Inadequate Empiric Antibiotic Therapy · EMPIRIC ANTIBIOTIC THERAPY ... the treatment of acquired...

Inadequate Empiric Antibiotic Therapy among Canadian

Hospitalized Solid-Organ Transplant Patients:

Incidence and Impact on Hospital Mortality

by

Bassem Hamandi

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Pharmaceutical Sciences

University of Toronto

© Copyright by Bassem Hamandi (2008)

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Inadequate Empiric Antibiotic Therapy among Canadian Hospitalized Solid-Organ Transplant Patients: Incidence and Impact on Hospital Mortality

Master of Science (2008)

Bassem Hamandi

Graduate Department of Pharmaceutical Sciences, University of Toronto

ABSTRACT Background: The incidence of inadequate empiric antibiotic therapy (IET) and its clinical

importance as a risk factor for hospital mortality in Canadian solid-organ transplant patients

remains unknown.

Methods: This retrospective cohort study evaluated all patients admitted to a transplant unit

from May/2002-April/2004. Therapy was considered adequate when the organism cultured was

found to be susceptible to an antibiotic administered within 24 hours of the index sample

collection time. Univariate and multivariate regression analyses were conducted to determine

associations between potential determinants, IET, and mortality.

Results: IET was administered in 169/312 (54%) transplant patients. Regression analysis

demonstrated that an increasing duration of IET (adjusted OR at 24h, 1.33; p < 0.001), ICU-

associated infections (adjusted OR, 6.27; p < 0.001), prior antibiotic use (adjusted OR, 3.56; p =

0.004), and increasing APACHE-II scores (adjusted OR, 1.26; p < 0.001), were independent

determinants of hospital mortality.

Conclusions: IET is common and appears to be an important determinant of hospital mortality in

the Canadian transplant population.

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ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr. Anne Holbrook for her support and guidance during my

graduate studies. Her constructive criticism and comments from the initial conception to the end

of this work were highly appreciated. Thank you to my committee members, Dr. James Brunton

and Dr. Manny Papadimitropoulos for their expert opinions, advice, and invaluable feedback at

our meetings. Dr. Michael Gardam’s expertise and feedback were much appreciated. I would

also like to thank Dr. Lehana Thabane for his statistical insight and advice. A special thanks to

Dr. Atul Humar for his unique expertise in transplant infectious diseases.

I am indebted to Mr. Gary Wong for his support, suggestions, and feedback during the early

planning and throughout the implementation of the study. I would also like to thank my

colleagues in the Pharmacy Department at The University Health Network for their help and

support.

Finally, I would like to thank my family and friends for their encouragement and support

throughout my studies.

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TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................II

ACKNOWLEDGEMENTS ..................................................................................................................................... III

LIST OF ABBREVIATIONS .................................................................................................................................. VI

LIST OF TABLES...................................................................................................................................................VII

LIST OF FIGURES............................................................................................................................................... VIII

LIST OF APPENDICES .......................................................................................................................................... IX

1.0 INTRODUCTION........................................................................................................................................1

1.1 STATEMENT OF THE PROBLEM.....................................................................................................................2 1.1.1 Infection after Solid-Organ Transplantation.............................................................................................2 1.1.2 Antibiotic Resistance .................................................................................................................................3 1.1.3 Inadequate Empiric Antibiotic Therapy ....................................................................................................5

1.2 PURPOSE......................................................................................................................................................7 1.2.1 Objective 1.................................................................................................................................................7 1.2.2 Objective 2.................................................................................................................................................7

1.3 STATEMENT OF RESEARCH HYPOTHESIS .....................................................................................................7 1.4 RATIONALE FOR HYPOTHESIS .....................................................................................................................8 1.5 REVIEW OF THE LITERATURE ......................................................................................................................8

2.0 METHODS .................................................................................................................................................25

2.1 STUDY LOCATION AND POPULATION.........................................................................................................25 2.1.1 Inclusion Criteria ....................................................................................................................................25 2.1.2 Exclusion Criteria ...................................................................................................................................25

2.2 STUDY DESIGN ..........................................................................................................................................25 2.3 MICROBIOLOGY.........................................................................................................................................26 2.4 DEFINITIONS..............................................................................................................................................27

2.4.1 Infection vs. Contamination vs. Colonization..........................................................................................27 2.4.2 Infectious Episodes..................................................................................................................................28 2.4.3 Healthcare vs. ICU vs. Community Associated Infections.......................................................................28 2.4.4 Primary Site of Infection..........................................................................................................................28 2.4.5 Empiric Antibiotic Therapy .....................................................................................................................29 2.4.6 Adequate vs. Inadequate Empiric Antibiotic Therapy .............................................................................29 2.4.7 Previous Antibiotic Therapy....................................................................................................................30 2.4.8 Previous Graft Rejection and Immunosuppressant Use ..........................................................................30 2.4.9 Multi-Drug Resistance.............................................................................................................................31

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2.5 OUTCOMES ................................................................................................................................................31 2.6 STATISTICAL ANALYSIS ............................................................................................................................31

3.0 RESULTS....................................................................................................................................................33

3.1 PATIENTS ..................................................................................................................................................33 3.2 INADEQUATE EMPIRIC ANTIBIOTIC THERAPY ...........................................................................................33 3.3 CHARACTERISTICS RELATED TO HOSPITAL MORTALITY...........................................................................40 3.4 LOGISTIC REGRESSION ANALYSIS .............................................................................................................43 3.5 SECONDARY OUTCOMES ...........................................................................................................................44

4.0 DISCUSSION .............................................................................................................................................45

4.1 STUDY LIMITATIONS .................................................................................................................................48 4.2 IMPLICATIONS OF INADEQUATE EMPIRIC THERAPY...................................................................................49

5.0 CONCLUSIONS.........................................................................................................................................51

6.0 REFERENCES ...........................................................................................................................................52

7.0 PUBLICATIONS AND ABSTRACTS TO DATE ..................................................................................58

8.0 APPENDICES ............................................................................................................................................59

APPENDIX I – LITERATURE REVIEW SEARCH STRATEGY .........................................................................................59 APPENDIX II – RAW DATA.......................................................................................................................................60 APPENDIX III – MULTIVARIATE LOGISTIC REGRESSION MODELLING ......................................................................66

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LIST OF ABBREVIATIONS

AET Adequate empiric antibiotic therapy

APACHE Acute Physiology and Chronic Health Evaluation

ATG Anti-thymocyte globulin

BAL Bronchoalveolar lavage

CDC Centers for Disease Control

CLSI Clinical and Laboratory Standards Institute

CMV Cytomegalovirus

CNS Coagulase-negative staphylococci

CVC Central venous catheter

HAI Healthcare-associated infections

ICU Intensive care unit

IET Inadequate empiric antibiotic therapy

IL-2 Interleukin-2

MDR Multi-drug resistant

MIC Minimum inhibitory concentration

MOT Multi-organ transplant

MRSA Methicillin-resistant Staphylococcus aureus

NNIS National Nosocomial Infections Surveillance

OR Odds ratio

RR Relative risk

SOT Solid-organ transplant

UTI Urinary tract infection

VAP Ventilator-associated pneumonia

VRE Vancomycin-resistant enterococci

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LIST OF TABLES

TABLE 1. A SUMMARY OF 22 NON-RANDOMIZED COMPARATIVE COHORT STUDIES ASSESSING THE ASSOCIATION

BETWEEN IET AND MORTALITY. .........................................................................................................................11

TABLE 2. CHARACTERISTICS OF PATIENTS RECEIVING ADEQUATE OR INADEQUATE EMPIRIC ANTIBIOTIC THERAPY .....34

TABLE 3. CHARACTERISTICS OF TRUE INFECTIOUS EPISODES TREATED WITH ADEQUATE OR INADEQUATE EMPIRIC

ANTIBIOTIC THERAPY ..........................................................................................................................................36

TABLE 4. ORGANISMS CULTURED FROM PATIENTS WITH INADEQUATELY TREATED INFECTIOUS EPISODES ..................38

TABLE 5. MULTI-DRUG RESISTANT ORGANISMS CULTURED AMONG PATIENTS RECEIVING ADEQUATE OR INADEQUATE

EMPIRIC ANTIBIOTIC THERAPY ............................................................................................................................39

TABLE 6. INTRA-ABDOMINAL ORGANISMS CULTURED AMONG 17 PATIENTS RECEIVING ADEQUATE AND 30 PATIENTS

RECEIVING INADEQUATE EMPIRIC ANTIBIOTIC THERAPY .....................................................................................39

TABLE 7. REASONS FOR ADMINISTRATION OF INADEQUATE EMPIRIC THERAPY ............................................................40

TABLE 8. PATIENT CHARACTERISTICS AMONG HOSPITAL SURVIVORS AND NONSURVIVORS .........................................41

TABLE 9. CULTURE CHARACTERISTICS AMONG HOSPITAL SURVIVORS AND NONSURVIVORS........................................42

TABLE 10. MOST COMMONLY CULTURED ORGANISMS AND THEIR ASSOCIATED MORTALITY AMONG PATIENTS

RECEIVING ADEQUATE OR INADEQUATE EMPIRIC ANTIBIOTIC THERAPY..............................................................42

TABLE 11. LOGISTIC REGRESSION ANALYSIS PREDICTING HOSPITAL MORTALITY.........................................................44

TABLE 12. OUTCOMES OF PATIENTS RECEIVING ADEQUATE VS. INADEQUATE EMPIRIC ANTIBIOTIC THERAPY..............44

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LIST OF FIGURES

FIGURE 1. INDIVIDUAL MULTIVARIABLE REGRESSION ANALYSIS OR (95% CI) OF 20 NON-RANDOMIZED COMPARATIVE

STUDIES ILLUSTRATING THE ASSOCIATION BETWEEN INADEQUATE ANTIBIOTIC THERAPY AND MORTALITY AT

END OF FOLLOW-UP. STUDIES CONDUCTED IN CRITICALLY ILL UNITS ARE SHOWN SEPARATELY NEAR THE

BOTTOM. .............................................................................................................................................................13

