. CAP: community-acquired pneumonia, ICU: intensive care unit, LE: life expectancy, PSI: pneumonia severity index.

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Figure 6.5 Cost-effectiveness acceptability curve (CEAC) labelled on the 1xWTP (US$ 3570.28/LE) and 3xWTP(US$ 10,710.84).

Note: CAP: community-acquired pneumonia, ET: empirical treatment; CBT: culture-based treatment; ICU: intensive care unit, PSI: pneumonia severity index.

DISCUSSION

Our findings demonstrate that treatment based on the evaluation of cultures followed by

antimicrobial susceptibility testing (CBT) has advantages in terms of both cost and LE for CAP

patients. CBT was dominant, not exceeding the GDP threshold in terms of cost-effectiveness (three

times GDP) and even being cost-saving. Therefore, this method can reasonably be implemented

for adults in general hospitals in Indonesia. Without culture analysis, treatments for CAP are more

expensive and result in lost life-years, particularly for geriatric patient cases and high-severity

classes of patients.24–28 Notably, the incidence of CAP among elderly people is high since they

frequently present with pre-existing immunosuppressive conditions and comorbid conditions.29,30

In addition, comorbidities are frequently infected multidrug resistant GNB. According to ATS/BTS,

the severity of CAP infection among males was higher than females.31 This is the reason that males

need hospitalization more than females, as reflected in the input data for our study. Also, some

previous prospective studies demonstrated this issue.31–33

In non-ICU hospitalization, culture analysis is one of the procedures recommended by the

Infectious Diseases Society of America (IDSA) and ATS to guide antibiotic use for CAP patients.4

The implementation of the guidelines with the threshold of US$ 100,000/QALY (quality-adjusted

life years) leads to cost-savings of US$ 799 to US$ 1,379 per patient and improved quality of life,

particularly in elderly people.34 In the ICU, it is essential to consider CBT implementation since the

use of antibiotic is costly. A previous study conducted in an LMIC reported that antibiotic utilization

for CAP patients in ICU was determined as the highest proportion (38%) of total hospitalization

costs.35

The assessment of severity using the PSI score resulted in higher sensitivity of sputum culture

analyses. Gram staining and sputum culture procedures are very useful for inpatient treatment

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plans and ICU patients, guiding CAP diagnosis and antimicrobial selection.36 The organisms from

the microbiological culture are more likely to accurately represent respiratory tract pathogens in

CAP patients.37 Furthermore, the implementation of blood and sputum culture analysis should be

considered in patients with moderate and severe CAP with poor clinical prognosis and who are

considerably susceptible to bacteremia.38,39

Culture analysis is part of diagnostic stewardship that supports antimicrobial identification

and infection prevention programs. CBT should be part of such integrated management within

a theragnostic approach, involving multi-disciplines, and leading personalized management,

improvement of prescriptions for treatment, improved clinical outcomes, and decreasing costs.40,41

An issue of concern is the potential contamination of biological specimens. Contaminated blood

and sputum cultures could extend costs and have adverse effects.42,43 The role of the clinical

microbiologist is critical at this point for screening assessments of the quality of the sample.

Recently, several advanced methods to make rapid diagnoses of agents have been developed,

such as matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) and multiplex

nucleic acid amplification test (NAAT).44,45 However, these methods are costly and not able to test

for antimicrobial sensitivity.

To our knowledge, this study is the first cost-effectiveness analysis for culture analysis in CAP

patients considering real-world data and the impact of the effect of culture analysis in terms of

changes in antibiotic treatments, particularly in an LMIC setting. The utility impact was accounted

for the overall population as well as relevant subgroups of patients aged ≥ 60, with PSI class ≥ III

and in the ICU. This approach provided the necessary level of granularity. However, this study has

some limitations. First, community data from primary healthcare was not available. There is a lack

of sample data related to CAP in the community since facilities for clinical diagnostics and bacterial

identification remain unavailable, and these are not routine procedures, especially in remote areas.

In clinical practice, we assumed randomness in the decision to do ET or CBT, as confirmed by the

clinicians. However, we could not exclude that in some specific cases some aspects, may still have

influenced this decision and so probably introducing some bias in our analysis. Second, this study

used a retrospective dataset; patients with incomplete data related to ICER outcomes could not

be analyzed. In further investigations comparing the effects of treatments, randomized controlled

clinical trials are necessary to obtain additional results. As such, our study can be considered

as hypothesis generating rather than providing final evidence. Lastly, we did not include costs

associated with the side effects of the treatments in the present research. Common undesirable

side effects of antibiotic treatment, such as diarrhea, Clostridium difficile infections, and allergic

reactions can extend the length of hospitalization and increase costs.46

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CONCLUSION

In conclusion, CAP patients undergoing treatments based on culture analyses (CBT) were

estimated to receive benefits in terms of cost-savings and increased LE compared to those with

empirical treatment (ET) during their hospitalization; CBT was labeled dominant compared to ET.

Notably, the implementation of CBT provides considerable advantages for patients of all ages as

well as for higher severity classes. Most notably in severe CAP cases, cultures can adequately guide

antibiotic selection for patients with PSI class III or above or who are hospitalized in ICUs.

Abbreviations:AC: antibiotic change

ATS: American Thoracic Society

BTS: British Thoracic Society

Ca: cost of administration

CAP: Community-Aquired Pneumonia

Cb: cost for bed

CBT: Culture-based treatment

Cl: costs for laboratory and radiology

Cm: costs for medical staff

Cp: costs for pharmacy

ET: Empirical treatment

GDP: Gross Domestic Product

ICD: International Classification of Diseases

ICER: Incremental Cost-Effectiveness Ratio

ICU: Intensive Care Unit

IDSA: Infectious Diseases Society of America

LE: Life Expectancy

LMICs: Low-Middle Income Countries

MALDI-TOF: Matrix-Assisted Laser Desorption/Ionization-Time Of Flight

NAAT: Nucleic Acid Amplification Test

NC: no antibiotic change

NICE: National Institute for Health and Care Excellence

OECD: Organization for Economic Cooperation and Development

PSI: Pneumonia Severity Index

QALY: Quality-Adjusted Life Years

UNFPA: United Nations Population Fund

WHO: World Health Organization

WHO-CHOICE: World Health Organization – Choosing Interventions for Cost-Effectiveness

criterion

WTP: Willingness-to-Pay

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Supplement 6.1. Tornado sensitivity analyses for CBT and ET strategies in the management of hospitalized CAP patientsVariable Description Low High Spread (low) Spread (high)

All cases

Hospitalization costs for ET group - 1,702.13 279.94 1,982.08 3,928,622.39

Hospitalization costs for AC -456.77 112.40 569.17 323,954.59

Hospitalization costs for NC group -402.37 31.89 434.26 188,580.62

Death averted in NC group -235.74 -146.23 89.51 8,012.78

Death averted in AC group -236.00 -150.83 85.17 7,254.69

Death averted in ET group -1,013.85 983.70 ∞ ∞

Adults aged under 60

Hospitalization costs for ET group -363.57 103.78 467.35 218,416.45

Hospitalization costs for AC -136.75 94.51 231.26 53,480.59

Hospitalization costs for NC group -114.70) 69.00 183.70 33,745.07

Death averted in NC group -51.43) -45.17 6.26 39.18

Death averted in AC group -51.41) -45.59 5.83 33.96

Death averted in ET group -316.19) 442.11 ∞ ∞

Adults aged 60 or older

Hospitalization costs for ET group -1,960.84 398.82 2,359.66 5,567,991.74

Hospitalization costs for AC -1,334.13 -631.82 702.31 493,235.44

Hospitalization costs for NC group -1,254.29 -717.84 536.45 287,779.48

Death averted in NC group -1,026.44 -647.88 378.56 143,307.31

Death averted in AC group -1,027.20 -662.79 364.41 132,794.67

Death averted in ET group - 13,395.09 2,665.75 ∞ ∞

Moderate-severe CAP (PSI class III-V)

Hospitalization costs for ET group -2,123.09 354.40 2,477.49 6,137,956.89

Hospitalization costs for AC -576.73 124.08 700.81 491,140.19

Hospitalization costs for NC group -492.31 50.49 542.80 294,632.47

Death averted of NC group -290.62 -177.78 112.83 12,731.70

Death Averted of AC group -290.82 -183.42 107.40 11,534.42

Death averted of ET group -3,805.17 753.25 ∞ ∞

ICU

Hospitalization costs for ET group -1,156.19 362.65 1,518.84 2,306,882.65

Hospitalization costs for AC -560.24 -155.79 404.44 163,574.16

Hospitalization costs for NC group -466.71 -153.96 312.74 97,808.78

Death averted of NC group -322.07 -172.47 149.60 22,379.97

Death Averted of AC group -322.18 -210.39 111.79 12,497.85

Death averted of ET group -3,161.19 930.34 ∞ ∞

Note: CAP: community-acquired pneumonia, CBT: culture-based treatment, ET: empirical treatment, ICU: intensive care unit, PSI: pneumonia severity index

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Supplement 6.2. Monte-Carlo simulations in 1,000 cohorts for CBT and ET strategies in the management of hospitalized CAP patients

Outcomes Statistics Stra-tegy

Value

All cases Adults under 60

Adults aged 60 or older

Moderate-severe CAP

(PSI class III-V)

ICU

Cost Mean CBT 3,007.44 2889.39 3,222.15 3,046.72 3,068.38

Cost Std Deviation CBT 893.81 944.8045 964.13 951.56 983.56

Cost Minimum CBT 912.71 540.1228 937.49 678.83 853.31

Cost 2.50% CBT 1,434.07 1328.797 1,565.76 1,448.21 1,456.71

Cost 10% CBT 1,934.12 1761.192 2,077.65 1,909.83 1,840.49

Cost Median CBT 2,941.62 2774.46 3,123.43 2,949.63 2,982.67

Cost 90% CBT 4,215.28 4137.939 4,458.21 4,359.13 4,368.38

Cost 97.50% CBT 4,938.74 4920.783 5,415.11 5,154.58 5,232.67

Cost Maximum CBT 6,627.27 7492.163 6,365.89 6,988.28 6,579.57

Cost Sum CBT 3,007,441.00 2889390 3,222,145.00 3,046,720.00 3,068,381.00

Cost Size (n) CBT 1,000.00 1000 1,000.00 1,000.00 1,000.00

Cost Variance CBT 798,889.60 892655.5 929,548.20 905,461.50 967,388.90

Cost Variance/Size CBT 798.89 892.6555 929.55 905.46 967.39

Cost SQRT[Variance/Size] CBT 28.26 29.87734 30.49 30.09 31.10

Cost Mean ET 4,074.33 3456.992 7,050.15 4,173.98 4,050.90

Cost Std Deviation ET 2,030.08 1231.7 1,834.73 2,297.20 2,145.99

Cost Minimum ET 313.78 888.9348 2,797.97 341.85 363.47

Cost 2.50% ET 1,082.21 1535.393 3,937.13 982.26 1,027.17

Cost 10% ET 1,753.40 2043.714 4,785.93 1,652.35 1,689.49

Cost Median ET 3,724.93 3299.887 6,862.70 3,759.92 3,673.84

Cost 90% ET 6,884.16 5123.774 9,550.29 7,179.33 6,917.49

Cost 97.50% ET 8,713.29 6344.698 10,896.16 9,911.25 9,249.41

Cost Maximum ET 14,661.25 8471.662 13,741.44 16,800.82 13,584.59

Cost Sum ET 4,074,326.00 3456992 7,050,151.00 4,173,976.00 4,050,898.00

Cost Size (n) ET 1,000.00 1000 1,000.00 1,000.00 1,000.00

Cost Variance ET 4,121,221.00 1517086 3,366,240.00 5,277,116.00 4,605,254.00

Cost Variance/Size ET 4,121.22 1517.086 3,366.24 5,277.12 4,605.25

Cost SQRT[Variance/Size] ET 64.20 38.94979 58.02 72.64 67.86

Effectiveness Mean CBT 1.50 1.8607 1.19 1.46 1.48

Effectiveness Std Deviation CBT 1.10 1.331701 0.86 1.04 1.11

Effectiveness Minimum CBT 0.04 0.04186 0.01 0.03 0.04

Effectiveness 2.50% CBT 0.15 0.232968 0.16 0.16 0.20

Effectiveness 10% CBT 0.35 0.494444 0.30 0.37 0.38

Effectiveness Median CBT 1.23 1.55566 0.97 1.21 1.16

Effectiveness 90% CBT 2.95 3.487604 2.35 2.85 2.91

Effectiveness 97.50% CBT 4.46 5.354721 3.40 4.17 4.20

Effectiveness Maximum CBT 9.07 9.913279 5.51 6.83 10.40

Effectiveness Sum CBT 1,499.46 1860.7 1,191.01 1,458.19 1,478.78

Effectiveness Size (n) CBT 1,000.00 1000 1,000.00 1,000.00 1,000.00

Effectiveness Variance CBT 1.21 1.773427 0.73 1.08 1.23

Effectiveness Variance/Size CBT 0.00 0.001773 0.00 0.00 0.00

Effectiveness SQRT[Variance/Size] CBT 0.03 0.042112 0.03 0.03 0.04

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Outcomes Statistics Stra-tegy

Value

All cases Adults under 60

Adults aged 60 or older

Moderate-severe CAP

(PSI class III-V)

ICU

Effectiveness Mean ET 1.25 1.118317 0.55 1.18 1.19

Effectiveness Std Deviation ET 1.30 1.065232 0.60 1.17 1.16

Effectiveness Minimum ET 0.00 2.9E-05 0.00 0.00 0.00

Effectiveness 2.50% ET 0.03 0.02202 0.02 0.03 0.02

Effectiveness 10% ET 0.12 0.120462 0.06 0.12 0.13

Effectiveness Median ET 0.82 0.793365 0.36 0.78 0.81

Effectiveness 90% ET 3.08 2.551493 1.36 2.66 2.74

Effectiveness 97.50% ET 4.68 3.927367 2.23 4.40 4.37

Effectiveness Maximum ET 8.85 7.918168 4.20 8.37 7.45

Effectiveness Sum ET 1,252.13 1118.317 554.20 1,182.86 1,188.88

Effectiveness Size (n) ET 1,000.00 1000 1,000.00 1,000.00 1,000.00

Effectiveness Variance ET 1.68 1.134719 0.36 1.37 1.34

Effectiveness Variance/Size ET 0.00 0.001135 0.00 0.00 0.00

Effectiveness SQRT[Variance/Size] ET 0.04 0.033686 0.02 0.04 0.04

Note: CAP: community-acquired pneumonia, CBT: culture-based treatment, ET: empirical treatment, ICU: intensive care unit, PSI: pneumonia severity index

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Supplement 6.3. Cost-effectiveness acceptability analyses for CBT and ET strategies in the management of hospitalized CAP patients