FIGURE 2. AN ILLUSTRATION DEPICTING THE TIMELINE FOR DETERMINING INADEQUATE EMPIRIC ANTIBIOTIC

THERAPY. THERAPY WAS CONSIDERED ADEQUATE WHEN, FOR A GIVEN INFECTIOUS EPISODE, THE ORGANISM

CULTURED WAS SUBSEQUENTLY FOUND TO BE SUSCEPTIBLE TO AN ANTIBIOTIC THAT WAS ADMINISTERED

WITHIN 24 HOURS OF THE SAMPLE COLLECTION TIME. ........................................................................................30

FIGURE 3. INCIDENCE AND RELATIVE RISK OF MORTALITY FOR INADEQUATE VERSUS ADEQUATE EMPIRIC ANTIBIOTIC

THERAPY AMONG HOSPITALIZED SOLID-ORGAN TRANSPLANT RECIPIENTS..........................................................34

FIGURE 4. DELAY IN ADMINISTRATION OF ADEQUATE EMPIRIC ANTIBIOTIC THERAPY AND ASSOCIATED MORTALITY

RATES. ................................................................................................................................................................43

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LIST OF APPENDICES

APPENDIX I – LITERATURE REVIEW SEARCH STRATEGY .........................................................................................59 APPENDIX II – RAW DATA.......................................................................................................................................60 APPENDIX III – MULTIVARIATE LOGISTIC REGRESSION MODELLING ......................................................................66

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1.0 INTRODUCTION

Healthcare–associated infections (HAIs) are infections that patients acquire in a healthcare

setting, during the course of receiving treatment for another condition (1). HAIs have imposed

significant burdens on our healthcare system, leading to increased morbidity, mortality, and

healthcare costs (2-4). As a result, the optimal management of HAIs has become an important

healthcare concern. HAIs typically affect patients who are immunocompromised, either because

of their age, underlying disease, or as a result of medical or surgical treatments (2). Along with

an aging population, the growing use of medical and surgical interventions, including invasive

devices and organ transplantation, have resulted in patients who are quite susceptible. The

pathogens involved and the body sites of infection are often related to the treatments and devices

used in intensive care units (ICUs). As a result, the highest infection rates are found among ICU

patients, who experience approximately three times higher rates than patients found elsewhere in

the hospital (2).

Upon clinical suspicion of infection, antibiotic therapy is often started early and empirically,

before pathogen identification, and before antibiotic susceptibilities are known. Deciding on the

use of an antibiotic requires a balance between the benefits of more potent broad-spectrum

antibiotics against their costs, including the potential for increased antibiotic resistance rates

caused by their overuse. Once the decision to initiate antibiotic therapy has been made, it should

ideally be directed at the most likely causative pathogens, taking into account local antibiotic

susceptibility patterns. As antibiotic resistance rates continue to increase, it appears that the

likelihood of administrating inadequate empiric antibiotic therapy (IET) also increases (5;6).

Most clinicians consider therapy to be inadequate when the antibiotic agent initiated

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demonstrates poor or no in vitro activity against the causative pathogen at the tissue site of

infection (7). Several studies concentrating on the consequences of IET have been conducted in

the ICU setting (5;6;8-11), however little information exists concerning outcomes of inadequate

empiric therapy among hospitalized solid-organ transplant (SOT) patients.

1.1 Statement of the Problem

1.1.1 Infection after Solid-Organ Transplantation

Infection in SOT patients is an important determinant of clinical outcomes (12), consequently,

the treatment of acquired bacterial infections with antibiotic therapy is recognized as being an

essential component in improving outcomes (13). Kidney, liver, heart, lung, pancreas, and small

bowel transplantation has become a therapeutic option for many end-stage organ diseases.

Advances in surgical techniques, medical management, and immunosuppressants have enhanced

both graft and patient survival rates and quality of life, however, infection continues to be a

major cause of morbidity and mortality among SOT recipients (14-19). The use of newer and

more potent immunosuppressants, particularly induction therapy with agents such as anti-

thymocyte globulin (ATG) and interleukin-2 receptor antagonists, increases the level of

immunosuppression and leads to increased susceptibility to infection in the early post-transplant

period (20). The incidence of bacterial infections in SOT recipients ranges from 21 to 68%

depending on the organ(s) transplanted, although the severity of the infection can vary among the

different SOT groups (20). In liver transplant patients, bacterial infections of the liver, peritoneal

cavity, biliary tree, bloodstream, and surgical wound are common (18). Lung and heart transplant

recipients are susceptible to pulmonary infections and bacteremias, of which 50% are of

pulmonary origin during the first post-transplant year (21). Infections among kidney transplant

patients include wound, bloodstream, and more commonly, urinary tract infections (UTIs)

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(15;17;19;22;23). Severe infections, such as bacteremia, continue to pose an increased risk of

death in transplant patients, with 14-day mortality rates ranging from 11% in kidney, 24% in

liver, 33% in heart recipients (14), and a 28-day mortality rate of 25% in lung recipients (21).

1.1.2 Antibiotic Resistance

The spread and rapid increase in antibiotic resistance rates has become a serious worldwide

healthcare concern (13). In 1946, Sir Alexander Fleming suggested that, “It seems likely that in

the next few years a combination of antibiotics with different antibacterial spectra will furnish a

cribrum therapeuticum from which fewer and fewer infecting bacteria will escape.” Despite the

advent of these potent antibiotics, the emergence and spread of antibiotic-resistant bacteria has

become a tremendous burden on our healthcare system. In 1970, the Centers for Disease Control

(CDC) established the National Nosocomial Infections Surveillance (NNIS) system, which

receives monthly reports of nosocomial infections from a non-random sample of hospitals in the

United States (24). With nearly 300 institutions currently reporting, data from the NNIS system

shows that the nosocomial infection rate remains relatively unchanged. However, the gradual

decline in the duration of inpatient stays has increased the rate of nosocomial infections per

1,000 patient days by 36%, from 7.2 in 1975 to 9.8 in 1995 (2). In 2002, nosocomial infections

accounted for nearly 1.7 million infections, resulting in excess of 98,000 deaths in the United

States (2;25). More than 70 percent of the bacteria that cause these infections are resistant to at

least one antibiotic that is commonly used to treat them (3). Drug-resistant infections can be

significantly more expensive to treat than non-resistant infections because they tend to result in a

longer duration of hospitalization, increased rates of readmission, higher drug costs, more post-

hospital care, lost work days, and increased mortality (3). Hospital-treated infections are

4

estimated to cost $260-553 million each year in Canada, however, resistant infections may add

2.8 times more than what a drug-susceptible infection adds to the direct cost of care (26).

The excessive and inappropriate use of antibiotic agents continues to be one of the most

important factors affecting antibiotic resistance patterns (13;27). In more and more cases,

bacteria are becoming resistant to multiple drugs, leaving clinicians with few effective therapies,

if any. Bacteria demonstrating multiple drug resistance were found to be responsible for 48% of

bloodstream infections in a cohort of lung transplant recipients (21). Antibiotic resistance in

hospitals may be increasing as a result of several factors, including the proliferation and

prolongation of broad-spectrum antibiotic use, grouping of patients with higher disease acuity in

segregated wards or units, and decreased staffing leading to increased person-to-person

transmission (28). Several studies have shown an association between previous antibiotic use and

the development of resistance in both gram-negative and gram-positive bacteria, especially in

specialized settings such as ICUs (29-31;31-33). Conversely, colonization and infection with

antibiotic resistant bacteria, increases the likelihood of administering IET (5), leading one to

believe that a circular and confounding relationship may exist between antibiotic use, resistance

and IET. Moreover, for some patients who receive IET, altering their antibiotic therapy later in

the course of infection, based on subsequent culture susceptibility results, may yield little benefit

with respect to in-hospital mortality, suggesting that adequate early treatment is vital (8).

Overall, it seems that infections caused by antibiotic resistant bacteria are more difficult to treat

and are associated with higher mortality rates and hospital costs (13).

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1.1.3 Inadequate Empiric Antibiotic Therapy

Pharmacological treatment with antibiotics demonstrating bacteriostatic or bactericidal activity

against the causative pathogen remains the cornerstone of managing infectious diseases. The aim

of in vitro antimicrobial susceptibility testing is to predict the in vivo success or failure of a panel

of antibiotics at standard concentrations. The results of antimicrobial susceptibility testing

combined with clinical information and experience, allows clinicians to select the most

appropriate antibiotic. The safety and efficacy of antimicrobial agents in treating infections

caused by specific pathogens must be established in well-controlled clinical trials or studies. The

degree to which in vitro susceptibility results may or may not correlate with the in vivo efficacy

of antimicrobials has been previously studied (34). Apart from the minimum inhibitory

concentration (MIC) for a particular isolate, clinicians must consider patient, antimicrobial and

pathogen-specific factors in determining how to best treat an infection. Achieving levels at or

above the MIC by itself does not provide any information on persistent effects of antibacterial

agents, such as the post-antibiotic effect. For moderate or severe infections, clinicians commonly

initiate antibiotic therapy early and empirically, before the results of cultures and their respective

antibiotic susceptibilities are known. Empiric therapy for patients with suspected or confirmed

infections should be prescribed after considering patient symptoms, laboratory findings and the

patient’s past medical history, in the context of appropriate local and wider antibiotic resistance

trends. As a consequence, there is a possibility then that situations may arise where the chosen

antimicrobial agent demonstrates poor or no in vitro activity against the identified causative

pathogen. This condition can be considered to be one of inadequate empiric antibiotic therapy.

Thus, prescribing empiric therapy demands a balance between the benefits of using agents that

have a broader spectrum of in vitro susceptibilities that may correspond to the isolated

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pathogen’s profile, against the current financial costs, potential side-effects, and future costs of

developing resistance.

Several factors have been shown to be problematic in the selection of adequate antimicrobial

therapy. First, the complexity of the drug selection process may seem confusing, presenting a

difficult challenge to many clinicians. Selecting adequate therapy involves early recognition of

infection in a patient that may present with several confounding signs and symptoms,

identification of the causative pathogen, and prescribing of an antimicrobial regimen that is

efficacious, cost-effective and poses minimal toxicity. In addition, the types of pathogens, along

with antibiotic resistance patterns, have been shown to vary among different hospitals and even

within in-hospital units, suggesting the need to develop unit-specific reporting systems (35;36).