Willingness-to-pay

In US$/LE

All cases Adults under 60 Adults aged 60 or older

Moderate-severe CAP (PSI

class III-V)ICU

ET CBT ET CBT ET CBT ET CBT ET CBT

0 0.599 0.401 0.566 0.434 0.252 0.748 0.364 0.636 0.228 0.772 357.03 0.523 0.477 0.442 0.558 0.244 0.756 0.352 0.648 0.214 0.786 714.06 0.461 0.539 0.363 0.637 0.239 0.761 0.330 0.670 0.205 0.795 1,071.08 0.405 0.595 0.313 0.687 0.235 0.765 0.324 0.676 0.208 0.792 1,428.11 0.359 0.641 0.277 0.723 0.229 0.771 0.328 0.672 0.209 0.791 1,785.14 0.326 0.674 0.261 0.739 0.222 0.778 0.327 0.673 0.213 0.787 2,142.17 0.301 0.699 0.241 0.759 0.218 0.782 0.322 0.678 0.217 0.783 2,499.20 0.285 0.715 0.225 0.775 0.212 0.788 0.321 0.679 0.223 0.777 2,856.22 0.273 0.727 0.21 0.79 0.204 0.796 0.319 0.681 0.225 0.775 3,213.25 0.266 0.734 0.201 0.799 0.197 0.803 0.321 0.679 0.226 0.774 3,570.28 0.251 0.749 0.195 0.805 0.195 0.805 0.318 0.682 0.229 0.771 3,927.31 0.238 0.762 0.186 0.814 0.188 0.812 0.318 0.682 0.237 0.763 4,284.34 0.234 0.766 0.183 0.817 0.185 0.815 0.324 0.676 0.240 0.760 4,641.36 0.226 0.774 0.18 0.82 0.176 0.824 0.323 0.677 0.245 0.755 4,998.39 0.223 0.777 0.175 0.825 0.172 0.828 0.327 0.673 0.253 0.747 5,355.42 0.218 0.782 0.172 0.828 0.169 0.831 0.331 0.669 0.254 0.746 5,712.45 0.215 0.785 0.169 0.831 0.165 0.835 0.331 0.669 0.258 0.742 6,069.48 0.209 0.791 0.163 0.837 0.162 0.838 0.335 0.665 0.258 0.742 6,426.50 0.206 0.794 0.161 0.839 0.160 0.840 0.338 0.662 0.258 0.742 6,783.53 0.203 0.797 0.157 0.843 0.154 0.846 0.340 0.660 0.260 0.740 7,140.56 0.199 0.801 0.154 0.846 0.149 0.851 0.342 0.658 0.261 0.739 7,497.59 0.195 0.805 0.152 0.848 0.144 0.856 0.342 0.658 0.261 0.739 7,854.62 0.190 0.810 0.15 0.85 0.142 0.858 0.341 0.659 0.262 0.738 8,211.64 0.190 0.810 0.15 0.85 0.139 0.861 0.343 0.657 0.263 0.737 8,568.67 0.188 0.812 0.149 0.851 0.133 0.867 0.343 0.657 0.266 0.734 8,925.70 0.184 0.816 0.147 0.853 0.128 0.872 0.342 0.658 0.268 0.732 9,282.73 0.182 0.818 0.145 0.855 0.126 0.874 0.342 0.658 0.268 0.732 9,639.76 0.178 0.822 0.144 0.856 0.122 0.878 0.343 0.657 0.270 0.730 9,996.78 0.176 0.824 0.14 0.86 0.117 0.883 0.347 0.653 0.274 0.726 10,353.81 0.173 0.827 0.138 0.862 0.115 0.885 0.347 0.653 0.275 0.725 10,710.84 0.172 0.828 0.138 0.862 0.112 0.888 0.348 0.652 0.276 0.724

Note: CAP: community-acquired pneumonia, CBT: culture-based treatment, ET: empirical treatment, ICU: intensive care unit, PSI: pneumonia severity index

Cost-effectiveness of culture-based treatment

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4Part

Discussion

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145

CHAPTER 7General discussion

146

GENERAL DISCUSSION

Chapter 2 was set the scene for this thesis, but as well forms the linking pin between much of the

work done here. Notably, Chapter 2 will now be discussed after most of the other chapters are

considered. A review of the economic evaluations of studies concerning preoperative prophylactic

antibiotics, administered either locally or systemically (Chapter 3), shows that they are considered

to be (cost-)effective interventions for surgical site infection (SSI) prevention.1 In the review, the

SSI rates ranged from 0 to 71%, with costs amounting to US$ 480–22,130 depending on the

type of surgery. Twenty-four types of bacteria were identified as causative agents of SSIs. Gram-

negative bacteria were the dominant causes of SSIs, especially in general surgery, neurosurgery,

cardiothoracic surgery, and obstetric cesarean sections. The independent potentially contributing

variables identified for deep SSI (dSSI) risk in major surgery, notably older patient age and neoplasm

comorbidity, need to be monitored intensively, and prophylactic antibiotics should, in fact, be

considered prior to initial incision.

The impacts of dSSIs were also investigated in Chapter 4 in an academic hospital in

Groningen, in the Netherlands. These impacts were reflected in readmission rates, extended LoS

and additional costs. Patients with SSIs in our study had a high readmission rate compared with

non-SSI patients. Also, dSSIs were found to contribute to prolonged LoS and high costs.2 The

explanation is that dSSI cases were readmitted for revision surgery because of wound infections

at the surgery sites and due to other post-surgical complications such as bleeding, dehydration,

renal failure, embolism, cardiovascular events, and ileus.3,4 Notably, dSSI patients were readmitted

at least once for revision surgery or wound debridement.5,6 As a strategy to reduce readmission

rates, post-surgical readmission could be replaced with home visits, or outpatient care within a

30-day medical follow-up after surgical discharge, possibly alleviating the costs.7 The timing of

unanticipated readmissions after surgical discharge, which is mainly contingent on the emergence

of crises relating to serious complications or existing comorbidities, requires further investigation.4

In addition, we found that dSSI patients aged 65 years or above were at a significantly higher risk of

readmission. The LoS for hospitalization and post-surgery readmission of elderly patients with SSIs

was three times greater than that for non-SSI patients. The mean cost incurred by older patients

was found to be double the cost incurred by non-SSI patients.8

The additional cost incurred for SSI cases is plausibly reflected in prolonged hospitalization

and readmissions.9–12 The cost, LoS and readmission rates of SSI cases were higher than those of

non-SSI cases in all types of surgery. Clean surgeries associated with the head and neck, thorax,

extremities, and spine were performed most frequently at this hospital, followed by contaminated

and dirty surgeries in which the abdominal region was commonly the targeted site. We discovered

that having SSI and being aged >65 were the two independent factors predicting extended LoS.

Elderly, neoplasm and cardiovascular diseases were also predictive at a higher risk of SSI, whereas

patients taking prophylactic antibiotics were at a lower risk of SSI. The cost of a day’s hospital

stay and rehospitalization, especially in the short-term, is virtually fixed, however.13 Moreover, the

costs due to antimicrobial resistance, included as an indication of the secondary cost of advanced

medication to overcome resistance rates, are eventually expected to become variable.

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As we revealed in Chapter 5, hospitalized community-acquired pneumonia (CAP) in Indonesia is

caused not only by Gram-positive bacteria (GBP) but also frequently by Gram-negative bacteria

(GNB).14 The pathogens generally remained sensitive to third-generation cephalosporins, which

are also recommended in the Indonesian national guideline. We identified A. baumannii as an

uncommon causative agent for CAP with high antimicrobial resistance. Our results are in line

with other regional studies, in which GNB has been determined to be the dominant class of

pathogens causing CAP in Indonesia and other countries within Asia.15–17 Multidrug-resistant

(MDR) Acinetobacter species are problematic, especially in immunocompromised hosts. In our

study, around 60% of Acinetobacter species were highly resistant to ciprofloxacin, similar to the

results of the Walter Reed Army Medical Center (WRAMC) study.18 In addition, we found that E.

coli, K. pneumoniae, and Enterobacter spp had poor sensitivity to penicillins. The CAP etiology

reported in Semarang, the sixth biggest city in Indonesia, showed that K. pneumonia was most

commonly identified among the bacteria causing hospitalized CAP.15

In view of the elevated mortality rates, the timing of culture collection, and unspecified results

from clinical and radiological methods for determining bacterial infections, the established

international guidelines of the British Thoracic Society (BTS), American Thoracic Society (ATS),

and National Institute for Health and Care Excellence (NICE) concur that empirical treatment of

CAP with antibiotics is urgently needed.19–21 In our study, microbiological culturing of sputum

and blood provided clinically relevant information on the identity of pathogens and their

susceptibility to antimicrobials. We found that S. pneumoniae was the most common pathogen

among hospitalized CAP cases, comparable to findings in thirteen other Asian countries.22 Mixed

presence of pathogens reflects an important consideration, since they may lead to delayed

response or even a lack of clinical improvement. In line with the systematic review of studies

in Asia, our findings revealed the mixed occurrence of infections often by S. pneumoniae and

M. tuberculosis or H. influenzae.16,17 The characteristics of the pathogens causing hospitalized CAP

should, therefore, be considered when the healthcare stakeholders provide Indonesian national

guidelines on the use of empirical antibiotics.

Empirical treatment confirmed by microbiological cultures and antimicrobial susceptibility

testing referred to as culture-based treatment (CBT), has advantages in terms of both cost and

life expectancy (LE) for hospitalized CAP patients. A pharmacoeconomics evaluation showed that

CBT was dominant (Chapter 6), not exceeding the GDP threshold in terms of cost-effectiveness

(three times GDP) and even producing cost savings.23 This method can therefore reasonably be

implemented for adults in general hospitals in Indonesia. Without culture analysis, fully empirical

treatments (ET) are more expensive and result in lost life-years, particularly in geriatric patients

and high-severity classes of patients.24–28

There is convincing evidence of a positive correlation between pneumonia and SSI and

sepsis.3,4,29 In this thesis, the economic burden for focal infections associated with sepsis for

surviving and deceased patients in Indonesia was addressed in Chapter 2. Sepsis was mostly

induced by lower-respiratory tract infections (LRTIs), including CAP. In addition to the findings of

sepsis burden in Indonesia in which conveyed in Chapter 2, multivariate and survival analyses of

the mortality outcome were performed.

General discussion

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148

RecommendationsDesired impacts of the proper use of prophylactic antibiotics in SSI prevention are both clinical and

economic: shorter lengths of stay, lower resistance and related better cure rates, and ultimately

a reduction in cost. There was some evidence of a positive relationship between infection rates

and LoS, reflecting that inpatients are at high risk of nosocomial infection by antibiotic- and multi-

resistant microorganisms. The microbial etiology of SSIs and antibiotic resistance are often omitted

from reports on the mid- and long-term clinical and economic impact of antibiotic use but should

get proper attention. Therefore, the use of prophylactic antibiotics, especially for SSIs, should take

into account the diagnostic-based antibiotic treatments using local epidemiological data on

pathogens from microbiological evaluations including patterns of antimicrobial susceptibility.

Clinical outcome measurements provide some evidence that systemic prophylactic antibiotics

have a significant impact on minimizing the incidence of SSIs and medical costs in high-risk patients,

especially those undergoing major surgical procedures. Preoperative measures and protocols

for prophylactic antibiotics include adherence to their proper use in terms of selection, optimal

dosing and timing. To achieve high efficacy, a current strategy is a prophylactic combination added

locally to the standard prophylaxis, especially in deep surgical sites, using intra-wound antibiotics,

for instance. The use of a local or intra-wound antibiotic as add-on treatment is predicted to be

more effective since the site-target concentration of antibiotics with local treatment is higher than

that without local antibiotics. However, a cost-effectiveness analysis of the effect of add-on local

wound prophylactic antibiotics on SSI outcomes is needed, particularly in developing countries.

Prophylactic antibiotics should be administered within 60 minutes (except for vancomycin

prophylaxis), as this is the optimal time to achieve clinically-effective SSI prevention. Moreover,

it is essential to monitor postoperative measures, focusing on the prolonged use of prophylactic

antibiotics and the timing of drain removal. Prolonged prophylactic use later than 24 hours post-

surgery did not show any benefit in terms of cost and SSI prevention.30

The clinical outcome not only depends on the prophylactic antibiotics given prior to surgical

procedures but also on whether the intervention is limited to preventing tissue damage, which

has the effect of accelerating wound healing. Other influential issues that should be considered

regarding the adequacy, quality, and cost of managing surgical patients include surgical

techniques, the availability of skilled surgeons, the types of disease, and the for-profit or not-profit

nature of the healthcare services involved.

Empirical antibiotic utilization for hospitalized CAP patients should be continued with a

diagnostic-based antibiotic treatment supported by microbiological information from CBT. Culture

analysis is part of good-practice diagnostic stewardship to support antimicrobial identification

and infection prevention programs. CBT should be part of such integrated management, based

on a theragnostic approach, involving multi-disciplines and a personalized treatment approach.

The CBT can play a critical role at this point to gain the improvement of treatment prescriptions

and clinical outcomes and cost reduction. In a resource-limited setting such as Indonesia, gram-

staining and sputum culture procedures are very useful for inpatient treatment plans and ICU

patients, guiding CAP diagnosis and antimicrobial selection. The organisms in the microbiological

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culture are more likely to accurately represent respiratory tract pathogens in CAP patients. The

implementation of blood and sputum culture analysis should be considered in patients with

moderate and severe CAP with a poor clinical prognosis, who are very susceptible to bacteremia.

The targeted population for CBT should include elderly patients.

Multivariate analyses revealed that mortality resulting from sepsis was significantly correlated

with patients aged >60 years, with gastrointestinal-tract infections (GTIs), LRTIs, neuromuscular

infections (NMIs), multi-focal infections, having SSIs, neoplasms, DM, cardiovascular disease, renal

disease, and ICU admission (Table 7.1). Overall, the survival rate of these patients was 39.3%. The

rate was lowest for sepsis patients with having SSIs (18.1%), followed by those with LRTIs (37.8%),

three or more focal infections (38.1%), two focal infections (39.7%), NMIs (41.6%), GIIs (42.5%), and

UTIs (56.1%). Figure 7.1 shows the Kaplan-Meier analysis revealing a significant difference in 30-day

survival rates among sepsis patients with site infections (log-rank test, p < 0.001). In addition, low

albumin levels, elevated levels of leukocytes, and prolonged prothrombin time were clinically

considered as independent predictors of mortality among adult patients with sepsis (Annex to

Chapter 7).

Table 7.1. Predictor factors for 30-day mortality among patients with sepsis. Covariate N(%) aOR 95% CI

SexFemale 6,609(47.0) RefMale 7,467(53.0) 0.975 0.903–1.052

Age<60 years 12,429(88.4) Ref>60 years 1,638(11.6) 1.357 1.199–1.536*

Types of focal infectionsUFI 1,637(11.6) RefCVI 110(0.8) 1.220 0.791–1.882GTI 1,328(9.4) 2.759 2.336–3.257*LRTI 3,932(27.9) 2.520 2.200–2.887*NMI 368(2.6) 2.140 1.658–2.763*UTI 1,348(9.6) 1.016 0.839–1.2312 focal infections 3,873(9.6) 2.332 2.010–2.705*3 or more focal infections 431(3.1) 3.247 2.529–4.169*Having SSIs 2,138(15.2) 2.223 1.954–2.529*

ComorbiditiesNeoplasm 2,123(15.1) 6.128 5.392–6.964*Diabetes mellitus 4,333(30.8) 2.098 1.919-2.292*Cardiovascular diseases 3,629(25.8) 4.455 4.044–4.907*Renal insufficiency 3,804(27.0) 1.134 1.019–1.263*ICU 4,297(30.8) 2.658 2.438–2.898*

Notes: aOR = adjusted odds ratio, CVI = cardiovascular infection, GTI = gastrointestinal-tract infection, ICU = intensive care unit, LRTI = lower respiratory tract infection, NMI = neuromuscular infection, OR = odds ratio, UFI = unspecified focal infection, Ref: reference, UTI = urinary tract infection, WI = wound infection.*Statistically significant.

General discussion

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Figure 7.1 Kaplan-Meier plots depicting 30-day survival rates for sepsis with focal infections.

Note: CVI = cardiovascular infection, GII = gastrointestinal infection, LRTI = lower respiratory tract infection, NMI = neuromuscular infection, SSI = surgical site infection, and UTI = urinary tract infection

We endorse the Indonesian national guidelines, which state that patients who have started on

empirical antimicrobial therapy and who show clinical improvement within the first three days

should be switched from intravenous to oral antibiotics. The day 3 evaluation is a critical point at

which to evaluate the efficacy of empirical treatment and to estimate patients’ risk of mortality. The

assessment of clinical responses on day 4 of the treatment for patients with community-acquired

bacterial pneumonia suggested in the guidance from the Food and Drug Administration (FDA)

could also be implemented.31 We recommend a combination assessment of clinical response

in the first three days to complement PSI scoring, where both assessments are investigated as

independent risk factors for mortality among patients with pneumonia. If there is a successful

response to empirical treatment, it could be useful to switch to oral antimicrobial treatment on

day 3, as this will provide additional information in the form of culture and susceptibility data from

the microbiology laboratory.