But until the susceptibility profile of the pathogen is known, antibiotic selection occurs through

an empiric process, based on local sensitivity patterns and the patient’s clinical presentation.

Infections caused by antibiotic resistant bacteria can lead to the problem of IET (37;38). To

further this dilemma, some organisms have become resistant to a point where few or no

treatment alternatives exist (39). The escalating concern with regard to antimicrobial resistance

in the hospital setting has led several investigators to examine how this and other factors have

influenced the prescribing of inadequate treatment. However, even after controlling for other

contributing risk factors, it may still be difficult to determine whether delayed or inadequate

therapy or antibiotic resistance has led to poor outcomes. To complicate issues further, humans

who are able to produce an innate immune response, may rid themselves of the infection and

thus seem to respond to inadequate therapy or even no treatment at all. Unfortunately, few

studies to date have addressed the issue of IET use in SOT recipients and its relationship to

clinical outcomes.

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1.2 Purpose

To date, little work has been done with respect to the incidence and clinical importance of IET as

a risk factor for hospital mortality in SOT recipients. The purpose of this study was two-fold.

Firstly, to determine the scale of the problem of IET among a cohort of Canadian hospitalized

SOT recipients. Secondly, this study aimed to examine the extent to which IET contributes to in-

hospital mortality among SOT patients. Determining the incidence and impact of IET in this

population may help in the decision-making process of prescribing empiric antibiotic therapy,

and perhaps justify the use of broad-spectrum empiric antibiotics in this population.

1.2.1 Objective 1

Our first objective was to determine the incidence of inadequate empiric antibiotic therapy

among Canadian hospitalized SOT patients.

1.2.2 Objective 2

Our second objective was to determine whether IET and the duration of IET are clinically

important risk factors for in-hospital mortality in SOT patients.

1.3 Statement of Research Hypothesis

Null Hypothesis: There is no statistically significant difference (p < 0.05) in hospital mortality

between SOT recipients receiving adequate versus inadequate empiric antibiotic.

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1.4 Rationale for Hypothesis

Although the view that early adequate therapy should improve survival seems plausible, few

studies exist to support this assumption outside the ICU setting. Many of these studies

specifically evaluated bacteremic ICU patients who had been prescribed IET, a possible

confounder given that bloodstream infections have been found to be an independent predictor for

mortality (5). The ICU setting includes a variety of pressures that are not as prevalent in other

settings, influencing the emergence and spread of antibiotic resistance. One of these pressures

includes patients with prolonged hospitalization who may harbour these organisms for the

duration of their stay. The presence of invasive devices, such as urinary catheters and

endotracheal tubes, along with prolonged mechanical ventilation may also promote infections

with resistant bacteria (24;40). Severity of illness may also be an important confounder among

ICU patients, and as such, use of broad-spectrum antibiotics early in the course of infection may

have a greater impact in this population. While SOT recipients are immunocompromised and

may share some of the qualities of ICU patients, in general, they are not as acutely ill, suggesting

that IET may not contribute to an excess risk for in-hospital mortality among this cohort.

1.5 Review of the Literature

We performed a literature search of the National Library of Medicine using the OVID

MEDLINE database to find original English-language articles published from 1950 to April

2007. The search strategy is outlined in Appendix I. The aim was to find publications that

included IET as a primary independent variable of interest, and mortality as a dependent

variable. We used terms related to antibiotic use or infection in addition to transplantation,

critical illness, and hospitalization to define our population of interest. We limited the search

9

with keywords for adequate or inadequate therapy and outcomes related to morbidity or

mortality. Additional articles referenced in publications found in the MEDLINE search were also

included. IET was considered to be a primary exposure of interest if it was explicitly stated as

such in the study objectives and it was forced into a multivariate statistical analysis. We only

included publications that accounted for the administration of empiric therapy, as defined by the

receipt of the final antibiotic sensitivity profile of the organism isolated from the index culture.

No studies that met the search criteria with SOT recipients as their primary population of interest

were found. We did find two studies that described the administration of ‘discordant initial’ and

‘inactive’ antibiotic therapy among SOT patients (21;41). In a prospective cohort of 56

bacteremic lung transplant recipients, discordant therapy (defined as therapy that was inactive in

vitro for the first two days following the index blood culture) occurred in 12/56 (21%) of the

patients (21). Six of the 12 patients receiving discordant died within 28-days, compared to a

mortality rate of 8/44 (18%) among patients receiving concordant therapy (21). Another

prospective examination of 66 SOT recipients who developed septic shock, revealed that empiric

therapy was inactive in vitro in 14/66 (21%) of the cases, with a mortality rate of 64% versus

52% for those receiving active empiric therapy (41). Neither study included inadequate empiric

therapy as a primary exposure of interest nor did they include the term in the final multivariable

analysis.

Table 1 summarizes 22 non-randomized comparative cohort studies that did assess the

association between IET and mortality (5;6;10;11;38;42-58). Two of these studies did not report

the 95% OR obtained from the multivariate analysis (53;58). The reported incidence of IET

among these studies ranges from 10-80%. Many studies assessing the impact of inadequate

10

empiric antibiotic usage have focused on the ICU population and have yielded more or less

similar conclusions about the importance of adequate therapy in bloodstream infections,

including sepsis and septic shock, and ventilator-associated pneumonia (VAP). The majority of

these studies have demonstrated that hospital mortality for critically ill patients receiving

inadequate antibiotic treatment is significantly greater than those receiving adequate therapy

(5;6;10;11;42;56). However, one study did not find adequate antibiotic treatment to be associated

with a significant mortality benefit in critically ill patients (58). We extracted the reported

multivariable regression analysis odds ratio along with the associated 95% confidence interval

from each individual study. Figure 1 depicts a list of the individual studies’ multivariable

regression analysis OR (95% CI), illustrating the association between inadequate antibiotic

therapy and mortality at end of follow-up. Compared to non-critically ill settings, studies

conducted in critically ill patients had a stronger trend towards favouring the use of adequate

therapy.

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Table 1. A summary of 22 non-randomized comparative cohort studies assessing the association between IET and mortality.

Study Design1 Patient

population Infection/ pathogens

Inadequate therapy

definition

Inadequate therapy

incidence

Mortality definition

Mortality (adequate vs.

inadequate therapy)

Multivariable regression analysis

OR (95% CI) Byl et al. (38), 1999

P 417 episodes 361 patients Tertiary care hospital

Bacteremia 24 h 159/428 (37%)

Attributable in-hospital mortality

33/258 (13%) vs. 24/159 (15%)

2.22 (1.09 - 4.55)

Clec’h et al. (42), 2004

P 196 episodes 142 patients Six ICUs

VAP 24 h 109/196 (56%)

In-hospital mortality

30/63 (48%) vs. 41/79 (52%)

7.24 (1.48-35.5)

Fraser et al. (43), 2006

P 895 patients 3 tertiary care hospitals

All bacterial infections

24 h 319/895 (36%)

30-day mortality

68/576 (12%) vs. 64/319 (20%)

1.58 (0.99-2.54)

Harbarth et al. (44), 2003

P 904 patients 108 hospitals

Severe sepsis or septic shock

24 h 211/904 (23%)

28-day mortality

168/693 (24%) vs. 82/211 (39%)

1.8 (1.2-2.6)

Hyle et al. (45), 2005

R 187 patients 2 Tertiary care hospitals

ESBL E. coli & Klebsiella species

48 h 112/187 (60%)


8/75 (11%) vs. 24/112 (21%)

0.69 (0.19-2.53)

Ibrahim et al. (6), 2000

P 492 patients ICU

Bacteremia >72 h 147/492 (30%)


98/345 (28%) vs. 91/147 (62%)

6.86 (5.09-9.24)

Iregui et al. (11), 2002

P 107 patients ICU

VAP 24 h 33/107 (31%) Attributable in-hospital mortality

8/74 (11%) vs. 13/33 (39%)

7.68 (4.50-13.09)

Kang et al. (46), 2005

R 286 patients Tertiary care hospital

Gram-negative bacteremia

24 h 151/286 (53%)

30-day mortality

37/135 (27%) vs. 58/151 (38%)

3.64 (1.13-11.72)

Kim et al. (47), 2006


S. aureus bacteremia

48 h 117/238 (49%)

12-week attributable mortality

34/121 (28%) vs. 45/117 (39%)

1.39 (0.62-3.15)

Kollef et al. (5), 1999

P 655 patients ICU

All bacterial infections

>72 h 169/655 (26%)

Attributable in-hospital mortality

59/486 (12%) vs. 88/169 (52%)

4.26 (3.35-5.44)

Leibovici et al. (10), 1998

P 3413 patients Tertiary care hospital

Bacteremia 48 h 1255/3440 (36%)


436/2158 (20%) vs. 432/1255 (34%)

1.6 (1.3-1.9)

Lodise et al. (48), 2003

R 167 patients Level 1 trauma centre


48 h 48/167 (29%) Attributable mortality

23/119 (19%) vs. 16/48 (33%)

3.8 (1.3-11.0)

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Study Design1 Patient

population Infection/ pathogens

Inadequate therapy

definition

Inadequate therapy

incidence

Mortality definition

Mortality (adequate vs.

inadequate therapy)

Multivariable regression analysis

OR (95% CI) Lujan et al. (49), 2004

P 100 patients Tertiary care hospital

S. pneumoniae bacteremia

24 h 10/100 (10%) 28-day mortality

13/90 (14%) vs. 5/10 (50%)

5.72 (0.72-45.26)

Micek et al. (50), 2005


P. aeruginosa bacteremia

>72 h 75/305 (25%) In-hospital mortality

41/230 (18%) vs. 23/75 (31%)

2.04 (1.42-2.92)

Osih et al. (51), 2007

R 167 episodes 159 patients Tertiary care hospital


24 h 68/167 (41%) In-hospital mortality

35/99 (35%) vs. 26/68 (38%)

0.93 (0.45-1.92)

Paterson et al. (52), 2003

P 85 patients 12 hospitals

ESBL K. pneumoniae

>72 h 11/82 (13%) 14-day mortality

10/71 (14%) vs. 7/11 (64%)

11.11 (1.54-100)

Roghmann (53), 2000

R 132 episodes 125 patients Tertiary care hospital


48 h 105/132 (80%)

30-day mortality

28/102 (27%) vs. 5/23 (22%)

Not reported

Scarsi et al. (54), 2006


Gram-negative bacteremia

24 h 125/884 (14%)


122/759 (16%) vs. 17/125 (14%)

0.61 (0.31-1.18)

Schramm et al. (55), 2006


MRSA sterile-site infections

24 h 380/549 (69%)


28/169 (17%) vs. 99/380 (26%)

1.92 (1.48-2.50)

Valles et al. (56), 2003

P 339 patients 30 ICUs

Community-acquired bacteremia

24 h 49/339 (14%) In-hospital mortality

107/290 (37%) vs. 34/49 (69%)

4.11 (2.03-8.32)

Vidal et al. (57), 1996

P 189 episodes 182 patients Tertiary care hospital


>72 h 19/189 (10%) All-cause mortality

24/170 (14%) vs. 10/19 (53%)

6.53 (1.10 - 48.80)

Zaragoza et al. (58), 2003

P 166 patients ICU

Bacteremia >72 h 39/166 (23%) Attributable mortality

29/127 (23%) vs. 12/39 (31%)

Not reported

1 P = Prospective; R = Retrospective

13

Figure 1. Individual multivariable regression analysis OR (95% CI) of 20 non-randomized comparative studies illustrating the association between inadequate antibiotic therapy and mortality at end of follow-up. Studies conducted in critically ill units are shown separately near the bottom.