Future perspectivesFurther work needs to focus on the current challenges in the use of non-guided treatments

using prophylactic and empirical antibiotics so as to improve patient outcomes, especially in a

resource-limited setting such as Indonesia. First, updated data on the local pattern of resistance to

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antibiotics would be useful, although it may be more challenging to determine the definite cause

of SSI and CAP, since both accurate hospital data and community data from primary healthcare are

not always available. A multicenter qualitative study representing the 34 provinces of Indonesia

is urgently needed, with a robust research methodology, to look at the antibiotic strategies used.

Further research into patient characteristics is needed as well as studies to evaluate SSIs and

CAP impacts at an individual level, inclusive quality-of-life assessments. A pharmacoeconomics

analysis of screening for bacterial infections using gram staining from infected sites, which is more

likely to produce fast results, should be undertaken to guide antibiotic use from an economic

perspective as started in this thesis. Notably, all strategies and research need the full involvement

of government, clinicians, infection control practitioners, clinical microbiologists, hospital and

community pharmacists, nurses and nutritionists. Implementation of antimicrobial resistance

and infection prevention measures within quintessential Antimicrobial, Infection Prevention, and

Diagnostic (AID) stewardship, involving a multidisciplinary team in the resource-limited setting,

could be considered by adapting the results of our study conducted in the Netherlands (Chapter

4) to the situation in Indonesian hospitals.

General discussion

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5. Trick, W. E. et al. Modifiable risk factors associated with deep sternal site infection after coronary artery bypass grafting. J. Thorac. Cardiovasc. Surg. 119, 108–114 (2000).

6. Borger, M. A. et al. Deep sternal wound infection: risk factors and outcomes. Ann. Thorac. Surg. 65, 1050–1056 (1998).7. Jencks, S. F., Williams, M. V & Coleman, E. A. Rehospitalizations among patients in the Medicare fee-for-service program.

N. Engl. J. Med. 360, 1418–1428 (2009).8. Kaye, K. S. et al. The effect of surgical site infection on older operative patients. J. Am. Geriatr. Soc. 57, 46–54 (2009).9. Badia, J. M. et al. Impact of surgical site infection on healthcare costs and patient outcomes: a systematic review in six

European countries. J. Hosp. Infect. 96, 1–15 (2017).10. Coello, R. et al. Adverse impact of surgical site infections in English hospitals. J. Hosp. Infect. 60, 93–103 (2005).11. Kashimura, N. et al. Impact of surgical site infection after colorectal surgery on hospital stay and medical expenditure in

Japan. Surg. Today 42, 639–645 (2012).12. Mahmoud, N. N., Turpin, R. S., Yang, G. & Saunders, W. B. Impact of surgical site infections on length of stay and costs in

selected colorectal procedures. Surg. Infect. (Larchmt). 10, 539–544 (2009).13. Roberts, R. R. et al. Hospital and societal costs of antimicrobial-resistant infections in a Chicago teaching hospital:

implications for antibiotic stewardship. Clin. Infect. Dis. 49, 1175–1184 (2009).14. Purba, A. K. et al. Multidrug-Resistant Infections Among Hospitalized Adults With Community-Acquired Pneumonia In An

Indonesian Tertiary Referral Hospital. Infect. Drug Resist. 12, 3663–3675 (2019).15. Farida, H. et al. Viruses and Gram-negative bacilli dominate the etiology of community-acquired pneumonia in Indonesia,

a cohort study. Int. J. Infect. Dis. 38, 101–107 (2015).16. Peto, L. et al. The bacterial aetiology of adult community-acquired pneumonia in Asia: a systematic review. Trans. R. Soc.

Trop. Med. Hyg. 108, 326–337 (2014).17. Goyet, S. et al. Etiologies and resistance profiles of bacterial community-acquired pneumonia in Cambodian and

neighboring countries’ health care settings: a systematic review (1995 to 2012). PLoS One 9, e89637 (2014).18. Hujer, K. M. et al. Analysis of antibiotic resistance genes in multidrug-resistant Acinetobacter sp. isolates from military and

civilian patients treated at the Walter Reed Army Medical Center. Antimicrob. Agents Chemother. 50, 4114–4123 (2006).19. Mandell, L. A. et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the

management of community-acquired pneumonia in adults. Clin. Infect. Dis. 44 Suppl 2, S27-72 (2007).20. Eccles, S. et al. Diagnosis and management of community and hospital acquired pneumonia in adults: Summary of NICE

guidance. BMJ 349, 1–5 (2014).21. Lim, W. S. et al. BTS guidelines for the management of community acquired pneumonia in adults: update 2009. Thorax 64

Suppl 3, iii1-55 (2009).22. Hung, I. F.-N., Tantawichien, T., Tsai, Y. H., Patil, S. & Zotomayor, R. Regional epidemiology of invasive pneumococcal

disease in Asian adults: epidemiology, disease burden, serotype distribution, and antimicrobial resistance patterns and prevention. Int. J. Infect. Dis. 17, e364-73 (2013).

23. Purba, A. K. R. et al. Cost-Effectiveness Of Culture-Based Versus Empirical Antibiotic Treatment For Hospitalized Adults With Community-Acquired Pneumonia In Indonesia: A Real-World Patient-Database Study. Clinicoecon. Outcomes Res. 11, 729–739 (2019).

24. Olasupo, O., Xiao, H. & Brown, J. D. Relative Clinical and Cost Burden of Community-Acquired Pneumonia Hospitalizations in Older Adults in the United States-A Cross-Sectional Analysis. Vaccines 6, (2018).

25. Konomura, K., Nagai, H. & Akazawa, M. Economic burden of community-acquired pneumonia among elderly patients: a Japanese perspective. Pneumonia (Nathan Qld.) 9, 19 (2017).

26. Choi, M. J. et al. Disease burden of hospitalized community-acquired pneumonia in South Korea: Analysis based on age and underlying medical conditions. Medicine (Baltimore). 96, e8429 (2017).

27. Vissink, C. E., Huijts, S. M., de Wit, G. A., Bonten, M. J. M. & Mangen, M.-J. J. Hospitalization costs for community-acquired pneumonia in Dutch elderly: an observational study. BMC Infect. Dis. 16, 466 (2016).

28. Lee, J. Y., Yoo, C. G., Kim, H.-J., Jung, K. S. & Yoo, K. H. Disease burden of pneumonia in Korean adults aged over 50 years stratified by age and underlying diseases. Korean J. Intern. Med. 29, 764–773 (2014).

29. Jaja, B. N. R. et al. Association of Pneumonia, Wound Infection, and Sepsis with Clinical Outcomes after Acute Traumatic Spinal Cord Injury. J. Neurotrauma 36, 3044–3050 (2019).

30. Lewis, A. et al. Antibiotic prophylaxis for subdural and subgaleal drains. J. Neurosurg. 1–5 (2016). doi:10.3171/2016.4.JNS1627531. Eckburg, P. B. et al. Day 4 Clinical Response of Ceftaroline Fosamil Versus Ceftriaxone for Community-Acquired Bacterial

Pneumonia. 20, 254–260 (2012).

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General discussion

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CHAPTER 8Appendix

Laboratory Findings as Predictors of Sepsis Mortality Among Adult Patients in a General Hospital in Indonesia

Ahmad Lukman Hakim

Rahmat Sayyid Zharfan

Abdul Khairul Rizki Purba

This chapter is based on the published article:

Zarfan RS, Hakim AL, Purba AKR, Sulistiawan SS, Soemedi BP. Albumin, Leukosit, and Protombin

as Predictors of Sepsis Mortality among Adult Patients in Soetomo General Hospital, Surabaya,

Indonesia. Indonesian Journal of Anaesthesiology and Reanimation, 2019; 1(1): 8-12. doi: 10.20473/

ijar.V1I12019.8-12.

156

ABSTRACT

Introduction: Sepsis is a complex, multifactorial syndrome that is globally associated with high

morbidity and mortality rates. However, there is a paucity of conclusive evidence regarding early

predictive factors for sepsis-related mortality and morbidity.

Objective: We aimed to identify and analyze prominent predictors of sepsis-related mortality

determined from patients’ laboratory reports.

Methods: The study was designed as an analytic observational study, entailing a case-control

approach. The data for the study were extracted from the medical records of 50 patients with

sepsis admitted to Dr. Soetomo General Hospital in Surabaya, Indonesia between 2014 and 2015.

We assessed levels of blood urea nitrogen, creatinine serum, albumin, sodium, potassium, and

chloride in the patients’ blood reports along with leukocyte, hemoglobin, hematocrit, and platelet

counts, prothrombin time (PT), and activated partial thromboplastin time (APTT). We applied

logistic regression to estimate the frequency of sepsis-related mortality and the relationship

between the laboratory results and less than 28-days mortality.

Results: A total of 22 out of 50 patients (44%) succumbed. We initially conducted the regression

model using all three biomarkers as covariates and subsequently eliminated the covariate with

the highest p-value through a process of backward elimination. This process was repeated until

only statistically significant covariates remained. Multivariate analysis revealed that albumin levels,

leukocyte counts, and PT were associated with high mortality rates. We obtained the following

independent predictors of mortality, identified through further multivariate regression analyses:

an albumin level lower than 3.5 g/dL, a leukocyte count above 12,000/µL, and prolonged (>14

seconds) PT, with p-values below 0.05 (0.029, 0.049, and 0.027, respectively).

Conclusions: Low albumin levels, elevated leukocyte counts, and prolonged PT are clinically

considered as independent predictors of mortality among adult patients with sepsis.

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INTRODUCTION

Sepsis is a complex, multifactorial disease associated with high rates of morbidity and mortality

worldwide.1 It continues to pose considerable challenges within intensive care units because of

the associated high mortality rate despite the provision of optimal care. The introduction and use

of serum biomarkers have significantly enhanced the abilities of doctors to diagnose and predict

sepsis prognoses.1

Within the clinical practice, the number of leukocytes is an extensively used biomarker that is

deemed sufficient for assessing the clinical progress of patients with sepsis along with the use of

other laboratory parameters, such as lactate acid, procalcitonin, and c-reactive protein. However,

the limited availability of facilities for assessing these parameters, particularly in remote areas,

significantly impacts on hospital costs. A previous study conducted in Indonesia reported that of

the patients in the sample diagnosed with sepsis, 27.08% had severe sepsis, 14.58% were in septic

shock, and the remaining 58.33% were in a state of sepsis. In the case of patients with severe

sepsis, the mortality rate ranged between 40% and 60%.2

Given the complex pathophysiology of sepsis, more than one biomarker is required to enable a

comprehensive description of host responses to the disease. A combination of several biomarkers

in conjunction with certain classification rules would improve accuracy and applicability. Given

a paucity of conclusive evidence on early predictive factors for sepsis-related mortality and

morbidity, we aimed to identify prominent predictors based on values extracted from laboratory

reports, entailing a combination of several biomarkers associated with less than 28-days mortality

in sepsis patients. These values relate, for example, to leukocyte counts, albumin levels, and

coagulation factors.

MATERIALS AND METHODS

The study was designed as an analytic observational study using a case-control approach. The

data were extracted from the medical records of 50 patients with sepsis admitted to Soetomo

General Hospital in Surabaya, Indonesia, between 2014 and 2015.

The records of adult patients who fulfilled the criteria required for a sepsis diagnosis were

collected. Patients who had received antibiotics for a period exceeding 24 hours prior to blood

sampling were excluded. Baseline and demographic data, such as the sex, age, admission category,

main site of infection, and comorbidities of the patients, were collected.

Levels of blood urea nitrogen, creatinine serum, albumin, sodium, potassium, and chloride

along with counts of leukocytes, hemoglobin, hematocrit, and platelets, activated partial

thromboplastin time, and prothrombin time (PT) were compiled from the patients’ blood reports.

Multivariate logistic regression was conducted to estimate the correlation between covariates in

the laboratory findings and less than 28-days of sepsis-related mortality.

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RESULTS AND DISCUSSION

We performed multivariate logistic regression to model biomarker capabilities, with the aim of

identifying patients with a mortality outcome of less than 28 days. We initially constructed the

regression model using the whole biomarkers provided as covariates. The covariate with the

highest p-value was removed using a backward elimination method and the model was applied

to the remaining three biomarkers. This process was repeated until only statistically significant

biomarkers remained.

Table 8.1. Predictive Values for Leukocyte Counts, Prothrombin Time (PT), and Albumin Levels Associated with Mortality in Sepsis Patients

Biomarker All patients N = 50

Survivors n = 28

Non- Survivors n = 22

p-value AUROC Cut-off

Leukocytes 13,370 12,463.57 14,523.63 0.049 0.606 12,800Albumin 2.92 3.21 2.55 0.029 0.750 2.217PT 19.81 18.19 21.86 0.027 0.649 14.2

Note. The descriptions of the two groups were derived from the results of the Compare Means test. AUROC = area under the receiver operating characteristic curve; PT = prothrombin time

We calculated the probability of under 28-days sepsis-related mortality, which represents the final

predictor of sepsis-related mortality based on the results of the regression equation. As shown in

Table 1, 22 out of 50 patients (44%) died. The results of our multivariate analysis showed that low

albumin levels, high leukocytes counts, and extended PT were associated with high mortality

rates. We identified the following independent predictors of mortality, determined through

further multivariate regression analysis: a level of albumin lower than 3.5 g/dL (p-value = 0.029);

a leukocyte count above 12,000/µL (p-value = 0,049), and a prolonged PT (>14 seconds) (p-value

= 0.027). Figure 8.1 shows the area under the receiver operating characteristic curve (AUROC)

model used for predicting sepsis-related mortality along with each of the constituent predictive

biomarkers of mortality within 28 days. We found that the AUROC is from the model: the values

for the leukocyte count, PT, and albumin levels were 0.606, 0.649, and 0.750 (95% CI), respectively,

indicating reasonably good model discrimination. The sepsis mortality scores outperformed

those of the individual biomarkers in predicting mortality within 28 days. These biomarkers, when

applied, revealed moderate to good performance levels.

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Figure 8.1. Receiver Operating Characteristic (ROC) Curve depicting leukocyte counts, prothrombin time (PT), and albumin levels

For this analytic observational study, we collected historical data for 50 patients with sepsis and

assessed three biomarkers used to predict the risk of less than 28-days mortality for these patients

following their hospital admissions. The use of baseline leukocyte counts, PT, and albumin levels

as sepsis-related mortality predictors could provide reasonable predictions of under 28-days

mortality outcomes.

Biomarkers, especially when used in combination, can be reliable for predicting sepsis-

related mortality outcomes. The findings of recent studies3,4 also indicated that a combination of

biomarkers performed better than other clinical scores used routinely for predicting sepsis-related

mortality.

Sepsis itself often disrupts the coagulation function, leading to conditions that range from

mild changes to severe disseminated intravascular coagulation (DIC). Sepsis patients with severe

DIC may experience symptoms of thromboembolic diseases, such as purpura fulminans or

clinically obscure microvascular fibrin deposition, both of which are strongly indicative of multiple

organ dysfunction. Severe bleeding or bleeding and thrombosis may be the main symptoms.5

The disrupted coagulation mechanism, specifically DIC, is an important predictor and possible

clinical outcome for patients with severe sepsis.6

The onset of coagulation activated by proinflammatory cytokines, such as IL-6, is contingent

on tissue factors (TF). Increased thrombin formation is caused by the tumor necrosis factor (TNF-α),

which breaks down the damaged physiological anticoagulant mechanism, whereas the spread of

fibrin deposition within the microvasculature is caused by inadequate fibrin degradation resulting

from an inhibited fibrinolytic system.6 The complex TF-VIIa factor catalyzes the activation of the IX

and X factors, increasing the activation of factor X and prothrombin, respectively.8

Our results entailed the combined use of leukocyte counts, PT, and albumin levels as predictors

of sepsis-related mortality. Although the results are encouraging, this study had some limitations.

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Our predictions of sepsis deaths are based on data from a single case; whether or not they

can be generalized to an external population remains to be determined. Clinical outcomes are

dependent on the quality of patient management, which can vary across health centers. This

lack of standardization may have affected our results. However, the 44% mortality rate within

the sample population approximates conditions observed more widely in Indonesia.2 We

attempted to control confounders posed by other clinical variables by modeling sepsis death

scores within our logistic regression model. Nevertheless, we found it difficult to explain these

other unmeasured confounding factors. Moreover, the selection bias associated with the use of a

convenience sample in this study may have led to a less representative population. Consequently,

further research is needed to improve and validate the clinical applicableness of this sepsis-related

mortality predictor of the clinical outcomes of sepsis treatment.

CONCLUSIONS

Our findings indicated that from a clinical perspective, low albumin levels, elevated leukocyte

counts, and prolonged PT are independent predictors of mortality among adult patients with

sepsis. More research is needed to explore and develop these findings and to assess whether

these predictors of sepsis-related mortality, derived from biomarkers, into certain classification

and score, can be successfully integrated with physicians’ clinical practice to enhance predictions

and decision making relating to patients’ clinical settings.

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REFERENCES1. Wan Fadzlina Wan Muhd Shukeri, Azrina MdRalib, Nor Zamzila Abdulah, Mohd BasriMat-Nor. Sepsis mortality score for the

prediction of mortality in septic patients. Journal of Critical Care, Volume 43, February 2018, Pages 163-168. 2. Rheza N. Tambajong, Diana C. Lalenoh, Lucky Kumaat. Profil Penderita Sepsis di ICU RSUP Prof. Dr. R. D. Kandou Manado

Periode Desember 2014 – November 2015. Thesis. Fakultas Kedokteran Universitas Sam Ratulangi Manado. 2016. 3. Farzanegan B, Zangi M. Predictive factors for sepsis diagnosis, length of intensive care unit (ICU) stay and mortality in ICU.

J Cell Mol Anesth. 2017;2(2):55-62. 4. Bárbara Magalhães Menezes, Fábio Ferreira Amorim, Adriell Ramalho Santana, Felipe Bozi Soares, Fernanda Vilas Bôas

Araújo, Jacqueline Rodrigues de Carvalho, Mariana Pinheiro Barbosa de Araújo, Louise Cristhine de Carvalho Santos, Pedro Henrique Gomes Rocha, Jaqueline Lima de Souza, Mateus Gonçalves Gomes, Pedro Nery Ferreira Júnior, Alethea Patrícia Pontes Amorim, Rodrigo Santos Biondi and Rubens Antônio Bento Ribeiro. Platelet/leukocyte ratio as a predictor of mortality in patients with sepsis. Critical Care 201317 (Suppl 4) :52. Available on: https://doi.org/10.1186/cc12952.

5. Aziz Kallikunnel Sayed Mohamed, Asmita Anilkumar Mehta, &Ponneduthamkuzhy James. Predictors of mortality of severe sepsis among adult patients in the medical Intensive Care Unit. Lung India. 2017 JulAug; 34(4): 330–335.

6. Miriam Sanderson, Marc Chikhani, Esme Blyth, Sally Wood, Iain K Moppett, Tricia McKeever, & Mark JR Simmonds. Predicting 30-day mortality in patients with sepsis: An exploratory analysis of process of care and patient characteristics. Journal of the Intensive Care Society 0(0) 1–6The Intensive Care Society 2018.

7. Alison Woodworth.Sepsis and the Clinical Laboratory. Department of Pathology, Microbiology, and Immunology Vanderbilt University Medical Center Nashville, TN. 2013.

8. Shapiro NI, Trzeciak S, Hollander JE, et al. prospective, multicenter derivation of a biomarker panel to assess risk of organ dysfunction, shock, and death in emergency department patients with suspected sepsis. Crit Care Med, 2009; 37:96–104.

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163

Addendum

SummarySamenvattingRingkasanAcknowledgmentsCurriculum VitaeList of publicationsBiography

164

SUMMARY

Antibiotics are widely used for surgical site infections (SSIs) and hospitalized community-acquired

pneumonia (CAP) treatments. Typically, for SSIs and hospitalized CAP, antibiotics are often given

before the pathogen has been identified, and their use is not always adequately informed by

the antibiotic susceptibility profiles. Prophylactic antibiotics are used for SSI prevention, whereas

empirical antibiotics are used for the temporary initial treatment of hospitalized CAP, based on the

pathogenic patterns of causes and the patterns of antibiotic sensitivity particular healthcare centers

that may or may not be accurate. High intensity of antibiotics without guided microbiological tests

can generate resistant organisms, causing therapy failure in individual patients and high medical

costs. Diagnostic-based antibiotic treatments using microbiological evaluations may contribute to

improved use of prophylactic and empirical antibiotics.

First, we analyzed the cost burden of systemic sepsis infection in Indonesia, looking at focal

infections, including pneumonia and postoperative infections such as SSIs. This research was

carried out in an integrated manner with respect to surviving and death outcomes and is planned

as part of the insurance financing system when universal health coverage (UHC) is introduced

in Indonesia. The average hospital cost per surviving and deceased sepsis patient was US$ 1,011

and US$ 1,406 respectively. The national burden of sepsis in 100,000 patients is estimated at US$

130 million. Sepsis patients with multifocal infections and single focal infections of the lower

respiratory tract were estimated as the two groups with the highest economic burden (US$ 48

million and US$ 33 million respectively in 100,000 cases). It is important to consider mortality and

focal infection when assessing the burden of sepsis, as there are significant differences in the total

cost of care. In a resource-limited context such as Indonesia, where the new UHC system is being

implemented, the provision of adequate health services requires a re-evaluation and recalculation

of the cost of sepsis. Furthermore, cases of sepsis with multifocal infections and pneumonia

should be categorized as high-burden cases; it is cases like these that require price adjustments at

the national level when replacing private and public health services.

The policy adopted by the government and clinicians in hospitals for the prevention and

control of antibiotic resistance in SSIs and hospitalized CAP needs to be supported by scientific

evidence. Pharmacoeconomics provides an integrity evaluation and an interpretation of the extent

and accuracy of the handling of SSI and hospitalized CAP patients in the therapeutic context, with

appropriate morbidity and mortality targets and outcomes. Clinical microbiological evaluation

to identify pathogens in these two diseases and the economic impact on patients’ outcomes

need to be analyzed as part of the pharmacoeconomics when developing a strategy for the use

of antibiotics. The strategy implemented needs to be effective, efficient and affordable and to be

able to improve patients’ quality of life.

The first part of this thesis contains a comprehensive discussion of SSIs based on a review of

20 studies of the effectiveness and cost of prophylactic antibiotics for patients who are about to

undergo surgery, in order to prevent postoperative SSI events (Chapter 3). Indeed, the preoperative

phase is an important period when it comes to preventing SSIs. Prophylactic antibiotics help to

Addendum

165

reduce the level of SSI, leading to a reduction in the time and cost of hospitalization. Preoperative

prophylactic antibiotics are given both locally and systemically, and this has been examined in

several studies on preventing SSIs. Among the studies reviewed, there were 14 trial-based studies;

the others were model-based studies. The incidence of SSIs in the trial-based study ranged from 0

to 71%, with average hospital care costs of between US$ 482 and US$ 22,130. A cohort study using

prophylactic antibiotics to prevent SSIs in primary hip replacement yielded pharmacoeconomics

outcomes with an estimated cost of US$ 121,000/QALY. In clinical practice, the selection of

prophylactic antibiotic agents also needs to take evidence on the microbiological costs and

results into account, in addition to effectiveness and safety. The most recent scientific evidence

on the use of antibiotics for SSI prophylaxis is presented in Chapter 3 from the perspective of

pharmacoeconomics and epidemiological microbiological findings. Twenty-four bacteria were

identified as agents causing SSI. Gram-negative bacteria are the dominant cause of SSIs, especially

in general surgery, neurosurgery, cardiothoracic surgery, and obstetric surgery patients.

The impact of SSI disease on hospital admission, length of stay and cost has been analyzed in-

depth, including predictors of length-of-stay outcomes and of SSI outcomes, which are discussed

in Chapter 4. Of a total of 12,285 patients in an academic hospital in the Netherlands, 343 SSI

patients (87%) needed a hospital stay after surgery. The average length of stay is around 12 days,

with an estimated cost per hospital admission of € 9,016. Independent variables related to SSI

outcome were patient’s age > 65 years (OR: 1.334; 95% CI: 1.036-1.720), prophylactic antibiotic use

(OR: 0.424; 95% CI: 0.344-0.537), and comorbid cancer (OR: 2,050; 95% CI: 1,473-2,854). In addition,

patients suffering from SSI showed a prolonged length of stay (HR: 0.742; 95% CI: 0.679-0.809).

The third part of this thesis discusses hospitalized CAP in-depth and the pathogens

responsible, in order to assess therapeutic effectiveness. The characteristics of the germs

that cause pneumonia are analyzed in Chapter 5. The epidemiology study of the etiology of

hospitalized CAP due to bacterial infections in Indonesia shows that one-fifth are multiple drug-

resistant organisms (MDROs). Resistance to ciprofloxacin and amoxicillin/clavulanate reached 82%.

Acinetobacter baumannii was a bacterium that was found to be multiresistant to some antibiotics.

Several factors, such as a history of inappropriate use and the use of unprescribed antibiotics

in the community, can cause multiresistant infections. In addition, patients with diabetes, heart

disease, cancer, kidney disorders, liver disorders, and immune disorders also trigger community

pneumonia with drug resistance. We recommend the third-generation drug cephalosporin as an

empirical antibiotic in national guidelines, since the sensitivity rate remained high (67-82%).

The risk of death in cases of pneumonia depends on the following three factors: the patient’s

condition, bacterial factors, and treatment. The mortality rate increased significantly in patients

with severe hospitalized CAP and those who did not show any improvement after day three.

Most of the severe pneumonia that requires hospital treatment is suffered by male patients aged

56 years and above. Symptoms that often arise are shortness of breath (98%), fever (96%), cough

(74%), and chest discomfort (21%). Patients with cancer and those with weak immunity are more

likely to fall prey to this severe hospitalized CAP, so it requires close clinical observation. An MDRO

infection may be suspected if the patient has received therapy but there is no clinical improvement,

Summary

166

for example, the patient is still complaining of dyspnea and fever. Clinical assessment 72 hours

after the administration of empirical antibiotics is a reliable integrated assessment indicator of

hospitalized CAP. In combination with the pneumonia severity index (PSI), this 72-hour clinical

assessment helps to predict mortality outcomes.

Hospitalized CAP patients treated in hospitals generally had serious symptoms that required

intensive observation and inpatient treatment. The focus during observation in the inpatient

room was the selection of appropriate antibiotics, based on the results of microbiological cultures

of both sputum and blood, to prevent further antibiotic resistance. Treatment based on culture

evaluation and antibiotic susceptibility testing provides benefits in terms of reduced cost and

extended life expectancy. The recommended strategy for the use of empirical treatment is

discontinuation the antibiotic administration if the culture results are negative and the patient

shows a clinical improvement (Chapter 6). The implementation of germ culture in pneumonia

cases can save as much as US$ 1,067 per patient and increase life expectancy in all cases. Giving

patients culture-based treatment (CBT), especially in intensive care, would save US$ 1,792 per

patient, along with a higher life expectancy than without CBT. Interestingly, in the elderly group,

CBT not only mediates the right antibiotic choices and saves a cost of US$ 3,828 per patient, it also

increases life expectancy by one year compared with patients who are not given CBT.

The implementation of microbiological culture analysis in developing countries with a

high incidence of CAP, such as Indonesia, needs to be considered. Since 2014, Indonesia has

implemented a national health insurance system (Jaminan Kesehatan Nasional, JKN) to manage

spending on treatment. Given the current limitations on administering cost-based antibiotics

to pneumonia patients, CBT could be used for CAP patients receiving treatment in hospitals

in Indonesia. Bacterial culture analysis makes the administration of antibiotics in the treatment

of pneumonia more precise, hence it can ultimately reduce the cost of care and increase life

expectancy, especially in the case of elderly patients, those with immune disorders and those

with concomitant diseases.

Additional analysis of antibiotic uses for empirical treatment, Chapter 6 analyzes the cost

burden of systemic sepsis infection by considering focal infections including pneumonia and

postoperative infections such as SSIs. This research was carried out in an integrated manner with

respect to life and death outcomes and is projected in the insurance financing system in the

era of universal health coverage (UHC) in Indonesia. The average hospital cost per living sepsis

and deceased patient was US$ 1,011 and US$ 1,406, respectively. The national burden of sepsis

in 100,000 patients is estimated at US$ 130 million. Sepsis patients with multifocal infections and

single focal infections of the lower respiratory tract infections are estimated as the two with the

highest economic burden (US$ 48 million and US$ 33 million, respectively, in 100,000 cases). It

is important to consider mortality and focal infection in the assessment of the burden of sepsis

because there are significant differences in the total cost of care. In a resource-limited context

such as in Indonesia, where the new UHC system is implemented, the provision of adequate

health services requires a re-evaluation and re-calculation of prices for sepsis. Furthermore, cases

of sepsis with multifocal infections and pneumonia should be categorized as high-burden sepsis

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167

cases, reflecting the clearest examples that require national price adjustments for the replacement

of private and public health services.

Summary

168

SAMENVATTING

Antibiotica worden vaak gebruikt om postoperatieve wondinfecties (POWI’s) en community-

acquired pneumonie (CAP) waarvoor een ziekenhuisopname plaatsvindt (gehospitaliseerde CAP)

te voorkomen. Deze aandoeningen dienen te worden onderzocht, aangezien antibiotica worden

toegediend nog voordat de ziekteverwekker is geïdentificeerd en het niet bekend is voor welke

antibiotica de ziekteverwekker gevoelig is. Antibiotica worden profylactisch toegediend om POWI’s

te voorkomen. Antibiotica worden daarentegen empirisch toegepast als initiële behandeling

voor gehospitaliseerde CAP. Deze empirische behandeling is gebaseerd op het patroon van

ziekteverwekkers en hun gevoeligheid voor antibiotica in een bepaalde zorginstelling. Hierbij

dient als kanttekening te worden geplaatst dat het toedienen van hoge doseringen antibiotica

zonder microbiologische onderbouwing kan leiden tot resistente micro-organismen. Hierdoor

kan de behandeling bij individuele patiënten mislukken en kunnen de medische kosten hoog

oplopen.

Het beleid van de overheid en klinisch werkzame artsen om antibioticaresistentie bij POWI’s

en gehospitaliseerde CAP te voorkomen en te bestrijden dient wetenschappelijk te worden

onderbouwd. Aan de hand van farmaco-economie kan een volledigheidsonderzoek en een

interpretatie van de reikwijdte en nauwkeurigheid van het beleid bij POWI’s en gehospitaliseerde

CAP plaatsvinden binnen een therapeutische setting. Daarin kunnen de juiste doelstellingen

en uitkomsten op het gebied van morbiditeit en mortaliteit worden opgenomen. Een

antibioticabeleid dient te zijn gestoeld op een farmaco-economische evaluatie bestaande uit een

klinisch microbiologische evaluatie om de ziekteverwekkers van POWI’s en gehospitaliseerde CAP

te identificeren en een analyse van de economische gevolgen voor de patiëntuitkomsten. Het

beleid dient effectief, efficiënt en betaalbaar te zijn. Daarnaast dient de kwaliteit van leven van de

patiënten door het beleid te worden bevorderd.