14

One of the earliest studies assessing the relationship between inadequate antibiotic treatment and

hospital mortality, evaluated a prospective cohort of 2,000 patients admitted over an 8-month

period to the medical or surgical ICU of a large urban teaching hospital in St. Louis, Missouri.

Inadequate antimicrobial treatment was defined as the microbiological documentation of a

pathogen causing infection, which was not effectively treated at the time of its identification.

This included both the absence of antimicrobial agents and the administration of an agent to

which the pathogen was resistant. Comparisons were made between patients receiving

inadequate and adequate therapy and hospital survivors to non-survivors. Multiple logistic

regression analysis was used to evaluate the relationship between the dependent variable of

hospital mortality and the independent variable of inadequate treatment and to identify

independent risk factors for the administration of inadequate treatment. Of the 655 patients with

a nosocomial or community-acquired infection, 169 (25.8%) were found to have received

inadequate antibiotic treatment. The infection-related mortality rate for infected patients

receiving inadequate therapy (42.0%) was significantly greater than patients receiving adequate

antibiotic treatment (17.7%) (RR, 2.37; 95% CI, 1.83 to 3.08; p < 0.001). In addition, a logistic

regression model demonstrated that inadequate antibiotic treatment was the most important

independent determinant of hospital mortality (adjusted OR, 4.27; 95% CI, 3.35 to 5.44; p <

0.001). The incidence of inadequate antimicrobial treatment was most common among patients

with nosocomial infections, which developed after treatment of a community-acquired infection

(45.2%), followed by patients with nosocomial infections alone (34.3%) and patients with

community-acquired infections alone (17.1%) (p < 0.001). Among patients with nosocomial

infections, inadequate therapy occurred most commonly as a result of Gram-negative bacteria

that were resistant to third-generation cephalosporins. Inadequate treatment for methicillin-

resistant Staphylococcus aureus (MRSA), Candida species and vancomycin-resistant enterococci

15

(VRE) were also commonly found among nosocomial infections. Multiple logistic regression

analysis revealed that the prior administration of antibiotics (adjusted OR, 3.39; 95% CI, 2.88 to

4.23; p < 0.001), presence of a bloodstream infection (adjusted OR, 1.88; 95% CI, 1.52 to 2.32; p

= 0.003), increasing Acute Physiology and Chronic Health Evaluation (APACHE) II scores in 1-

point increments (adjusted OR, 1.04; 95% CI, 1.03 to 1.05; p = 0.002), and decreasing patient

age (adjusted OR, 1.01; 95% CI, 1.01 to 1.02; p = 0.012) were independently associated with the

administration of inadequate antibiotic treatment. This study established an association between

the prescribing of inadequate therapy and hospital mortality, and demonstrated that previous

antibiotic use may be an important risk factor for inadequate therapy among ICU patients.

Continuing the work of Kollef et al., Ibrahim et al. prospectively evaluated the relationship

between the adequacy of antimicrobial treatment for bloodstream infections and the primary

outcome of hospital mortality among a cohort of patients at the same university-affiliated urban

teaching hospital. All patients admitted to the medical or surgical ICU were eligible for

enrolment. Inadequate antimicrobial treatment was defined as the microbiological documentation

of a pathogen causing infection, both bacterial and fungal, which was not effectively treated at

the time the pathogen and its susceptibility profile were known. The primary analysis compared

hospital survivors to non-survivors. Multiple logistic regression analysis was used to evaluate the

relationship between the dependent variable of hospital mortality and the independent variable of

inadequate antimicrobial treatment and to identify independent risk factors for the administration

of inadequate treatment. Over a two-year period, 4913 critically ill patients were admitted, of

whom 492 (10.0%) were found to have a bloodstream infection. Of the 492 patients, 147

(29.9%) received inadequate treatment. Furthermore, the hospital mortality for these patients was

significantly greater than those receiving adequate therapy (61.9% vs. 28.4%; RR, 2.18; 95% CI,

16

1.77 to 2.69; p < 0.001). Multiple logistic regression analysis identified the administration of

inadequate antibiotic treatment as an independent risk factor hospital mortality (adjusted OR,

6.86; 95% CI, 5.09 to 9.24; p < 0.001). The most commonly identified bloodstream pathogens

along with their rates of inadequate antimicrobial treatment included: VRE (n = 17; 100%),

Candida species (n=41; 95.1%), MRSA (n = 46; 32.6%), coagulase-negative staphylococci

(CNS) (n = 96; 21.9%), and Pseudomonas aeruginosa (n = 22; 10.0%). A statistically significant

correlation was found between the rates of inadequate antimicrobial treatment for individual

micro-organisms and their associated rates of hospital mortality (Spearman’s correlation

coefficient 0.8287; p=0.006). However, some organisms, such as Escherichia coli and Klebsiella

species, were found to be associated with relatively low rates of inadequate antimicrobial

therapy, though their associated hospital mortality rates were greater than 30%. Multiple logistic

regression analysis also demonstrated that the following criteria were independently associated

with the administration of inadequate antimicrobial treatment: Bloodstream infection attributable

to Candida species (adjusted OR, 51.86; 95% CI, 24.57 to 109.49; p < 0.001); Prior

administration of antibiotics during the current hospital stay (adjusted OR, 2.08; 95% CI, 1.58 to

2.74; p = 0.008); Decreasing serum albumin concentrations (adjusted OR, 1.37; 95% CI, 1.21 to

1.56; p = 0.014); Increasing central catheter duration (adjusted OR, 1.03; 95% CI, 1.02 to 1.04; p

= 0.008). The study demonstrated that ICU patients with bloodstream infections receiving

inadequate antimicrobial treatment were at an increased risk of death compared to patients

receiving adequate treatment. The authors recommended initial empiric therapy with

vancomycin for MRSA and CNS, along with combination therapy for the treatment of P.

aeruginosa.

17

Most studies with bacteremia seem to support the importance of adequate empiric therapy,

however, there are a few studies that do not come to this conclusion (51;54;58). These studies

may include certain groups of organisms that may be less virulent and thus it may be more

problematic in determining their role in morbidity or mortality. One such cohort study in Spain

was conducted among 166 prospectively followed patients with bacteremia, 39 (23.5%) of which

received inadequate antibiotic treatment, while 127 (76.5%) received adequate treatment (58).

Bacteremia was determined to be nosocomial in nature in 92.3% of the inadequately treated

cohort, and 79.5% of adequately treated group. The occurrence of coagulase-negative

staphylococci isolates (OR, 2.62; 95% CI, 1.10 to 6.21; p = 0.015), and absence of a respiratory

or abdominal source of infection (OR, 0.35; 95% CI, 0.12 to 0.97; p = 0.04) was greater in the

cohort with inadequate treatment than in the group with adequate treatment. Neither crude

mortality rates (56.4% vs. 50.3%; p = 0.512) nor bacteremia-related mortality rates (30.8% vs.

22.8%; p = 0.315) were significantly different among the inadequately and adequately treated

groups, respectively. Multivariate analysis did not reveal inadequate treatment to be an

independent predictor of mortality. The authors suggested that this was likely a result of

microbiological factors and clinical features, such as the types of micro-organisms isolated and

the sources of the bacteremia. Cultures from patients in the inadequately treated arm were more

likely to have grown less virulent isolates and to have been sampled from sources of infections

with typically better prognoses. As a result, this tended to dilute the effects on mortality, and

produce a statistically non-significant difference.

In an application to non-critically ill patients, Leibovici et al. set out to determine whether

empiric antibiotic treatment matching the in vitro susceptibility of the pathogen, which they

termed as being appropriate treatment, improved survival in hospitalized patients with

18

bloodstream infections. This prospective and observational cohort study examined patients with

bloodstream infections identified between 1988 and 1994 in an urban hospital in Israel.

Empirical antibiotic treatment was defined as appropriate if it was started within two days of the

first positive blood culture, and the infecting micro-organism was subsequently found to be

susceptible to an intravenously administered drug. However, the authors decided that treatment

of a pseudomonal infection with just an aminoglycoside would be considered inappropriate. This

study analyzed the benefit presented by appropriate empiric treatment in stratified subgroups of

patients defined by a set of other mortality risk factors. Logistic regression analysis was used to

measure the independent contribution of inappropriate treatment to hospital mortality. Of the

3415 patients identified with bloodstream infections, 2158 (63.2%) were given appropriate

empiric antibiotic treatment, of which 436 (20.2%) died, compared with the death of 432

(34.4%) of 1255 patients who were given inappropriate treatment (OR, 2.1; 95% CI, 1.8 to 2.4; p

= 0.0001). The median duration of hospital stay for survivors was 9 days when given appropriate

treatment and 11 days when given inappropriate treatment (p = 0.0001). The greatest relative

reduction in the mortality rate associated with appropriate treatment versus inappropriate

treatment in patients was seen most commonly in: Pediatric patients (4% vs. 17%; OR, 5.1; 95%

CI, 2.4 to 10.7); Intra-abdominal infections (12% vs. 34%; OR, 3.8; 95% CI, 2.0 to 7.1); Skin

and soft tissue infections (23% vs. 49%; OR, 3.1; 95% CI, 1.8 to 5.6); Infections caused by

Klebsiella pneumoniae (17% vs. 39%; OR, 3.0; 95% CI, 1.7 to 5.1), and Streptococcus

pneumoniae (22% vs. 42%; OR, 2.6; 95% CI, 1.1 to 5.9). Multivariable logistic regression

analysis revealed that inappropriate empiric treatment was associated with a significant risk for

in-hospital mortality (adjusted OR, 1.6; 95% CI, 1.3 to 1.9) independent of other risk factors.