De preoperatieve fase is van groot belang bij het voorkomen van POWI’s. Door het profylactisch

toedienen van antibiotica kan het aantal POWI’s worden verminderd. Hierdoor nemen het aantal

opnamedagen en de opnamekosten af. Het preoperatief toedienen van antibioticaprofylaxe

vindt zowel lokaal als systemisch plaats. Dit is onderzocht in een aantal studies naar de preventie

van POWI’s. In het eerste deel van dit proefschrift vindt een uitgebreide bespreking plaats van

POWI’s op basis van een overzichtsstudie. In deze studie worden twintig onderzoeken beschreven

naar de effectiviteit en kosten van het profylactisch toedienen van antibiotica voorafgaand aan

een operatie met als doel het voorkomen van POWI’s (Hoofdstuk 3). Veertien van de onderzoeken

waren ‘trial-based’; de overige waren ‘model-based’. De incidentie van POWI’s in de ‘trial-based’-

studies varieerde van 0 tot 71%. De ziekenhuiskosten in deze studies bedroegen gemiddeld

US$482 tot US$22.130. In een cohortstudie waarin antibioticaprofylaxe werd toegediend bij het

plaatsen van een eerste (primaire) heupprothese om POWI’s te voorkomen, werden de kosten

op basis van een farmaco-economische analyse geschat op US$121.000/QALY. In de klinische

praktijk dient de keuze voor de profylactisch toe te dienen antibiotica niet alleen gebaseerd

te zijn op de effectiviteit en veiligheid van de antibiotica, maar ook op de kosten en resultaten

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van microbiologische diagnostiek. In Hoofdstuk 3 worden de meest recente wetenschappelijke

gegevens betreffende het toedienen van antibioticaprofylaxe bij POWI’s gepresenteerd op

basis van farmaco-economisch onderzoek en epidemiologisch microbiologische bevindingen.

Van vierentwintig bacteriën werd vastgesteld dat ze POWI’s kunnen veroorzaken. Het betreft

overwegend Gram-negatieve bacteriën, die met name POWI’s kunnen veroorzaken bij algemene

chirurgische, neurochirurgische, cardio-thoracale en obstetrische ingrepen.

In Hoofdstuk 4 bespreken we de resultaten van een uitgebreide analyse van de relatie

tussen POWI’s en ziekenhuisopnamen, opnameduur en opnamekosten. Daarnaast hebben

we onderzoek gedaan naar eventuele voorspellers van de opnameduur en het optreden van

POWI’s. In een academisch ziekenhuis in Nederland werden in totaal 12.285 patiënten behandeld.

Driehonderddrieënveertig van hen (87%) moesten na een chirurgische ingreep worden

opgenomen vanwege een POWI. De gemiddelde opnameduur bedroeg ongeveer twaalf dagen.

Een ziekenhuisopname kostte naar schatting $9.016. Onafhankelijke variabelen die verband

hielden met POWI’s waren: leeftijd van de patiënt ≥ 65 jaar (OR: 1.334; 95% CI: 1.036-1.720), het

toedienen van antibioticaprofylaxe (OR: 0.424; 95% CI: 0.344-0.537) en comorbiditeit in de vorm

van een maligniteit (OR: 2.050; 95% CI: 1.473-2.854). Daarnaast bleek dat patiënten met een POWI

langer in het ziekenhuis verbleven (HR: 0.742; 95% CI: 0.679-0.809).

In het tweede deel van dit proefschrift komen gehospitaliseerde CAP en de ziekteverwekkers

uitgebreid aan bod, met als doel de therapeutische effectiviteit vast te stellen. De eigenschappen

van de bacteriën die pneumonie veroorzaken worden onderzocht in Hoofdstuk 4. Uit een

epidemiologische studie naar de oorzaak van gehospitaliseerde CAP met een bacteriële

verwekker in Indonesië blijkt dat een vijfde van deze infecties wordt veroorzaakt door Multidrug

Resistente Organismen (MDRO). Tot 82% van de organismen was resistent tegen ciprofloxacine

en amoxicilline/clavulanaat. De bacterie Acinetobacter baumannii bleek resistent te zijn tegen een

aantal antibiotica. Infecties met multiresistente bacteriën kunnen ontstaan wanneer bijvoorbeeld

antibiotica in het verleden op inadequate wijze zijn gebruikt en wanneer antibiotica zonder

recept worden gebruikt binnen de gemeenschap. Daarnaast zijn patiënten met diabetes,

hartaandoeningen, oncologische aandoeningen, nierziekten, leverziekten en immuunziekten

vatbaar voor CAP veroorzaakt door organismen die resistent zijn tegen antibiotica. Omdat

bacteriën gevoelig blijven voor derde generatie cefalosporines (67-82%) adviseren we deze

antibiotica als empirische behandeling op te nemen in nationale richtlijnen.

Of een patiënt met pneumonie overlijdt, hangt af van drie factoren, namelijk de conditie

waarin de patiënt verkeert, eigenschappen van de bacterie en de behandeling. Patiënten met

ernstige gehospitaliseerde CAP en patiënten bij wie geen enkele verbetering werd geconstateerd

na de derde dag, hadden een significant hogere kans om te overlijden. Met name mannen

≥56 jaar leden aan ernstige gehospitaliseerde pneumonie. Symptomen die vaak voorkomen

zijn kortademigheid (98%), koorts (96%), hoesten (74%) en ongemak en pijn in de borststreek

(21%). Patiënten met een oncologische aandoening en patiënten met een verminderde afweer

hebben een grotere kans op ernstige gehospitaliseerde CAP. Daarom dient nauwkeurige klinische

observatie plaats te vinden. Er dient rekening te worden gehouden met een infectie die wordt

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170

veroorzaakt door Multidrug Resistente Organismen (MDRO) als de patiënt ondanks behandeling

klinisch niet vooruitgaat. Hiervan kan bijvoorbeeld sprake zijn bij persisterende kortademigheid

en koorts. Gehospitaliseerde CAP kan op betrouwbare wijze worden vastgesteld aan de hand van

een klinische beoordeling die tweeënzeventig uur na het empirisch toedienen van antibiotica

plaatsvindt. Op basis van deze klinische beoordeling na tweeënzeventig uur in combinatie met

de ‘pneumonia severity index’ (PSI) kan worden voorspeld of de patiënt kans loopt te overlijden.

Gehospitaliseerde patiënten met CAP hadden meestal ernstige symptomen waarvoor

intensieve observatie en een klinische behandeling nodig was. De observatie in de behandelkamer

was gericht op het selecteren van de juiste antibiotica op basis van de uitslagen van sputum-

en bloedkweken. Het doel was om verdere resistentie tegen antibiotica tegen te gaan.

Behandeling op basis van kweekonderzoek en op basis van een bepaling voor welke antibiotica

de ziekteverwekker gevoelig is, leidt tot lagere kosten en een hogere levensverwachting van de

patiënt. Bij een empirische behandeling is het raadzaam het gebruik van antibiotica te beëindigen

indien de kweekuitslagen negatief zijn en de patiënt klinisch vooruitgaat. De implementatie van

kweekonderzoek bij patiënten met een pneumonie kan leiden tot een kostenbesparing van

US$1.067 per patiënt en een hogere levensverwachting van alle patiënten. Met name op de

intensive care leidt behandeling op basis van kweekonderzoek (‘culture-based treatment’, CBT) tot

een kostenbesparing van US$1.792 per patiënt en tot een hogere levensverwachting dan wanneer

CBT niet wordt gegeven. Bij ouderen leidt CBT niet alleen tot de juiste antibioticakeuze en een

kostenbesparing van US$3.828 per patiënt, maar ook tot een toename van de levensverwachting

met één jaar vergeleken met het niet toepassen van CBT.

In ontwikkelingslanden met een hoge incidentie van CAP, zoals Indonesië, dient het

invoeren van kweekonderzoek te worden overwogen. Sinds 2014 is in Indonesië de Sociale

Ziektekostenverzekering (BPJS Kesehatan) in werking getreden om de zorguitgaven te beheersen.

CBT zou toepasbaar kunnen zijn bij gehospitaliseerde patiënten met CAP in Indonesië, gelet op

de huidige beperkingen om antibiotica op basis van kosten toe te dienen aan patiënten met

een pneumonie. Het uitvoeren van kweekonderzoek kan leiden tot gerichtere toepassing van

antibiotica bij de behandeling van een pneumonie, met als gevolg lagere zorgkosten en een

hogere levensverwachting met name bij oudere patiënten, patiënten met immuunziekten en

patiënten met comorbiditeiten.

We hebben de toepassing van antibiotica als empirische behandeling nader onderzocht. In

Hoofdstuk 6 hebben we onderzoek gedaan naar de kosten als gevolg van sepsis. Daarbij hebben

we gelokaliseerde infecties bestudeerd, waaronder pneumonie en postoperatieve infecties zoals

POWI’s. Dit onderzoek, waarin uitkomsten wat betreft leven en overlijden op geïntegreerde wijze

in kaart zijn gebracht, is opgezet in het kader van de financiering van de invoering van een

universele ziektekostenverzekering (‘universal health coverage’, UHC) in Indonesië. Voor patiënten

die sepsis overleefden bedroegen de ziekenhuiskosten gemiddeld $1.011. Voor patiënten die

overleden aan sepsis bedroegen de ziekenhuiskosten daarentegen gemiddeld $1.406. Op

nationaal niveau worden de kosten als gevolg van sepsis geschat op $130.000.000 per 100.000

patiënten. De ziektebeelden die gepaard gaan met de hoogste kosten zijn sepsis op basis van

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171

infecties in meerdere organen ($48.000.000 per 100.000 patiënten) en sepsis op basis van een

gelokaliseerde infectie in de onderste luchtwegen ($33.000.000 per 100.000 patiënten). Bij het

vaststellen van de kosten van sepsis dienen de mortaliteitcijfers en de lokalisatie van de infecties te

worden meegewogen, aangezien er significante verschillen zijn in de totale zorgkosten. In landen

zoals Indonesië, waar de middelen beperkt zijn en een nieuwe universele ziektekostenverzekering

wordt ingevoerd, moeten de kosten als gevolg van sepsis opnieuw worden geëvalueerd en

berekend om adequate gezondheidszorg te kunnen verlenen. Sepsis op basis van infecties in

meerdere organen en sepsis op basis van een pneumonie moeten als een hoge kostenpost worden

beschouwd. Deze ziektebeelden maken een prijsaanpassing op nationaal niveau noodzakelijk bij

het vervangen van het particuliere en openbare gezondheidszorgstelsel.

Samenvatting

172

RINGKASAN

Obat yang digunakan secara luas untuk pencegahan infeksi daerah operasi (IDO) dan penyakit

pneumonia komuniti adalah antibiotik. Pembahasan IDO dan pneumonia komuniti menjadi hal

yang penting dibahas karena penggunaan antibiotik untuk kedua penyakit ini diberikan lebih

awal sebelum patogen teridentifikasi dan belum mengetahui kerentanannya terhadap antibiotik

yang diberikan. Antibiotik profilaksis digunakan untuk pencegahan IDO sedangkan antibiotik

empirik digunakan untuk pengobatan sementara pneumonia komuniti berdasar pola patogen

penyebab dan pola kepekaan antibiotik di suatu layanan kesehatan. Intensitas penggunaan

antibiotik yang tinggi dapat menimbulkan penggunaan antibiotik yang tidak rasional. Masalah

besar akibat ketidakrasionalan penggunaan antibiotik adalah resistensi obat yang menyebabkan

kegagalan terapi dan biaya pengobatan yang tinggi.

Implementasi strategi yang dilakukan pemerintah dan pemegang kebijakan klinis di rumah

sakit dalam pencegahan dan pengendalian resistensi antibiotik pada IDO dan penumonia komuniti

harus didukung oleh bukti ilmiah. Evaluasi farmakoekonomi secara integritas memberikan

interpretasi sejauh mana ketepatan dan efektivitas penangan pasien IDO dan pneumonia

komuniti dalam konteks terapi dengan target yang tepat dan luaran morbiditas dan mortalitas.

Analisis evaluasi mikrobiologi klinik terhadap identifikasi patogen pada kedua penyakit tersebut

merupakan bagian dari farmakoekonomi yang dipertimbangkan untuk menyusun strategi dalam

penggunaan antibiotik. Strategi yang diimplementasikan diharapkan efektif, efisien, terjangkau,

dan dapat meningkatkan kualitas hidup pasien.

Penggunaan antibiotik profilaksis membantu mengurangi tingkat IDO, yang mengarah pada

pengurangan waktu dan biaya rawat inap. Antibiotik profilaksis pra operasi yang diberikan baik

secara lokal maupun sistemik perlu dipertimbangkan dalam mencegah IDO. Pada bagian pertama

tesis ini, IDO dibahas secara komprehensif melalui review 20 studi terkait efektivitas dan biaya dari

penggunaan antibiotik profilaksis untuk pasien yang akan menjalani operasi dalam mencegah

IDO pascaoperasi (Bab 3). Dari studi yang direview, terdapat 14 studi berbasis riset pada populasi,

dan yang lainnya adalah studi berbasis model. Insiden IDO pada studi populasi berkisar antara 0

hingga 71% dengan biaya rerata perawatan rumah sakit sebesar antara US$482 hingga US$22.130.

Pada studi model kohort penggunaan antibiotik profilaksis untuk pencegahan IDO pada tindakan

primary hip-replacement menunjukkan estimasi biaya sebesar US$121,000/QALY. Fase pra operasi

adalah periode penting untuk mencegah IDO. Dalam praktik klinis, selain efektivitas dan keamanan,

pemilihan agen antibiotik profilaksis juga harus mempertimbangkan bukti yang berkaitan dengan

biaya dan hasil mikrobiologis. Bukti ilmiah terkini terkait dengan penggunaan antibiotik untuk

profilaksis IDO ditampilkan pada Bab 3 dengan perspektif farmakoekonomi dan epidemiologi

temuan mikrobiologis. Dua puluh empat bakteri diidentifikasi sebagai agen penyebab SSI. Bakteri

Gram negatif adalah penyebab dominan SSI terutama pada pasien dengan tindakan bedah

umum, bedah saraf, bedah kardiotoraks dan operasi obstetrik.

Penyakit IDO berdampak terhadap kejadian hospital readmission, lama rawat inap, dan biaya.

Dampak tersebut dianalisis secara mendalam dengan analisis faktor prediktor luaran lamanya

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173

perawatan di rumah sakit dan faktor prediktor luaran terjadinya IDO yang dibahas dalam Bab 3. Dari

total 12.285 pasien di suatu rumah sakit di Belanda, sebanyak 343 pasien IDO (87%) memerlukan

perawatan tinggal di rumah sakit setelah operasi. Lama rawat rata-rata sekitar 12 hari dengan

estimasi biaya per masuk rumah sakit sebesar € 9.016. Variabel independen yang terkait dengan

luaran terjadinya IDO adalah usia pasien >65 tahun (OR: 1,334; 95%CI: 1,036-1,720), penggunaan

antibiotik profilaksis (OR: 0,424; 95%CI: 0,344-0,537), dan pasien yang memiliki komorbid penyakit

kanker (OR: 2.050; 95%CI: 1.473-2.854). Selain itu, pasien yang menderita IDO menunjukkan lama

rawat yang berkepanjangan (HR: 0,742; 95% CI: 0,679-0,809).

Bagian kedua membahas secara mendalam pneumonia komuniti dari patogen penyebab

penyakit untuk menilai efektivitas terapi. Karakteristik kuman penyebab pneumonia dianalisis pada

Bab 4. Epidemiologi etiologi pneumonia komuniti dengan infeksi bakteri di Indonesia menunjukkan

seperlimanya merupakan multiple drug resistant organism (MDRO). Resistensi terhadap ciprofloxacin

dan amoxicillin/clavulanate mencapai 82%. Acinetobacter baumannii merupakan bakteri yang

dijumpai multiresisten terhadap antibiotik ini. Beberapa faktor seperti riwayat penggunaan

antibiotik yang tidak tepat dan tanpa resep dokter di komunitas dapat menyebabkan infeksi yang

multiresisten ini. Pasien dengan diabetes, penyakit jantung, kanker, gangguan ginjal, gangguan

liver dan gangguan imunitas juga menjadi faktor pencetus terjadinya pneumonia komuniti

dengan resistensi obat. Pada studi ini merekomendasikan cephalosporin generasi ketiga untuk

pedoman lokal karena tingkat sensitivitasnya masih tinggi (67-82%)

Risiko kematian pada kasus pneumonia tergantung dari tiga faktor, yakni kondisi pasien,

karakteristik bakteri, dan terapinya. Tingkat kematian meningkat secara bermakna pada pasien

dengan pneumonia komuniti yang berat dan pada pasien yang tidak menunjukkan perbaikan

setelah hari ke tiga. Sebagian besar pneumonia berat yang membutuhkan perawatan di rumah

sakit diderita oleh pasien laki-laki dengan usia 56 tahun keatas. Gejala yang sering timbul adalah

sesak napas (98%), demam (96%), batuk (74%), dan rasa tidak nyaman di dada (21%). Pasien dengan

kanker dan pasien dengan imunitas yang lemah lebih rentan jatuh dalam kondisi pneumonia

komuniti berat ini sehingga memerlukan observasi klinis yang ketat. Kecurigaan terhadap infeksi

MDRO dapat ditelusuri ketika pasien sudah mendapatkan terapi namun tidak ada perbaikan klinis,

misalnya pasien masih mengeluh sesak dan demam. Penilaian klinis 72 jam setelah pemberian

antibiotik empirik sebagai indikator penilaian terintegrasi yang dapat diandalkan untuk pasien

pneumonia komuniti yang dirawat di rumah sakit. Bersama pengukuran indeks keparahan

pneumonia (pneumonia severity index, PSI), penialian klinis 72 jam ini membantu memprediksi

luaran kematian.

Manfaat pemeriksaan kultur baik dari dahak maupun darah dalam pengobatan pneumonia

di rumah sakit dibahas dalam Bab 5. Sebelum ada hasil kultur, pasien diberi antibiotik sesuai

dengan pola kuman yang sering muncul sebagai penyebab pneumonia. Setelah ada hasil kultur

dari individu pasien, pemberian antibiotik dapat disesuaikan dengan hasil kultur tersebut dan

sensitivitasnya terhadap antibiotik. Kami evaluasi pasien saat pulang dari perawatan ke dalam 2

kategori yaitu sembuh atau meninggal. Kami menilai usia harapan hidup pasien yang pulih dari

perawatan.

Ringkasan

174

Pasien infeksi yang dirawat di rumah sakit pada umumnya memiliki gejala yang sudah serius

sehingga memerlukan observasi dan pengobatan di ruang rawat inap yang dikaji oleh tenaga

medis setiap hari. Hal yang menjadi fokus selama observasi di ruang rawat inap adalah pemilihan

antibiotik yang tepat sesuai dengan hasil kultur kuman dari dahak dan darah untuk mencegah

resistensi antibiotik lebih lanjut. Hasil penelitian menunjukkan bahwa pengobatan berdasarkan

evaluasi kultur yang disertai uji kerentanan terhadap antibiotik memberikan manfaat dalam hal

pengurangan biaya dan memperpanjang usia harapan hidup. Setelah hasil kultur kuman diserahkan

kepada dokter, pasien diberi antibiotik yang sesuai dengan kondisi pasien, kuman penyebab,

kekebalan antibiotik, dan biaya. Pemberian antibiotik dihentikan jika hasilnya negatif dan jika pada

diri pasien terdapat perbaikan klinis. Implementasi kultur kuman pada kasus pneumonia dapat

menghemat biaya sebesar US$1.067 (sekitar Rp.15 juta) per pasien dan meningkatkan harapan

hidup dalam semua kasus. Kultur kuman dan hasil kepekaan terhadap antibiotik pada pasien

yang dirawat di ruang intensif akan menghemat US$ 1.792 (sekitar Rp. 25 juta) per pasien dan

menambah usia harapan hidup lebih tinggi daripada tanpa kultur. Menariknya, pada kelompok

usia lanjut, kultur kuman membantu memberikan pilihan antibiotik yang tepat dan menghemat

biaya sebesar US$3.828 (sekitar Rp.53 juta) per pasien dan juga meningkatkan harapan hidup satu

tahun lebih lama daripada pasien yang tidak dievaluasi dengan kultur kuman.

Setelah mengetahui manfaat analisis kultur kuman terhadap biaya dan harapan hidup

pasien sebagaimana hasil penelitian di atas, maka implementasi analisis kultur kuman di

negara-negara berkembang dengan angka kejadian pneumonia yang tinggi, seperti Indonesia,

harus dipertimbangkan. Sejak 2014, Indonesia telah menerapkan sistem jaminan kesehatan

nasional (JKN) dalam mengelola pengeluaran terkait pembiayaan untuk pengobatan. Dengan

mempertimbangkan keterbatasan saat ini dalam pemberian antibiotik berbasis biaya pada pasien

pneumonia, maka kultur kuman dapat diterapkan untuk pasien pneumonia yang mendapat

perawatan di rumah sakit di Indonesia. Melalui analisis kultur kuman ini maka pemberian antibiotik

pada pengobatan pneumonia menjadi lebih tepat sehingga pada akhirnya dapat mengurangi

biaya perawatan dan meningkatkan usia harapan hidup terutama pada kasus pasien usia lanjut,

pasien dengan kondisi gangguan imun dan pasien dengan penyakit penyerta.

Bab 6 menganalisis beban biaya akibat infeksi sistemik sepsis dengan mempertimbangkan

infeksi fokal termasuk pneumonia dan infeksi pasca operasi seperti IDO. Penelitian ini dilakukan

secara integrasi terhadap luaran hidup dan kematian serta diproyeksikan pada sistem pembiayaan

asuransi di era universal health coverage (UHC) di Indonesia. Biaya rata-rata rumah sakit yang

dikeluarkan per pasien sepsis yang masih hidup dan yang meninggal masing-masing adalah

US$1.011 dan US$ 1.406. Beban nasional sepsis pada 100.000 pasien diperkirakan mencapai US$130

juta. Pasien sepsis dengan infeksi multifokal dan infeksi fokal tunggal infeksi saluran pernapasan

bawah diperkirakan sebagai dua peringkat teratas beban ekonomi tertinggi (US$48 juta dan US$33

juta, masing-masing, dalam 100.000 kasus). Sepsis dengan infeksi kardiovaskular diperkirakan

menjamin harga nasional tertinggi yang diusulkan untuk penggantian (US$4.256).

Mempertimbangkan mortalitas dan infeksi fokal dalam penilaian beban sepsis menjadi hal

yang penting karena ada perbedaan total biaya perawatan yang bermakna. Dalam konteks sumber

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175

daya yang terbatas seperti di Indonesia, di mana sistem UHC yang baru diimplementasikan,

penyediaan layanan kesehatan yang memadai memerlukan evaluasi dan perhitungan ulang paket

pembayaran untuk sepsis. Lebih jauh, dalam konteks kasus sepsis dengan infeksi multifokal dan

pneumonia harus dikategorikan sebagai kasus sepsis dengan beban tinggi, yang mencerminkan

contoh paling jelas yang memerlukan penyesuaian standard biaya nasional untuk klaim

pembayaran di layanan kesehatan sektor pemerintah dan swasta.

Ringkasan

176

ACKNOWLEDGMENT

Syukur Alhamdulillahirobbil’alamin. This achievement is a great gift from Alloh SWT to complete

my PhD. First of all, I would like to thank Mama Titiek Hariyati, and Papa alm. John Eddy Purba, for

your sincere prayer and endless love.

Being a single son, father of three children, husband, civil servant, an organizational leader, and a

clinical consultant, it would not have been possible to do a journey of PhD at three departments

without the presence of many people who have never stopped giving supports and sincere

prayers for my success.

I am very grateful to have dedicated promotors Prof. Maarten J. Postma and Prof. Alex W. Friedrich,

and my co-promotor Dr. Jan-Willem H. Dik to supervise me being an independent and innovative

researcher at the University of Groningen and the University Medical Center Groningen.

Dear respected promotor, Prof. Maarten J. Postma,

Wednesday, 20 May 2015, was the first day we met at the Faculty of Medicine, Universitas Gadjah

Mada, Yogyakarta. You gave an introductory lecture on pharmacoeconomics, afterward, with dr.

Jarir (Pak Itob), we discussed a research topic of antimicrobial stewardship for my PhD project. I

would like to express my deepest gratitude to you for giving me an opportunity as a PhD student

at the Unit of Pharmacotherapy, Pharmacoepidemiology, and Pharmacoeconomics, and at the

Department of Health Sciences, UMCG. When I came to you for the first meeting, I brought a

4-page proposal representing my ambitious work. After the meeting, I realized that I needed to

be wise and simply to see what was essential to be implemented for my country with a resource-

limited setting. I remember that you had a great dream of your Indonesian students someday

successfully having roles giving benefits to the community. During my PhD trajectory, you trained

me on how science works and how to be wise in respecting life. You were always there in the time

when I most needed you despite your busy schedule. You are an awesome teacher showing me

how to turn complicated concepts into a lot easier and simpler ones. Every meeting, I always got

clear explanations from you and afterward felt reassured. When I did not understand, you used

your whiteboard or took a paper to make a simulation. When I needed 15 minutes, you gave me

30-45 minutes. I enjoyed working with an open-minded person like you. You gave freedom of

thought so that a series of research topics could be packaged in something meaningful.

The invaluable experience was learning how to publish in Q1 journals. We have four published

articles in Q1 journals with minor revisions. By this, you taught me how to manage PhD time,

and then I could make it complete three years ten months with a total of six publications. Also, I

would like to acknowledge you for your willingness to become an adjunct professor and giving

some lectures at our campus of Nederlandsch Indishe Artsenschool (NIAS), at the Department

of Pharmacology and Therapy, Faculty of Medicine, Universitas Airlangga, Indonesia, in 2018-

2019. You showed me the awesome relationship between a supervisor and a promovendus. I

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177

am grateful that you taught me how to think comprehensively, logically, critically, not only in the

academic field but also at a more personal level, including how to manage time for relaxation.

You are such a multitalented teacher and thank you for playing squash and swimming with me.

Absolutely, it was the valuable moments, and I realize how lucky I was to work with you. For all of

this, I would like to send my gratitude and looking forward to seeing you in the future.

Dear respected promotor, Prof. Alex W. Friedrich,

We met for the first time at an online meeting via Webex on Tuesday, 20 October 2015. I would like

to express my deepest gratitude to you for giving me an opportunity as a PhD at the Department

of Medical Microbiology, UMCG. You have inspired me a lot with your passion for conducting

research and making a great collaboration. You always supported me to have collaboration

research and have some courses related to pharmacoeconomics, antimicrobial stewardship,

and infection prevention. I would like to thank you for your time to supervise me and having

constructive discussions. You always replied to my emails even you have busy schedules. When

you open your working desktop, I was honored to see the PowerPoint I presented at the first

meeting. With this, you made me motivated to do my PhD on the schedule. From you, I have

learnt much about clinical microbiology, antimicrobial resistance, and also how to make a vast

network.

Dear respected co-promotor, Jan-Willem Hendrik Dik

On Wednesday, 4 May 2016, after I had a meeting with the international office staff, I came to you

at the Department of Medical Microbiology (MMB), UMCG, to start my PhD journey. You showed

my first place at the office 2.056 deBrug with great friends: Erley, Maria, Mart, Rendy, Ana Carolina,

Huub, Ilona, and Jelte. At the moment, you were busy finalizing your thesis. After you achieved

your PhD, you have a career in Amsterdam. Even though you were not in Groningen, you always

made a regular monthly meeting with me at UMCG to discuss my PhD progress, to clarify the data,

to validate the research input, and to see any possible solutions for the difficulties during the work.

I felt how lucky I was to work with you, who could make a bridge between pharmacoeconomics

and microbiology. I would like to express my deepest gratitude to you for your supports, time,

hope, countless discussions, and wise advice throughout all the phases of my PhD.

I would like to acknowledge the reading committee: Prof. Kuntaman, dr., MS., Sp.MK(K), Prof. B.

Wilffert, and Prof. J.C. Wilschut, for willing to read and assess this thesis, and also many thanks for

considering me continuing the next steps to have a defense.

To my friendly paranymphs, Erley and Rifqi. Thank you very much for your contributions to

prepare all of the defense-related issues. You are so very organized people to make my defense

memorable. I highly appreciate it. Also, I would like to thank Mas Joko and Mas Deni for helping

me and the paranymphs to make my big day held successfully.

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178

I would especially like to acknowledge the Directorate General of Resources for Science, Technology

and Higher Education (DIKTI), Ministry of Research, Technology, and Higher Education, Republic

of Indonesia, for the financial support. To Prof. dr. Ali Ghufron Mukti, I would like to thank you for

your supports and prayer. To BPPLN DIKTI staff: alm. Bu Fine Resyalia, Bu Anis Apriliawati, Septian

Maryanto, Pak Sabar, and Pak Pujianto, I would like to thank you for assisting and relieving me from

all administration hurdles, and for your constant support during my PhD.

I would like to thank all colleagues and friends from the Unit of Global Health, Department of

Health Sciences: Simon van der Pol, Simon van der Schans, Abrham Wondimu Dagne, Mb. Afifah

Machlaurin, Tanja Fens, Jurjen van der Schans, Mas M Rifqi Rokhman, Mb. Ajeng Viska Icanervilia,

Mas Angga Prawira Kautsar, Kang Deni Iskandar, and Jap for friendship, cooperation, research

collaboration, and all technical and non-technical supports. Working at office 615 was fantastic. It

was a very convenient office to talk, to discuss, and to share knowledge. I felt welcome anytime, so

I had a good time to finalize my thesis. We will certainly keep in touch. I would like to send a special

word of thankfulness to Simon van der Pol for organizing ISPOR students, solving non-academic

matters, and your kind advice about the way to assess life-expectancy in cost-effectiveness

analyses.

Also, I would like to thank all people in the Department of Health Sciences: Janneke, Obbe, Prof.

Menno, Prof. Sandra, Femke, Patricia, Lindy, Matheus, Joke, Nicole, Loes, Alex, Pepijn, Haltze, Jitse,

Andrea, Jaap, Kor Brongers, Gabriel, Harriet, Tialda, Jelle, Janne, Regien, Yuwei, Lotte, Siobhan,

Henk-Jan, Lisette, Carin, Janine, Joyce. The most exciting thing was we have fruit breaks, outings,

drinks and a small birthday party for everyone. The research topic in this department was very

diverse and dynamic. I also would like to thank Josue Almansa Ortiz for checking the statistic

results. A huge thank you to the secretaries: Janneke, Obbe, Rieta, and Hanneke for assisting and

relieving me from all administration hurdles. To Mb. Hana, thank you for sharing stories, jokes, and

tips for staying in Holland, always be healthy and take care.

To my kindly MMB friends: Erley, Maria, Mart, Rendy, Ana Carolina, Huub, Ilona, Natacha, Christina,

Giuseppe, Jelte, Leonard, Hayley, Nilay, Christian, Matthijs, Henry, Linda, Silvia, Paola, Prof. Bhanu,

Ieneke, Mathilde, Sigrid, Monika, Caroline, Judith, Henk, and Ank, I would like to thank you for

having lunch together, celebrating a new publication, and sharing happiness during my years

of working at UMCG. We will certainly keep in touch. A special thanks to Linda, Erley, Maria, Ana,

Christina, and Christian, you always motivate and support me during my PhD. I am thankful for

your time to talk and discuss everything with you. Also, to Christian, thank you for involving in

the SSI study and helping me to understand using R. To Henk and Ank, I would like to express

my acknowledgment for all of your helps to handle administration issues and for sharing non-

academic matters. I wish you all the best in life and looking forward to seeing you in the future.

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I would like to thank all colleagues and friends from the Unit of Pharmacotherapy,

Pharmacoepidemiology, and Pharmacoeconomics: Prof. Bob, Prof. Elko, Jannie, Abrham, Mb.

Ira Sianturi, Tanja, Mas Fajri, Mas Ivan, Mas Akbar, Christian, Jurjen, Eva, Taichi, Pepijn, Mb. Neily,

Pieter, Ury, Mb. Tia, Mb. Lusi, Qi, Mas Riswandy, Mas Didik, Mb. Sofa, Mb. Doti, Aizati, Atiqul, and

Bert. You made me feel welcome in working at the office. Thank you for lunch together and

sharing knowledge. We will certainly keep in touch. A special thank you to the secretary, Jannie

Schoonveld, for all arrangements you had made for me.