The investigators concluded that in this relatively large cohort of hospitalized patients with

19

bloodstream infections, inappropriate empiric treatment was associated with an increased risk of

death, regardless of concomitant risk factors for mortality.

Infections resulting from certain groups of organisms, such as antibiotic-resistant gram-negative

bacilli, have become more of a concern in recent years, as patients infected by these relatively

virulent isolates may be at a higher risk of receiving inadequate therapy (45;46;52;55). To

evaluate the effect of inappropriate initial antimicrobial therapy on mortality, Kang et al.

retrospectively reviewed 286 hospitalized patients in Seoul, South Korea. They identified and

included patients with nosocomial antibiotic-resistant gram-negative bacteremia: 61 patients with

E. coli, 65 with K. pneumoniae, 74 with P. aeruginosa, and 86 with Enterobacter species. Initial

antibiotic therapy was considered to have been appropriate if a patient received at least one agent

within 24 hours of blood culture collection to which the causative pathogens were susceptible.

Only the first bacteremic episode per patient was included. Antibiotic resistance was defined as

in vitro resistance to either cefotaxime or ceftazidime, except for P. aeruginosa, which was

required to be resistant to either piperacillin, ciprofloxacin, ceftazidime, or imipenem. High-risk

sources of bacteremia were defined as the lung, peritoneum, or an unknown source. The main

outcome measure was 30-day mortality. Of the 286 patients, 135 (47.2%) received appropriate

initial empirical antimicrobial therapy, with the remaining 151 (52.8%) patients receiving

inappropriate therapy. The inadequately treated group had a significantly greater mortality rate

compared to the adequately treated cohort (38.4% vs. 27.4%; p=0.049). Multivariate analysis

demonstrated that septic shock, a high-risk source of bacteremia, P. aeruginosa infection, and an

increasing APACHE II score, were independent risk factors for mortality. In a subgroup analysis

of patients with a high-risk source of bacteremia (n = 132), inappropriate initial antimicrobial

therapy was independently associated with decreased survival (adjusted OR, 3.64; 95% CI, 1.13

20

to 11.72; p = 0.030). The results of this study suggest that inappropriate initial antimicrobial

therapy is associated with adverse outcomes in patients diagnosed with antibiotic-resistant gram-

negative bacteremia, specifically in those that are severely ill, have a high-risk source of

bacteremia, or have isolates that are especially virulent.

Some evidence suggests that when adequate empiric antibiotic therapy (AET) is initiated early in

the course of the infection and prior to the availability of susceptibility results, the associated

mortality rates are significantly lower (8;11;48;55;59). Iregui et al. fixed their efforts on the

clinical importance of delayed initial appropriate antibiotic treatment in critically ill patients with

clinically diagnosed VAP. Their goals were to identify the occurrence of initially delayed

appropriate antibiotic treatment for VAP, and to determine its effects on patient outcomes. They

prospectively observed a cohort of patients requiring mechanical ventilation and admitted to the

medical ICU of a large urban teaching hospital in St. Louis, Missouri. The primary outcome

compared hospital mortality among those receiving initially delayed appropriate antibiotic

treatment to all other patients in the cohort. Adequate initial treatment was defined as an

antibiotic with in vitro susceptibility to the pathogen isolated in respiratory samples. Delayed

therapy was defined as a time period of >24 hours between the time of VAP diagnosis, until the

time that appropriate antibiotic therapy was administered. Thirty-three of 107 (30.8%) patients

received appropriate antibiotic therapy that was delayed >24 hours after the clinical diagnosis of

VAP. The most common cause of inadequate initial treatment was a delay in writing the medical

orders (n = 25; 75.8%). The presence of a resistant micro-organism (n = 6) accounted for 18.2%

of cases. Among these patients, the mean time delay between VAP diagnosis and the

administration of an appropriate antibiotic was 28.6 ± 5.8 hours, compared to 12.5 ± 4.2 hours

for all other patients (p < 0.001). Patients with initially delayed treatment had a significantly

21

greater hospital mortality compared to the other patients in the cohort (69.7% vs. 28.4%; p <

0.01). It is important to note that diagnostic delays were not counted, and that the authors utilized

a clinical VAP diagnosis as opposed to using bronchoscopically obtained cultures. It was

suggested that clinicians avoid delaying the administration of appropriate antibiotic therapy to

patients with VAP to minimize their mortality risk.

The extent to which the timely use of adequate therapy impacts clinical outcomes in hospitalized

patients may depend on the causative pathogens and populations studied, but may also depend on

the actual time delay itself. In a study of episodes of nosocomial S. aureus bacteremia, Lodise et

al. attempted to determine the effect of delayed therapy on morbidity and mortality in a

retrospective cohort of 167 hospitalized patients at a trauma centre in Detroit, Michigan. If a

patient had more than one episode of S. aureus bacteremia during a hospitalization, only the first

episode was included. Classification and regression tree analysis was utilized to select the time

interval (from the time the culture result was obtained until administration of adequate therapy)

that classified patients as having either a low-risk or high-risk of infection-related mortality. The

time breakpoint between delayed and early treatment was determined to be 44.75 hours.

Accordingly, 48 (28.7%) patients did not receive appropriate treatment prior to the breakpoint

time and were deemed to have received delayed treatment. The remaining 119 (71.3%) patients

did receive appropriate treatment within 44.75 hours, and were classified into the early treatment

group. A comparison of the infection-related mortality rates between the two groups revealed a

1.7-fold increase among patients receiving delayed therapy versus early treatment (33.3% vs.

19.3%; p = 0.05). A multivariate analysis revealed that delayed treatment was an independent

predictor of infection-related mortality (adjusted OR, 3.8; 95% CI, 1.3 to 11.0; p = 0.01) and was

associated with a longer hospital stay than early treatment (20.2 vs. 14.3 days; p = 0.05). For

22

patients with an APACHE II score >15.5 and a ‘high-risk’ source of infection (non-IV catheter

related), mortality was 86.7% in the delayed treatment group compared with 44.7% in the early

treatment group (p = 0.006). However, among patients with an APACHE II score <15.5, the

mortality rate was not significantly different, indicating that delayed treatment had more of an

adverse effect on those that were more severely ill. The results of this study point towards a

delay in adequate therapy in the realm of 24-48 hours as being an important factor in clinical

outcomes of patients hospitalized with S. aureus nosocomial bacteremias, particularly those who

are severely ill or have a ‘high-risk’ source of infection.

To date, studies determining the impact of initially delayed adequate therapy on the clinical

outcomes of infections, have utilized observational cohorts in their design. Randomized

controlled trials would provide a methodological advantage in reducing bias and the effect of

confounders, however, this design is neither practical nor ethical. The propensity score is an

analysis that utilizes the probability of exposure to a specific treatment conditional on observed

variables and is increasingly being used in observational studies. This analysis attempts to

compensate for selection bias by creating strata in which subjects are matched on the propensity

score, balancing the covariables between patients receiving adequate or inadequate therapy. Kim

et al. studied 238 hospitalized patients with S. aureus bacteremia who received either

inappropriate or appropriate empirical therapy, and compared them by using two risk

stratification models. The first model used a cohort study with a propensity score to adjust for

confounding by treatment allocation, and the second, used a propensity-matched case-control

study. Inappropriate therapy was modeled on the basis of patient characteristics, and included in

the multivariate model to adjust for confounding. For the case-matching analysis, patients with

inadequate empiric treatment (cases) were matched to those with adequate empiric treatment

23

(controls) on the basis of the propensity score. The cohort study revealed that the bacteremia-

related mortality rate was 38.4% (45/117) among those inappropriately treated versus 28.1%

(34/121) for those appropriately treated (adjusted OR 1.60; 95% CI, 0.93-2.76; p = 0.09).

Conducting a multivariate analysis to adjust for independent predictors for mortality and the

propensity score, demonstrated that inappropriate empiric therapy was not associated with

mortality (adjusted OR, 1.39; 95% CI, 0.62-3.15). The matched case-control study analyzed 50

pairs, with mortality rates of 32% (16/50) for the case group and 28% (14/50) for the control

group (OR, 1.15; 95% CI, 0.51-2.64; p = 0.85). Once more, these authors indicated that this may

have been a result of microbiological factors, specifically, the fact that most gram-positive

pathogens tend not to be as virulent as gram-negative ones. In addition, they point to the

methodology of the study and the propensity score, including the possibility of it being

underpowered and not being able to control for detection bias.

In summary, these results suggest an association between the time to administration of IET and

mortality, promoting the principle of utilizing broad-spectrum antibiotics early in the course of

infection. However, the patients were not homogeneous, with considerable variation with respect

to their sites of infection, organism virulence, and severity of illness. With a few exceptions, the

evidence favours the early use of adequate empiric therapy in relatively ill patients who are at

risk of an infection with a virulent organism. The Infectious Diseases Society of America and the

Society for Healthcare Epidemiology of America has developed guidelines and a set of

recommendations for enhancing antimicrobial stewardship. With respect to combination therapy,

they state that there are insufficient data to recommend the routine use of combination therapy to

prevent the emergence of resistance (Grade C-II) (60). However, combination therapy does have

a role in certain clinical contexts, including increasing the breadth of empiric coverage and the

24

likelihood of adequate initial therapy in critically ill patients at risk of infection with multidrug-

resistant pathogens (Grade A-II) (60). It is also important to note that they also stress a more

targeted approach to empirical antimicrobial therapy on the basis of culture results and

elimination of redundant combination therapy. This strategy can more effectively target the

causative pathogen, resulting in decreased antimicrobial exposure and substantial cost savings

(Grade A-II) (60). It remains to be seen whether SOT patients are at increased risk of mortality

secondary to inadequate empiric therapy.