To my respected teacher from Universitas Gadjah Mada: alm Prof. Iwan Dwiprahasto, Prof. Mustofa,

dr. Indwiani Astuti, Bu Erna, and Pak Jarir, I would like to express an enormous thank you for your

kindness and attention. To dr. Jarir, you were my favorite teacher from Yogyakarta. I cannot show

how much my gratitude is with your attention giving from I did my master up to now I did my

PhD. I am very grateful to have a teacher like you, so smart and a very nice person. You taught

me pharmacoeconomics and also introduced me to Prof. Postma. Again, thank you very much for

your kindness and all your supports.

Dear Ury, thank you very much for your everlasting friendship and togetherness. I have known

you since we did a Master program in Yogyakarta. We had togetherness moments from doing

presentations, having discussions, and working in the lab. Besides academic matters, we had plenty

of experiences when we stayed in Yogyakarta. We had lunch, dinner, and time for swimming, and

traveling. Afterward, you continued your PhD at RuG, and then you introduced me to Prof. Postma.

Although your thesis had a different topic from mine, we conducted a nice study, and then finally,

we had one awesome paper published in a Q1 journal. I wish you a successful person and always

welcome when you visit Surabaya.

Dear Tim Zwaagstra and Renzo Tuinsma, thank you for your kindness and guidance during my

PhD period. You made links for research collaboration between Groningen and Surabaya. I believe

we will still keep in touch to implement the agreement for student and staff exchanges.

My special word of thankfulness goes to Mas Joko for everlasting friendship and togetherness. I

am grateful to know you. We met on the bus going to the Introductory PhD event. To me, you are

a nice brother and very helpful - no rejections from you when I need help. I would like to express

my gratitude to you for taking care of my children and my mother when I went abroad. Also, I

would say many thanks for jamaah sholat, togetherness involving in organizations, lunch together,

sharing your knowledge, time, patience, prayers, and all support that I can not mention one by

one. Also, to me, you are so cool. You have five children, and you will have finalized your PhD this

year. I wish you a successful PhD.

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180

To the awesome neighbors of BBBO families: Mas Joko & Mb Uci’s family, Mas Afif, Mas Ivan & Mb

Dita’s family, Mas Amak & Mb. Putri’s family, Mb. Icha & Mas Erdi’s family, Mas Agung & Mb Inna’s

family, Mas Riswandy & Mb Cici’s family, Mas Adyatmika & Mb Nuri’s family, Mas Romy’s family, Mas

Angga, Kang Deni, Mas Sem, Mb. Sarah, Mb. Zamrotul Izzah, Annas, Bayan, Malik, Mas Haris, Mas

Agung, Mb. Tania, Mb. Defa, Mb. Valina and Mas Aldo, Mb. Btari, and Mb. Afi for warmest welcome

and togetherness for sharing the happiness. I felt much at home because of the cozy atmosphere

and the lovely moments of togetherness with all of you and your children. Someday, we could

make silaturahim and BBQ again with our children in Indonesia, inshaAlloh. Also, Mas Alfian, thank

you for being part of Nieuwe Ebbingestraat 59b, and playing with Ahsan and Annisa. I wish you

successful people and see you in the future in Surabaya.

In particular, for my friends who involved in the Indonesia student association (Perhimpunan

Pelajar Indonesia - PPI) Groningen 2016-2017, especially for those participating in the Health

Division: Mb. Marina Ika Irianti, dr. Didin, dr. Salva, Mas Joko, Mas Didik, Ury, Mas Alfian, Mas

Riswandy, Mas Akbar, Mb. Sofa, Mas Ivan, Mb. Anggreni, Mas Frans Simanjuntak, Mb. Citra, Mb.

Amirah, Mas Yudi, Mas Ananditya (from the University of Wageningen), Mas Lukman, Mas Mikhael

Manurung (from Leiden University Medical Center - LUMC), as a coordinator for the division, I am

very thankful that we together successfully developed a proposal of some inputs for the new

Indonesian Universal Health Coverage (UHC) implementation. The proposal was granted by the

President of PPI Groningen (Mas Amak) and the coordinator of the division for strategic issues and

scientific study (Mb. Titissari). Also, I would like to thank Prof. Hartono, Prof. Ari Probandari, and

dr. Brian Wasita for participating in the Indonesian Science Café 2 at UMCG, where the meeting

focused on the health issues in the UHC era.

I am delighted to have an opportunity to be a leader of the organization of deGromiest from

2017 to 2018. I would like to thank all deGromiest staff: Mas Joko, Mb. Inna, Mb. Nuril, Mas Lathif.

To Kinderen deGromiest staff: Mb. Uchi, Mb. Amalina, Bu Rini, Mb. Monik, Mb. Irma, Mb. Nadia,

Mb. Sannya, Mb. Anisah, Mb. Arum, Mb. Ghina, and Retno, I would like to thank you very much

for being teachers and making creativities for kids that they had moments for interaction with

each other. Of course, they enjoyed learning Islam and Indonesian culture in Groningen. To Mas

Rai, Mas Ghozi, Mas Afif, I would like to thank you for coordinating the weekly meeting for tadarus

keliling and kultum – Darlingku. To Mas Jabbar, Mas Lathif, dr. Didin, Mas Akbar, and Mas. Habibie,

I would like to thank you very much for coordinating Sholat Jumat. To Mb. Yosi, Mb. Monik, Mas

Fajar, Mas Amak, Mas Lana, Mas Yudi, Mb. Sofa, Mas Azkario, and dr, Didin, I would like to thank

you for your supports to maintain the Buletin deGromiest I and II by providing update news and

inspiring stories with an attractive design. To Mas Yudi and Mas Azkario, I would like to thank you

for handling the website that gives fruitful information to the public. To Mas Agung, Mas Lathif,

Mas Panji, and Mas Joko, I would like to thank you for coordinating the events of Iedul Fitri, Halal bi

Halal, and Sholat Iedul Adha). To Mas Azka M, I would like to thank you for organizing deGromiest

visiting SGB Utrecht for KALAMI event. To Mas Ghozi, Mas Joko, Mas Haris, and Mas Ivan, I would

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181

like to thank you for coordinating deGromiest to have an initial draft of AD/ART. To Mas Ega, I

would like to say a special thank you for being a coordinator for Hajj together with seven families

and four kids. In addition, I would like to thank Ust. Agus Suranto and Pak Said from EuroMuslim

Amsterdam for teaching us to do Umroh and Hajj and guiding during in Mecca and Madinah. Also,

many thanks to Ust Cholis and Ust. Eko for sharing knowledge and experience.

To other Indonesian friends: Pak Tatang & Bu Rohmah, Mas Auliya & Mb. Neily, Mb. Nur Qomariyah,

Mb. Ira, Mas Ronny Prabowo, Mb. Erna, Mas Azis & Mb. Amalina, Mas Bino & Mb. Susan, Mas Rully

& Mb. Intan, Mas Kuswanto & Mb. Fitria, Mas Hegar & Mb. Anisa, Bhimo, Deka, dr. Fundhy, dr.

Mahendra, Mas Ristiono & Mb. Afifah, Mas Yopi & Mb. Dewi, dr. Budi Darmawan & Mb. Nonny, Mas

Adhi, Mas Azzam & Mb. Ghina, Ust. Naufal & Mb. Moza, Mas Ali Syari’ati & Mb. Liany, Mas Harry

& Mb. Fiska, Mas Dimas & Mb. Anya, Mas Ali Abdurrahman & Mb. Yosi, Mas Fean, Mas Tri Efriadi,

Mb. Masyitha, Mas Aunurrofik, Mas Prayoga, Mas Yusran, Mas Zaki & Mb. Nadia, Ust. Fika & Mb.

Nisak, Mb. Pretty, Mas Gerry, Mb. Endira, Mas Zaenal & Mb. Ayu, Pak Asmoro & Bu Rini, Mas Cholis

& Mb. Jean, Mb. Inda & Mas Feri, Mas Romy & Mb. Arlina, Mas Kadek & Mb. Laksmi, Mas Habibie

& Mb. Ma’wa, Mas Krisna & Mb. Icha, Mas Adityo, Mas Mega & Mb. Irma, Mas Lathief & Mb. Septi,

Mas Lana & Mb. Arum, Mas Azka Mujib & Mb. Aidina, Mas Surya & Mb. Yasaroh, Mas Ade & Mb.

Cika, Mas Akbar & Mb. Andis, and to all Indonesian seniors: Uwak Asiyah, Om Meno and Bachtiar

in Delfzijl; Om Archi and Tante Mary in Robijnstraat; Budhe Arie and Om Herman in Hoogezand;

Budhe Nunung, Pakdhe Said and Vincent in Bankastraat; Mb. Hellen’s family, Bu Elvira’s family, Bu

Nur’s family, Bu Roos’ family, Mb. Ade & Mas Joesoef, Mb. Siti’s family, Mb. Atika and Salim’s family,

Mb. Eny’s family, Mb. Amalia’s family. Mb. Sindhu’s family, Mb. Ria’s family, Mb. Rani’s family, and

Om Dedi’s family in Amsterdam, I would like to many thanks for warm welcome and making

Netherlands more special to me. I felt at home having a huge family that I could find big supports

and help at any time.

To all co-authors: Maarten J. Postma, Alex W. Friedrich, Jan-Willem Dik, Nina Mariana, Gestina

Aliska, Sonny Hadi Wijaya, Riyanti Retno Wulandari, Usman Hadi, Hamzah, Cahyo Wibisono

Nugroho Jurjen van der Schans, Didik Setiawan, Erik Bathoorn, Christian F Luz, BTF van der Gun,

Purwantyastuti, Armen Muchtar, Laksmi Wulandari, Alfian Nur Rosyid, Priyo Budi Purwono, Tjip

S van der Werf, Annette d’Arqom, and my hard-working students: Rahmat Sayyid Zharfan, and

Ahmad Lukman Hakim, I would like to many thanks for your thoughtful guidance on my papers

and the great collaborations. Moreover, the deepest gratitude to Prof. Tjip S van der Werf for

countless constructive discussions and sharing your exciting journey from Indonesia. A special

word of thankfulness to Pak Hendro Suprayogi and Bu Rosita Prananingtias, who managed all the

data collections. Also, I would like to express many thanks to everyone involved in my study from

Prof. Dr. Sulianti Saroso Hospital, Dr. Soetomo General Academic Hospital in Surabaya, Universitas

Airlangga Hospital, Dr. M. Djamil Hospital. I hope that all what we did will have plenty of fruitful

contributions and benefits to the community.

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182

To people working at the Drug and Therapeutics Committee (Komite Farmasi dan Terapi) Dr.

Soetomo Hospital: Dr. Hamzah, dr. Fendik, Prof. Kuntaman, apt. Ali, apt. Yahya, apt. Woro, Bu Hermin,

Bu Nur, Pak Yuwono, and Mb. Nia, I would like to thank all of you for supporting me in doing

research for my PhD. I would like to appreciate all of you. Although I was doing a study abroad,

we could stay in touch. Then, we successfully developed a national guideline for antimicrobial

stewardship implementation in the hospital.

To WHO secretariat and consultants who involved in the technical expert working on essential

medicine list (EML) for surgical antibiotic prophylaxis: Prof. Benedetta Allegranzi, Dr. Peter Bischoff,

Mr. Carl Coleman, Jerome Delauzun, Dr. Benedikt Huttner, Dr. Stijn de Jonge, Dr. Nicola Margrini,

and Mr. Paul Roger, Pilar Ramin-Prado (WHO Pan American Office - PAHO), Prof. Dale W. Bratzler,

Prof. Hanan Balkhy, Prof. Adrian Brink, Dr. Adrian Brink, Dr. Nizam Damani, Prof. E. Patchen Dellinger,

Dr. Mazen Ferwana, Prof. Daniela Filipescu, Prof. Lindsay Grayson, Prof. Stephan Harbarth, Dr. Joost

Hopman, Prof. Shaheen Mehtar, Prof. Bisola Onajin Obembe, Dr. Leonardo Pagani, Dr. Giampietro

Pellizer, Prof. Evelina Tacconelli, I would like to express my gratitude to have a meeting with all of

you in Geneva. Afterward, we had dinner, and a moment to share all of your experiences handling

antimicrobial resistance.

I would like to thank all my respected teachers, my seniors, my colleagues from Universitas

Airlangga, especially for all people at the Department of Pharmacology and Therapy, Faculty of

Medicine, UNAIR: Prof. Achmad Basori, dr. Roostantia, dr. alm Moh Teguh Wahjudi, dr. Haryanto

Husein, drg. Indriyatni Uno, dr. Rahardjo, dr. Ramadhani, dr. alm. Sunarni Zakaria, apt. Nuraini

Farida, dr. Arifa Mustika, dr. Bambang Hermanto, dr. Widayat, dr. Endang Isbandiyati, apt. Abdul

Mughni, dr. Nurmawati Fatimah, dr. Ratna Sofaria Munir, dr. Maftuchah Rochmanti, dr. Sri, dr. M.

Fathul Qorib, dr. Yuani Setiawati, dr. Danti Nur Indiastuti, dr. Nurina Hasanatuludhiyah, dr. Annette

d’Arqom, dr. Maulana Antiyan Empitu, dr. Firas Farisi Alkaff, Bu Nana, Bu Tari, Pak Didik, Bu Erti, Pak

Joko, Pak Udin, Pak Bibit, for your supports and prayers. Also, I would like to express my deepest

gratitude to all of you for your understanding of allowing me to have school in Yogyakarta, Jakarta,

and Groningen. I wish you all the best in all of our steps forward. To the Rector of my home

university: Prof. Nasih, the Dean: Prof. Dr. Sutojo, dr., Sp.U(K), and other respected teachers: Prof.

Djoko Santoso, Sp.PD, Prof. Dr. David S. Perdanakusuma, dr., Sp.BP-RE(K), Prof. Dr. Budi Santoso, dr.,

Sp.OG (K), Prof. Dr. Ni Made, dr., MS., Sp.MK(K), and to all my respected teachers, I would like to

thank you for your supports and consideration. Also, to all people involving my success during my

PhD period: Bu Rini, Pak Fadli, Bu Nurul, Mb. Endah, Mb. Ella, Bu Peni, Bu Ani, Bu Dyah, and Bu Triana,

I would like to thank you for your supports and helps so that I did my PhD successfully.

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I would like to express my deepest gratitude to my beloved father, Papa alm. John Eddy Purba,

and my mother, Mama Titiek Hariyati for your endless prayer, love, and encouragement. To Papa, I

apologize for not being on your side when you had a difficult time. When I flew to Netherland to

start my PhD in May 2016, you looked strong and healthy. Four months later, you had experienced

with cancer, and you did not allow me to know your condition since you considered me focusing

on my study. Afterward, I had news that you fell into a serious critical state and had the last

breathing, but I was still in Groningen. I did not expect that the warmest hug at the airport in May

2016 was the last hug from you. I wish Alloh loves you and brings us together in His heaven. Untuk

Mama, terima kasih atas kasih sayang yang tidak pernah putus, doa yang sungguh-sungguh,

keikhlasan dan pengertiannya yang luar biasa, serta semua dukungan yang diberikan kepada

kami sekeluarga. Kelembutan tanganmu membuat Khairul yang kecil dulu telah tumbuh menjadi

seseorang yang haus akan ilmu. Mohon maaf jika selama ini saya sering jauh secara fisik. Terima

kasih telah merawat Papa hingga Papa sedo. Terima kasih juga telah memberi perhatian kasih

sayang kepada cucu. Patut mencontoh Mama yang sabar menghadapi realita, dan tidak pernah

putus asa dalam berdoa. Sekali lagi terima kasih banyak atas semuanya. Semoga Mama selalu

sehat, mendapat ridho dan keberkahan dari Alloh SWT. Untuk Ibu Unsidah, terima kasih atas doa,

kesabaran, dan dukungannya semoga Ibu selalu sehat dan mendapat ridho dan keberkahan dari

Alloh SWT.

Untuk kakakku tercinta, Mb. Inna, Mas Erman, Mb. Rini, Mas Yanto, Mb. Prapti, Mas Slamet, Mb.

Yani, Mas Nur, Mb. Wachid, Mb. Pipit, dan Mas Bhakti; adikku tercinta, Mifta dan Helga, dan juga

keponakanku: Mahren, Andre, Tiara, Vania, Akbar, Arif, Ahmad, Latif, and Ishom, terima kasih banyak

atas doa, tenaga, waktu, pikiran, keikhlasan, kesabaran, dan dukungannya yang diberikan kepada

saya dan keluarga saya. Terima kasih sebesar-besarnya telah merawat Papa di rumah, rumah sakit,

dan mendampingi saat-saat sulitnya. Terima kasih juga menjaga Mama, Ibu, Retno, Annisa and

Ahsan selama saya mengambil studi di Jogja, Jakarta, dan Groningen, serta mengajak jalan-jalan

Annisa dan Ahsan, menghibur dan membawa kehangatan serta kebersamaan keluarga. Semoga

Alloh SWT memberikan limpahan kasih sayang, keberkahan, kesehatan dan kesuksesan untuk kita

semua.

A very special appreciation and many great thanks go to my dearest wife, Retno, for your endless

love, patience, thoughts, sincere prayers, and time to be part of my life. Also, many thanks for

understanding my complicated rhythms and always being on my side during my difficult time. I

am thankful for all of your kindness and smiles that treated my fatigue. It was very often to leave

you since I had to have higher education qualifications for my further career. It was not easy

for you to take care of our children by yourself in Surabaya for six years: two years when I did a

master in Yogyakarta, three years when I did a specialization program in Jakarta, and one year

when I did my first year of PhD in Groningen. Absolutely, your presence made me motivated to

do PhD. Thank you so much for hearing me at any time I need. Thanks for leaving your job and

choosing togetherness with me and with our children: Annisa, Ahsan, and Mafaza, to live in the

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place with an extreme climate, freezing in the winter (we believe that there is nothing above

Groningen); and in the summer, we had almost 19 hours fasting in the Month of Ramadhan. After

a year in Groningen, surprisingly, you made my life colorful to have a newborn, baby Mafaza, in

Groningen. When you were going to deliver, I brought you with all things of your and baby’s

needs to UMCG by bike in the morning. Of course, it was such an amazing and unforgettable

moment. Unbelievable, we have made many stories that can tell our children someday. To my

children: Annisa, Ahsan, and Mafaza, many thanks for giving supports and endless sincere prayer

to your parents. To Annisa and Ahsan, you successfully managed your study at Dutch basis school

in the morning and Netherland-Indonesia elementary school in the evening. To all of my children,

I wish all of you a successful person with an excellent attitude. We wish Alloh gives His Rahmat and

Blessing to all of us and keeps our hearts to everlasting love.

I realize that a lot of people involving in my success. To anyone who is not mentioned in this part,

I would like to express my deepest gratitude, and I wish you all the best.

Thank you! Bedankt! Terima Kasih!

Groningen, February 2020

Abdul Khairul Rizki Purba

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CURRICULUM VITAE

Abdul Khairul Rizki Purba, dr., M.Sc., Sp.FK-Clin. Pharmacologist

Nationality: Indonesian

Email: khairul_purba@fk.unair.ac.id / a.k.r.purba@umcg.nl

Higher education Year

Medical Doctor (dr.) Faculty of Medicine, Universitas Airlangga, Indonesia

Sep 2001 – Dec 2007

M.Sc in Pharmacology and Therapy

Faculty of Medicine, Universitas Gadjah Mada, Indonesia

Sep 2010 – Jun 2012

Sp.FK – Clinical pharmacologist

Universitas Indonesia, Indonesia (Ciptomangunkusumo, Harapan Kita, and Persahabatan Hospitals)

Jul 2012 – Jun 2015

Non-degree Center for Clinical and Translational Research, Faculty of Medicine, Kyushu University, Japan

Sep – Oct 2015

PhD in Medical Sciences University Medical Center Groningen, University of Groningen, the Netherlands

May 2016 – Feb 2020

Professional membershipsIndonesian Medical Doctor Association (IDI)Indonesian Pharmacologist Association (IKAFI)Indonesian Clinical Pharmacologist Association (PERDAFKI)European Society Clinical Microbiology and Infectious Disease (ESCMID-Europe)International society for Pharmacoeconomics and outcome research (ISPOR-Europe)

Courses and scientific meetings Place Year

ISPOR Europe 2019 Copenhagen, Denmark 2019

Understanding survival modelling with application to health technology assessments (HTA)

Copenhagen, Denmark 2019

Bayesian network meta-analysis – Cochrane-Netherlands

Utrecht, the Netherlands 2019

Fitting the structure to the task: Choosing the right dynamic simulation model to inform decisions about health care

Copenhagen, Denmark 2019

Value of information analyses Copenhagen, Denmark 2019

Mapping to estimate utility values from non-preference based outcome measures

Copenhagen, Denmark 2019

Curriculum Vitae

186

Courses and scientific meetings Place Year

Pharmacokinetics and pharmacodynamics of antibiotics: optimal-dose achievement

Rotterdam, the Netherlands 2019

Advance course of Statistical Methods in Economic Evaluation for health technology assessments (HTA)

York University, UK 2019

Foundation course of Statistical Methods in Economic Evaluation for health technology assessments (HTA)

York University, UK 2019

Use of propensity scores in observational studies of treatment effects

Copenhagen, Denmark 2019

Advanced methods for addressing selection bias in real-world effectiveness and cost-effectiveness studies

Copenhagen, Denmark 2019

Writing a thesis using word University of Groningen, the Netherlands

2018

ISPOR Europe 2018 Barcelona, Spain 2018

Pharmacoeconomic Modelling-Application Barcelona, Spain 2018

National Seminar of Health Technology Assessment in Drug Use (as a speaker)

Universitas Airlangga, Indonesia

2018

1st International Scientific Meeting on Clinical Microbiology and Infectious Disease (as a speaker)

Universitas Airlangga, Indonesia

2018

Pharmacokinetics and pharmacodynamics of antibiotics (as a speaker)

Indonesian Society of Medical Microbiology and Infectious Disease

2018

Bayesian Analysis for HTA – Overview and Applications ISPOR-Barcelona, Spain 2018

Advanced Methods for Cost-Effectiveness Analysis: Meeting Decision Makers’ Requirements

York University, UK 2018

Modelling in Health Technology Assessment UMCG, the Netherlands 2018

Phase II and III clinical trials GSMS, UMCG, the Netherlands 2018

Antimicrobial stewardship: principles and practice Istanbul, Turkey 2017

Systematic reviews and meta-analysis GSMS, UMCG, the Netherlands 2018

R statistics GSMS, UMCG, the Netherlands 2018

Medical statistics GSMS, UMCG, the Netherlands 2018

Excel Advanced RuG, the Netherlands 2017

Advanced in genetic epidemiology GSMS, UMCG, the Netherlands 2017

Epidemiology and applied statistics GSMS, UMCG, the Netherlands 2017

Advanced Pharmaco-epidemiology GSMS, UMCG, the Netherlands 2017

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Courses and scientific meetings Place Year

Good research practice: GCP/GLP GSMS, UMCG, the Netherlands 2017

Managing your PhD GSMS, UMCG, the Netherlands 2017

Ethics of Research and Scientific Integrity for Researchers

GSMS, UMCG, the Netherlands 2017

Pharmacoeconomics course GSMS, UMCG, the Netherlands 2016

Modelling in Health Technology Assessment GSMS, UMCG, the Netherlands 2016

Pubmed and Embase search strategy for reviews CMB, UMCG, the Netherlands 2016

RefWorks CMB, UMCG, the Netherlands 2016

Pharmacovigilance and Monitoring of side effects in hospitals

World Health Organization (WHO) and BPOM

2015

Sosialisasi Gerakan Masyarakat Cerdas Menggunaan Obat (GeMa CerMat)

Minstry of Health, Republic of Indonesia

2015

Essential pain management Indonesian Medical Doctor Association

2014

TLC fingerprint Biofarmaka, IPB Bogor 2014

Bioavailability and bioequivalence (BaBe) Universitas Indonesia 2014

Pharmacokinetics & pharmacodynamics modeling: concept and application of antibiotic use in infection management

UNAIR and Erasmus Medical Center, Rotterdam

2014

Course on publishing in international journals Universitas Airlangga and Erasmus University

2013

Western blot Cancer chemoprevention research center, UGM

2012

Course and Workshop Good Clinical Practice Research Hospital for Tropical-Infectious Disease, IASMED and Universitas Airlangga

2012

Workshop on Introduction to Clinical Research Institute of Tropical Medicine, Antwerp, Belgium

2012

Frontier in Biomedical Science: From Gene to Applications

Universitas Gadjah Mada 2011

Immunopharmacology Indonesian Pharmacologist Association

2010

TOT tutor & instructor Problem Based Learning Universitas Airlangga 2010

Applied Approach plus Universitas Airlangga 2009

Curriculum Vitae

188

Courses and scientific meetings Place Year

Technology of immunization for disease of infectious and cancer

Universitas Airlangga & Dutch foundation

2009

Neonates Life Support Hospital of DR. Soetomo, Surabaya

2008

Advanced Cardiac Life Support Indonesian Heart Association 2008

Microsoft Office Community based Training and Learning Center

2008

Training for occupational health Yogyakarta 2008

Primary Trauma Care Management World Federation of Societies of Anaesthesiologists

2005

Integrated Management in Cancer PKTP 2004

Addendum

189

LIST OF PUBLICATIONS

Purba AKR, Setiawan D, Bathoorn E, Postma MJ, Dik JW, Friedrich AW. Prevention of surgical site infections: A systematic review of cost analyses in the use of prophylactic antibiotics. Frontiers in Pharmacology, 2018; 9(776): 1-18. doi: 10.3389/fphar.2018.00776. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060435/.

Purba AK, Ascobat P, Muchtar A, Wulandari L, Rosyid AN, Purwono PB, van der Werf TS, Friedrich AW, Postma MJ. Multidrug-resistant infections among hospitalized adults with community-acquired pneumonia in an Indonesian tertiary referral hospital. Infect Drug Resist, 2019(12): 3663-3675. doi: 10.2174/IDR.S217842. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883944/

Purba AKR, Ascobat P, Muchtar A, Wulandari L, Dik JW, d’Arqom A, Postma MJ. Cost-effectiveness of culture-based versus empirical antibiotic treatment for hospitalized adults with community-acquired pneumonia in Indonesia: A real-world patient-database study. Clinicoecon Outcomes Res, 2019(11): 729-739. doi: 10.2147/CEOR.S224619. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890194/

Zarfan RS, Hakim AL, Purba AKR, Sulistiawan SS, Soemedi BP. Albumin, Leukosit, and Protombin as Predictors of Sepsis Mortality among Adult Patients in Soetomo General Hospital, Surabaya, Indonesia. Indonesian Journal of Anaesthesiology and Reanimation, 2019; 1(1): 8-12. doi: 10.20473/ijar.V1I12019.8-12. https://e-journal.unair.ac.id/IJAR/article/view/12705

Arifin B, Probandari A, Purba AKR, Perwitasari DA, Schuiling-Veninga CCM, Atthobari J, Krabbe PFM, Postma MJ. ‘Diabetes is a gift from God’ a qualitative study coping with diabetes distress by Indonesian outpatients. Qual Life Res, 2020: 29(1): 109-125. doi:10.1007/s11136-019-02299-2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962255/.

Purba AKR, Mariana N, Aliska G, et al. The burden and costs of sepsis and reimbursement of its treatment in a developing country: An observational study on focal infections in Indonesia [published online ahead of print, 2020 May 5]. Int J Infect Dis. 2020;S1201-9712(20)30294-0. doi:10.1016/j.ijid.2020.04.075. https://pubmed.ncbi.nlm.nih.gov/32387377/

Purba AKR, Mariana N, Aliska G, Wulandari RR, Wijaya SH, Postma MJ. National burden of sepsis in Indonesia: An analysis based on focal infections. Value in health, 2019(22): Supplement 3, page S655. https://doi.org/10.1016/j.jval.2019.09.1339

A.K.R. Purba, P. Purwantyastuti, A. Muchtar, L. Wulandari, A. d’Arqom, J.W.H. Dik, M.J. Postma, PIN131 Cost-effectiveness of culture-based versus empirical antibiotic treatment for hospitalized adults with community-acquired pneumonia in indonesia: a real-world patient-database study, Value in Health, Vol. 22, Supplement 3, 2019, Page S660, https://doi.org/10.1016/j.jval.2019.09.1372.

Purba AKR. Resistensi obat pada kasus pneumonia (Drug resistance among pneumonia cases). December 2019. UNAIR news. http://news.unair.ac.id/2019/12/19/resistensi-obat-pada-kasus-pneumonia/.

Purba AKR. Manfaat pemeriksaan kultur kuman pada pasien pneumonia (The benefits of specimen culture among pneumonia patients). UNAIR news. December 2019. http://news.unair.ac.id/2019/12/19/manfaat-pemeriksaan-kultur-kuman-pada-pasien-pneumonia/

List of Publications

190

BIOGRAPHY

Abdul Khairul Rizki Purba was born on February 22nd, 1984, in

Surabaya, Indonesia. He obtained his Medical Doctor (MD) or a

title of “dokter” in 2007 from the Faculty of Medicine, Universitas

Airlangga (formerly named Nederlandsch Indishe Artsenschool),

with an internship program at Dr. Soetomo General and Academic

Hospital, Surabaya. After graduation, he joined the Hospital of

Petrokimia in Gresik as a physician in the Department of Emergency.

In December 2008, he went back to his almamater of Faculty of

Medicine, Universitas Airlangga, as a lecturer at the Department

of Pharmacology and Therapy. In 2010, he started his Master at

the Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, and

finished it cum laude with research in drug-drug interactions in

cancer treatments under supervisors Prof. Dr. Mustofa, Apt and Dr. Med. Indwiani Astuti, dr. After

graduation from his Master, he wrote a book with his colleagues about drug-drug interactions.

In 2012, he moved to Jakarta to do his medical specialization in clinical pharmacology in Universitas

Indonesia with an internship program in the National Center of General Hospital of Cipto

Mangunkusomo, Jakarta. He conducted clinical research with a topic of pharmacoeconomics of

empirical antibiotic treatments under supervisors Prof. Dr. Purwantyastuti, dr., M.Sc., Sp.FK and

Prof. Dr. Armen Muchtar, dr., MS., Sp.FK. In 2015, he joined in the Drug and Therapeutics Committee

(Komite Farmasi dan Terapi) as a clinical pharmacologist at General Hospital of Dr. Soetomo in

Surabaya.

In 2015, he decided to pursue his doctoral degree in the Netherlands under a grant from the

Directorate General of Higher Education, Ministry of National Education, Republic of Indonesia.

From May 3rd 2016, he officially started working his Ph.D. research at University Medical Center

Groningen (UMCG), University of Groningen. His Ph.D. trajectory was performed with a series

of research focused on pharmacoeconomics of antibiotic treatments under the supervision

of Prof. Dr. Maarten J. Postma and Prof. Dr. Alex W. Friedrich, together with Jan-Willem H. Dik,

Ph.D. The research was conducted under Groningen University Institute for Drug Exploration

(GUIDE) with collaboration between the Department of Medical Microbiology (MMB); the Unit of

Pharmacotherapy, PharmacoEpidemiology, and PharmacoEconomics, Department of Pharmacy;

and the Unit of Global Health, Department of Health Sciences, UMCG. He finished it in February

2020, with a defence on July 8th, 2020. After finalizing his Ph.D., he will continue working as a

lecturer, researcher, and clinical pharmacologist at the Department of Pharmacology and Therapy,

Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia. Also, he has been involving in the

expert working on the Essential Medicine List (EML) for surgical antibiotic prophylaxis in World

Health Organization (WHO), Geneva, Switzerland, since December 2018.

Addendum