25

2.0 METHODS

2.1 Study Location and Population

2.1.1 Inclusion Criteria

We included solid-organ transplant recipients admitted to the Multi-Organ Transplant (MOT)

unit for the 2-year period of May 2002 to April 2004 at The Toronto General Hospital,

University Health Network, Toronto, Canada. Our institution accounted for approximately 40%

of all new transplant recipients in the province of Ontario, and 15% of all new transplant patients

in Canada (61). All transplant patient groups were eligible, including kidney, kidney/pancreas,

liver, lung, heart, heart/lung, and small bowel transplant recipients. Patients must have had a

microbiologically documented infection (i.e. positive culture) in the context of systemic signs of

infection and defined according to criteria established by the CDC (62). We identified eligible

patients through the records of the clinical microbiology laboratory (Department of

Microbiology at Mount Sinai Hospital and Toronto Medical Laboratories, Toronto, Canada),

which processes and performs cultures and sensitivities of all clinical specimens obtained at our

institution.

2.1.2 Exclusion Criteria

Non-transplant patients, including living donors, transferred to the unit were excluded since they

may not be representative of our population of interest.

2.2 Study Design

A retrospective cohort study design was used to characterize the sampled patients. Patient data

were analyzed to determine if an association existed between the presence or absence of

26

inadequate antibiotic therapy and the main outcome of all-cause in-hospital mortality. Data

variables collected included patient demographics, severity of illness during the first 24 hours of

admission (as measured by the Acute Physiology And Chronic Health Evaluation score,

APACHE II), central venous catheter use, presence of acute renal failure, chronic dialysis

requirement, length of ventilation, transplant data, concomitant immunosuppression,

neutropenia, diabetes mellitus requiring insulin, infection-related data, previous use of

antibiotics, mortality, length of hospitalization, previous admissions to the ICU, length of ICU

stay, empiric antibiotic use and adequacy of treatment. Data sources included electronic medical

records, microbiology records, the Outpatient Transplant Tracking Record (OTTR) clinic

database, and transplant pharmacist patient profiles. This study was submitted to the institution’s

Research Ethics Board and received expedited review approval (REB number 04-0306-AE).

2.3 Microbiology

The clinical microbiology laboratory used the VITEK-1 system (Bio Mérieux, Charbonnières les

Bains, France), an automated, short-incubation broth micro-dilution system capable of

performing susceptibility testing of most rapidly growing gram-positive and gram-negative

aerobic bacteria. It yields results in a period of 4 to 10 hours. Results were automatically

transferred from the VITEK-1 system to the Laboratory Information System via a computer

interface. Susceptibility testing was performed and interpreted according to guidelines and

breakpoints established by the Clinical and Laboratory Standards Institute (CLSI) (63). Results

were reported qualitatively as either Susceptible (S), Intermediate (I), or Resistant (R). Quality

control procedures were performed on all new lots of identification and susceptibility cards, once

when received and once weekly when the lot was in use. Alternatively, the Kirby-Bauer disk

diffusion method was also available for use. Daily quality controls were performed when any

27

out-of-control results were observed. Subjects were included if documentation existed for both

the antibiotic therapy administered and the patient’s in-hospital survival outcome. Patient culture

data could be included more than once if they had multiple positive cultures during the study

period. For a given patient, the first infection episode was included, and subsequent infections

were included only if a different organism was cultured.

2.4 Definitions

2.4.1 Infection vs. Contamination vs. Colonization

Clinical pharmacists followed the CDC definitions of nosocomial infections to critically assess

each positive culture (62). To avoid reviewer bias, the patient being evaluated must not have

been under the direct clinical care of the pharmacist. Based on clinical documentation from the

patients’ medical records, all isolates were categorized as being a true infection, contaminant, or

of unknown clinical significance. If the medical records explicitly stated that the isolate was a

contaminant, then it was deemed as such. For laboratory-confirmed bloodstream infections with

common skin contaminants (eg. Coagulase-negative staphylococci), patients must have had at

least one of the following signs or symptoms: fever (>38°C), chills, or hypotension; and a

positive culture from two or more blood cultures drawn on separate occasions, or from at least

one blood culture from a patient with an intravascular line, with the physician instituting

appropriate antimicrobial therapy (62). Otherwise, infection was defined as the entry and

multiplication of micro-organisms in the tissues of the host leading to local or systemic signs and

symptoms of infection. We defined colonization as the presence of micro-organisms in or on a

host with growth and multiplication, but without tissue invasion or damage. Only true infections

were included among the infectious episodes.

28

2.4.2 Infectious Episodes

An infectious episode was defined by the first true positive bacterial (index) culture collected per

isolate per patient, or when identical isolates are cultured within 72 hours of each other, the

culture collected from the primary body site of infection. The CLSI recommendation to exclude

duplicates and include only the first isolate of a given species per patient, irrespective of body

site or antibiotic pattern, was implemented (64).

2.4.3 Healthcare vs. ICU vs. Community Associated Infections

Each infectious episode was classified as being healthcare (nosocomial), ICU, or community-

associated, according to criteria established by the CDC (62). The NNIS system defines an HAI

as a localized or systemic condition that results from an adverse reaction to the presence of an

infectious agent or its toxin; and that was not present or incubating at the time of admission to a

healthcare setting (1). For the purposes of this study, the infection must become evident (i.e.

result in a positive culture) 48 hours or more post-admission to a non-ICU ward, unless the

patient had been hospitalized within 30 days before admission, or had been transferred from

another hospital or long-term care facility. Otherwise, positive cultures obtained within 48 hours

of admission were categorized as being community-associated. An ICU-associated infection was

defined as a positive culture drawn 48 hours after admission to the ICU or within 48 hours after

transfer from the ICU (24).

2.4.4 Primary Site of Infection

The primary site of infection was confirmed by culture, clinical evidence, or not confirmed. If

confirmed, the site of infection was categorized as one of the following, according to established

CDC criteria (62): Pneumonia or lower respiratory tract infection; Urinary tract infection;

29

Bloodstream or IV catheter infection (e.g. central venous catheter); Central nervous system

infection; Skin and soft tissue infection; Intra-abdominal infection; and Cardiovascular system

infection. If the site was unconfirmed or determined solely by clinical evidence, then it was

classified as ‘Other’.

2.4.5 Empiric Antibiotic Therapy

A multidisciplinary team of physicians and pharmacists determined requirements for antibiotic

treatment and selection of specific antibiotics during the patients’ hospital course. Antibiotic

therapy was defined as empirical when administered in response to an infectious episode, but

before organism identification and susceptibility results were available.

2.4.6 Adequate vs. Inadequate Empiric Antibiotic Therapy

Empiric antibiotic therapy was critically assessed starting from the date of an infectious episode

(index culture), up to the date of patient discharge from the hospital (Figure 2). Therapy was

considered adequate when, for a given infectious episode, the organism cultured was

subsequently found to have in vitro susceptibility to an antibiotic that was administered within 24

hours of the index culture collection time. A 24 hour period after cultures are taken was used as

the cut-off point given that some evidence points to this period as being important for reducing

mortality rates when adequate therapy is prescribed (8;11;48;55). Additionally, the patient must

receive at least 3 days of therapy. Patients were classified into the inadequate therapy group if

they had experienced at least one episode of inadequate empiric therapy during their hospital

stay.

30

Time

Patient admitted

Symptoms/clinical suspicion

Culture results

Initiate empiric therapy +

samples taken

24 hours

Continue/change initial empiric

therapy

End of symptoms

Discharge or death

Adequate vs. Inadequate

Time

Patient admitted

Symptoms/clinical suspicion

Culture results

Initiate empiric therapy +

samples taken

24 hours24 hours

Continue/change initial empiric

therapy

End of symptoms

Discharge or death

Adequate vs. Inadequate

Figure 2. An illustration depicting the timeline for determining inadequate empiric antibiotic therapy. Therapy was considered adequate when, for a given infectious episode, the organism cultured was subsequently found to be susceptible to an antibiotic that was administered within 24 hours of the sample collection time.

2.4.7 Previous Antibiotic Therapy

Oral and intravenous antibiotics prescribed for patients for at least 3 days and up to 30 days prior

to their hospital admission were recorded.

2.4.8 Previous Graft Rejection and Immunosuppressant Use

Previous episodes of graft rejection and anti-thymocyte globulin or basiliximab use were

recorded for a period of up to six months prior to the patients’ admission date. Since all patients

would be receiving maintenance immunotherapy, we limited data collection to these two potent

immunosuppressants as they may have a greater effect on infection-related outcomes (15).

31

2.4.9 Multi-Drug Resistance

Some bacteria are inherently resistant to specific antibiotics. Organisms found to have in vitro

resistance to two or more antibiotics to which they would normally be susceptible, were

classified as being multi-drug resistant. The Sanford Guide to Antimicrobial Therapy was used

as a reference in determining normal susceptibility patterns (65).

2.5 Outcomes

We compared the primary outcome of in-hospital all-cause mortality between those receiving

adequate versus those receiving inadequate empiric antibiotic therapy. Secondary outcomes

included duration of hospital stay (defined as the number of days from admission to discharge or

death), need for ICU transfer, and duration of stay in the ICU.

2.6 Statistical Analysis

All data were collected and entered into a computerized database using Microsoft Access®

(Microsoft Corporation, Redmond, WA) and analyzed using SPSS 14.0® (SPSS Incorporated,

Chicago, Ill). Categorical data were compared using Fisher’s exact test, and normally distributed

continuous variables were compared using the Student’s t-test. Alternatively, depending on the

validity of the normality assumption, the Wilcoxon rank sum test was also utilized. All

comparisons were unpaired, all tests of significance two-tailed, and equal variances were not

assumed. Values are expressed as the mean ± standard deviation for continuous variables or as a

proportion for categorical variables. Relative risks are reported along with their 95% confidence

intervals. P-values of ≤ 0.05 were considered to be statistically significant. The primary data

analysis compared infected patients who received inadequate antibiotic treatment to infected

32

patients receiving adequate antibiotic treatment. Binary logistic regression analysis was used to

determine independent associations for the dependent outcome variable of in-hospital all cause

mortality. Building of the model began with forced inclusion of IET as the exposure of interest.

All clinically plausible variables with p<0.2 on univariable analysis were also considered for

inclusion in the model. A sequential nested approach was used to enter new terms into the

models with 0.05 as the limit for acceptance or removal of new terms. Results of the logistic

regression analysis are reported as adjusted Odds Ratios (OR) with 95% confidence intervals.

Goodness of fit was assessed by comparing the predicted and observed outcomes as

demonstrated by the contingency table for the Hosmer and Lemeshow Test (66).

33

3.0 RESULTS

3.1 Patients

During the two-year study period, 1675 out of approximately 3300 actively followed transplant

patients were admitted to the multi-organ transplant unit and eligible for evaluation. Of these

patients, 355 were found to have microbiologically documented positive cultures, and 43 were

deemed to be a result of either contamination or colonization. None of these 43 patients received

antibiotic therapy, and all survived their hospital stay. The remaining 312 patients were included

in the evaluation, and were found to have 574 evaluable cultures (Figure 3). Tables 2 and 3

outline various characteristics among patients and infectious episodes receiving adequate versus

inadequate empiric antibiotic therapy. The mean age (49.7 vs. 52.1; p = 0.088), mean graft age

(1.8 vs. 1.4; p = 0.231), proportion of males (60.1% vs. 65.7%; p = 0.347), and severity of illness

score (19.0 vs. 19.8; p = 0.205) did not differ significantly between those receiving adequate

versus inadequate therapy.

3.2 Inadequate Empiric Antibiotic Therapy

Inadequate empiric antibiotic therapy was prescribed in 169/312 (54.2%) patients and among

248/574 (43.2%) cultures. Univariate analysis revealed that the presence of a central IV catheter

(43.2% vs. 25.2%; p<0.001), increasing duration of mechanical ventilation (9.5 vs. 4.0 days;

p=0.016), liver transplantation (40.2% vs. 27.3%; p = 0.017), previous episodes of rejection

(29.6% vs. 18.9%; p = 0.035), anti-thymocyte globulin use (39.1% vs. 24.5%; p = 0.007), and

previous fluoroquinolone use (23.7% vs. 13.3%; p = 0.021), were statistically more likely to be

associated with patients receiving inadequate therapy. Multi-drug resistant organisms (50.8% vs.

34

39.6%; p = 0.009) and intra-abdominal infections (16.9% vs. 7.7%; p < 0.001) were more likely

to occur among patients with cultures that were treated inadequately.

Admitted

SOT Patients N= 1675

Patients with positive cultures

N = 355

Patients with true infections

N = 312

Patients with contam’n/colon’n

N = 43

Patients with no/ negative cultures

N = 1320

Adequate N = 143 (46%)

Inadequate N = 169 (54%)

Survivors N = 133 (93%)

Survivors N = 127 (75%)

Nonsurvivors N = 42 (25%)

Nonsurvivors N = 10 (7%)

Exclude

Exclude

24.9% vs. 7.0% RR = 3.55

95% CI: 1.85 to 6.83 P < 0.001

Figure 3. Incidence and relative risk of mortality for inadequate versus adequate empiric antibiotic therapy among hospitalized solid-organ transplant recipients.

35

Table 2. Characteristics of patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)

AET IET p

No. of Patients 143 (%) 169 (%) Age (yrs) 49.7 ± 13.6 52.1 ± 11.6 0.088 Gender Male 86 (60.1) 111 (65.7) 0.347 Female 57 (39.9) 58 (34.3) Diabetes mellitus requiring insulin

7 (4.9) 11 (6.5) 0.630

Dialysis 17 (11.9) 14 (8.3) 0.344 Acute renal failure 6 (4.2) 10 (5.9) 0.610 Neutropenia 5 (3.5) 5 (3.0) >0.999 Central IV catheter 36 (25.2) 73 (43.2) <0.001 Length of ventilation (days) 4.0 ± 10.7 9.5 ± 26.8 0.016 APACHE II score 19.0 ± 6.0 19.8 ± 5.3 0.205 Graft age (yrs) 1.8 ± 3.9 1.4 ± 3.1 0.231 Organ Heart 6 (4.2) 10 (5.9) 0.610 Heart-Lung 1 (0.7) 1 (0.6) >0.999 Kidney 39 (27.3) 31 (18.3) 0.076 Kidney-Pancreas 7 (4.9) 15 (8.9) 0.190 Liver 39 (27.3) 68 (40.2) 0.017 Lung 50 (35.0) 44 (26.0) 0.107 Small Bowel 1 (0.7) 0 (0.0) 0.458 Previous graft rejection 27 (18.9) 50 (29.6) 0.035 CMV serology D+ R- 22 (15.4) 21 (12.4) 0.511 D+ R+ 32 (22.4) 43 (25.4) 0.595 D- R+ 43 (30.1) 54 (32.0) 0.806 D- R- 21 (14.7) 32 (18.9) 0.365 Unknown 25 (17.5) 19 (11.2) 0.142 Immunosuppression Anti-thymocyte globulin 35 (24.5) 66 (39.1) 0.007 IL-2 receptor antagonists 44 (30.8) 47 (27.8) 0.618 Muromonab-CD3 0 (0.0) 0 (0.0) Previous antibiotic use 67 (46.9) 84 (49.7) 0.650 Carbapenems 5 (3.5) 8 (4.7) 0.778 Aminoglycosides 12 (8.4) 10 (5.9) 0.507 Macrolides 12 (8.4) 8 (4.7) 0.247 Fluoroquinolones 19 (13.3) 40 (23.7) 0.021 Piperacillin-Tazobactam 2 (1.4) 3 (1.8) >0.999 Penicillins 3 (2.1) 6 (3.6) 0.515 3rd/4th Generation Cephalosporins 4 (2.8) 12 (7.1) 0.121 1st/2nd Generation Cephalosporins 0 (0.0) 5 (3.0) 0.065 Cotrimoxazole 30 (21.0) 35 (20.7) >0.999

36

Table 3. Characteristics of true infectious episodes treated with adequate (AET) or inadequate empiric antibiotic therapy (IET)

AET IET p

No. of Positive Cultures 326 (%) 248 (%)

Primary source Pulmonary 100 (30.7) 47 (19.0) 0.002 Urinary 72 (22.1) 69 (27.8) 0.142 Bloodstream/IV Catheter 39 (12.0) 16 (6.5) 0.031 CNS 0 (0.0) 2 (0.8) 0.187 Cardiovascular 2 (0.6) 0 (0.0) 0.508 Intra-Abdominal 25 (7.7) 42 (16.9) <0.001 Skin/Soft Tissue 20 (6.1) 26 (10.5) 0.064 Other 68 (20.9) 46 (18.5) 0.527

Bacteremia 111 (34.0) 63 (25.4) 0.028

Gram-Negative organisms 169 (51.8) 133 (53.6) 0.736

Multi-drug resistant organisms 129 (39.6) 126 (50.8) 0.009

Location acquired Nosocomial 182 (55.8) 153 (61.7) 0.172 ICU 87 (26.7) 63 (25.4) 0.774 Community 57 (17.5) 32 (12.9) 0.132

Among the 574 infectious episodes, 58.2% were classified as being an HAI (excluding ICU),

26.1% were ICU-associated, followed by community-associated infections, which contributed to

15.5% of all episodes. Gram-negative organisms were cultured from 302 (52.6%) episodes. The

most common primary sources of infection among all episodes were found to be pulmonary (147

cultures) and urinary (141 cultures) sources. Table 4 shows the distribution of organisms

associated with the various body sites of infection among the 248 infectious episodes deemed to

have received IET. Coagulase-negative staphylococci and Enterococcus species were the most

commonly cultured gram-positive organisms, whereas Pseudomonas species and Escherichia

coli were the most commonly cultured gram-negative pathogens.

37

Coagulase-negative staphylococci, Escherichia coli, Pseudomonas aeruginosa, and

Enterococcus species were the most common multi-drug resistant organisms found among the

inadequately treated cultures (Table 5). Enterococcus species, coagulase-negative staphylococci,

Escherichia coli, and Citrobacter species were the most common organisms causing intra-

abdominal infections among the inadequately treated cultures (Table 6). The reasons for initial

administration of inadequate empiric antibiotic therapy are listed in Table 7. The most common

reason among both gram-negative and gram-positive organisms, was a delay of more than 24

hours in initiating any type of therapy. Other reasons included resistance of gram-negative

species to 3rd-Generation Cephalosporins, Ciprofloxacin, and other antibiotics initiated within 24

hours of the sample collection time. Among gram-positive isolates, resistance to penicillins and

failure to initiate gram-positive specific therapy also contributed to inadequate empiric therapy.

38

Table 4. Organisms cultured from patients with inadequately treated infectious episodes

Organism Urinary Pulmonary Other Intra- Abdominal

Skin/ Soft Tissue

Line Central Nervous

Total

CNS 1 1 25 12 7 6 2 54 Pseudomonas sp. 7 20 2 5 34 Enterococcus sp. 22 0 1 6 2 31 Escherichia coli 18 1 4 5 2 30 Citrobacter sp. 6 4 4 4 1 19 Klebsiella sp. 6 2 1 3 1 2 15 Enterobacter cloacae 2 3 3 1 1 2 12 Enterococcus faecium 4 4 2 10 Enterococcus faecalis 2 1 2 3 1 9 Acinetobacter sp. 1 1 2 1 5 Serratia marcescens 4 1 5 MSSA 1 2 1 1 5 MRSA 2 1 3 Hafnia alvei 1 1 1 3 Enterobacter aerogenes

1 2 3

Moraxella catarrhalis 2 2 Proteus mirabilis 2 2 Haemophilus influenzae

1 1 2

Streptococcus sp. 1 1 2 Enterococcus gallinarum

1 1

Burkholderia cepacia 1 1

Total 69 47 46 42 26 16 2 248

39

Table 5. Multi-drug resistant organisms cultured among patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)

Multi-drug Resistant Organisms AET

(N=326) (%) IET

(N=248) (%)

Coagulase-negative staphylococci 49 (38.0) 47 (37.3) Escherichia coli 25 (19.4) 20 (15.9) Pseudomonas aeruginosa 12 (9.3) 19 (15.1) Enterococcus sp. 10 (7.8) 11 (8.7) MRSA 7 (5.4) 3 (2.4) Enterobacter sp. 7 (5.4) 4 (3.2) Citrobacter sp. 6 (4.7) 9 (7.1) Klebsiella sp. 5 (3.9) 9 (7.1) Acinetobacter sp. 2 (1.6) 2 (1.6) Burkholderia cepacia 4 (3.1) 1 (0.8) Others 2 (1.6) 1 (0.8)

TOTAL 129 (100) 126 (100)

Table 6. Intra-abdominal organisms cultured among 17 patients receiving adequate (AET) and 30 patients receiving inadequate empiric antibiotic therapy (IET)

Intra-abdominal Organisms AET

(n=17) (%) IET

(n=30) (%)

Enterococcus sp. 7 (28) 13 (31.0) Coagulase-negative Staphylococci 6 (24) 12 (28.6) Escherichia coli 1 (4) 5 (11.9) Citrobacter sp. 2 (8) 4 (9.5) Klebsiella sp. 3 (12) 3 (7.1) Acinetobacter sp. 0 (0) 2 (4.8) Pseudomonas aeruginosa 1 (4) 2 (4.8) Enterobacter cloacae 4 (16) 1 (2.4) Serratia sp. 1 (4) 0 (0.0)

TOTAL 25 (100) 42 (100)

40

Table 7. Reasons for administration of inadequate empiric therapy

Reason No. (%)

GNB therapy initiated >24h after sample 85 (34.2) GPB therapy initiated >24h after sample (Other) 53 (21.4) GPB therapy initiated >24h after sample (CNS) 39 (15.7) GNB resistant to other empiric therapy 24 (9.7) GNB resistant to 3rd-Generation Cephalosporins 14 (5.6) GNB resistant to Ciprofloxacin 13 (5.2) GPB resistant to penicillins 11 (4.4) No GPB-specific antibiotic initiated 9 (3.6)

TOTAL 248

• GNB = Gram-negative bacteria • GPB = Gram-positive bacteria • CNS = Coagulase-negative staphylococci

3.3 Characteristics Related to Hospital Mortality

Of the 312 patients evaluated, 52 did not survive their in-hospital stay. Univariate analyses

revealed that lung transplant recipients (50.0% vs. 26.2%; p < 0.001), prior antibiotic use (69.2%

vs. 44.2%; p < 0.001), and ICU-associated infections (59.1% vs. 16.3%; p < 0.001) were more

likely to be associated among the 52 nonsurvivors (Tables 8 and 9). The in-hospital mortality

rate for patients receiving at least one episode of inadequate empiric therapy was significantly

greater than those receiving adequate therapy (24.9% vs. 7.0%; RR, 3.55; 95% CI, 1.85 to 6.83;

p < 0.001; Figure 3). Furthermore, there was a significant association between increasing time to

administration of adequate empiric therapy (measured in 24 hour increments) and increased

hospital mortality rates (Figure 4). During the same two-year period, the mean mortality rate for

all patients admitted to the transplant unit was 4.7%. Organisms cultured from patients receiving

IET and their associated mortality rates are shown in Table 10. The most commonly cultured

pathogens among non-survivors included Pseudomonas sp., Enterococcus sp., Escherichia coli,

Citrobacter sp., Klebsiella sp., and Enterobacter cloacae.

41

Table 8. Patient characteristics among hospital survivors and nonsurvivors

Nonsurvivors Survivors p

No. of Patients

52 (%) 260 (%)

Age (yrs) 51.0 ± 13.3 51.0 ± 12.5 0.995

Gender Male 31 (59.6) 166 (63.8) 0.637 Female 21 (40.4) 94 (36.2) Diabetes requiring insulin 2 (3.8) 16 (6.2) 0.747

Dialysis 4 (7.7) 27 (10.4) 0.799

Acute renal failure 3 (5.8) 13 (5.0) 0.736

Neutropenia 2 (3.8) 8 (3.1) 0.675

Central IV catheter 37 (71.2) 72 (27.7) <0.001

Previous ICU admission 17 (32.7) 37 (14.2) 0.002

Length of ventilation (days) 3.1 ± 9.3 26.4 ± 42.9 <0.001

APACHE II score 25.6 ± 7.6 18.2 ± 4.2 <0.001 Graft age (yrs) 1.8 ± 3.3 1.5 ± 3.5 0.629

Organ

Heart 2 (3.8) 14 (5.4) >0.999

Heart-Lung 0 (0.0) 2 (0.8) >0.999

Kidney 4 (7.7) 66 (25.4) 0.004

Kidney-Pancreas 1 (1.9) 21 (8.1) 0.143

Liver 19 (36.5) 88 (33.8) 0.750

Lung 26 (50.0) 68 (26.2) <0.001

Small Bowel 0 (0.0) 1 (0.4) >0.999

Previous graft rejection 10 (19.2) 67 (25.8) 0.381

CMV serology

D+ R- 4 (7.7) 39 (15.0) 0.192

D+ R+ 15 (28.8) 60 (23.1) 0.378

D- R+ 12 (23.1) 85 (32.7) 0.192

D- R- 11 (21.2) 42 (16.2) 0.418

Unknown 10 (19.2) 34 (13.1) 0.274

Immunosuppression

Anti-thymocyte globulin 17 (32.7) 84 (32.3) >0.999

IL-2 receptor antagonist 12 (23.1) 79 (30.4) 0.321

Muromonab-CD3 0 (0.0) 0 (0.0)

Previous antibiotic use 36 (69.2) 115 (44.2) <0.001

Carbapenems 5 (9.6) 8 (3.1) 0.047

Aminoglycosides 6 (11.5) 16 (6.2) 0.229

Macrolides 5 (9.6) 15 (5.8) 0.348

Fluoroquinolones 18 (34.6) 41 (15.8) 0.003

Piperacillin-Tazobactam 1 (1.9) 4 (1.5) >0.999

Penicillins 1 (1.9) 8 (3.1) >0.999

3rd/4th Gen. Cephalosporins 4 (7.7) 13 (5.0) 0.499

1st/2nd Gen. Cephalosporins 3 (5.8) 3 (1.2) 0.060

Cotrimoxazole 15 (28.8) 50 (19.2) 0.135

42

Table 9. Culture characteristics among hospital survivors and nonsurvivors

Nonsurvivors Survivors p No. of Positive Cultures

132 (%) 442 (%)

Primary source Pulmonary 42 (31.8) 106 (24.0) 0.089 Urinary 28 (21.2) 113 (25.6) 0.357 Line 16 (12.1) 39 (8.8) 0.311 CNS 0 (0.0) 2 (0.5) >0.999 Endocardium 1 (0.8) 1 (0.2) 0.407 Intra-Abdominal 17 (12.9) 50 (11.3) 0.643 Skin/Soft Tissue 7 (5.3) 39 (8.8) 0.272 Other 21 (15.9) 93 (21.0) 0.216 Bacteremia 46 (34.8) 153 (34.6) >0.999 Gram Negative Cultures 75 (56.8) 226 (51.1) 0.276 Multi-drug resistant organisms

68 (51.5) 187 (42.3) 0.072

Location acquired Nosocomial 48 (36.4) 287 (64.9) <0.001 ICU 78 (59.1) 72 (16.3) <0.001 Community 6 (4.5) 83 (18.8) <0.001

Table 10. Most commonly cultured organisms and their associated mortality among patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)

Organism AET

Nonsurvivors/ Patients

Mortality (%)

IET Nonsurvivors/

Patients

Mortality (%)

p

Acinetobacter species 1/5 (20.0) 2/5 (40.0) >0.999

Citrobacter freundii 1/15 (6.7) 6/18 (33.3) 0.095

CNS 0/58 (0.0) 0/54 (0.0) -

Enterobacter cloacae 7/14 (50.0) 6/12 (50.0) >0.999

Enterococcus sp. 8/61 (13.1) 15/52 (28.8) 0.043

Escherichia coli 6/39 (15.4) 7/30 (23.3) 0.537

Klebsiella sp. 7/23 (30.4) 3/15 (20.0) 0.709

MSSA 7/31 (22.6) 1/5 (20.0) >0.999

Pseudomonas aeruginosa 5/45 (11.1) 14/32 (43.8) 0.003

Serratia sp. 1/7 (14.3) 0/5 (0.0) >0.999

43

7.5

55.6

24.128.2

0

10

20

30

40

50

60

70

80

≤24 24-48 48-72 >72

Time to administration of adequate empiric therapy (h)

Mort

alit

y (

%)

Figure 4. Delay in administration of adequate empiric antibiotic therapy and associated mortality rates.

3.4 Logistic Regression Analysis

Binary logistic regression analysis was conducted to determine independent risk factors for

hospital mortality (Table 11). Increasing time to administration of adequate empiric antibiotic

therapy (per one hour increment) was found to be an independent determinant of in-hospital

mortality (adjusted OR, 1.01; 95% CI, 1.006 to 1.018; p < 0.001). ICU-associated infections

(adjusted OR, 6.27; 95% CI, 2.79 to 14.09; p < 0.001), antibiotic use 30 days prior to admission

(adjusted OR, 3.56; 95% CI, 1.51 to 8.41; p = 0.004), and an increasing APACHE II score

(adjusted OR, 1.26; 95% CI, 1.16 to 1.34; p < 0.001), were also identified as independent

predictors of hospital mortality. Raw data and detailed results of the binary logistic model are

found in Appendices II and III respectively.

p<0.001

P=0.049

p=0.001

N=143 N=42 N=36 N=91

44

Table 11. Logistic regression analysis predicting hospital mortality

Predictor B Wald χ2 P-value Adjusted

Odds Ratio 95% CI

Lower Upper

IET (1 hour increment) 0.012 14.311 <0.001 1.012 1.006 1.018

APACHE II score (1 point increment)

0.227 39.467 <0.001 1.255 1.163 1.336

Previous antibiotic use 1.270 8.408 0.004 3.562 1.509 8.406

ICU infection 1.836 19.781 <0.001 6.273 2.793 14.090

Constant -8.698 70.696

3.5 Secondary Outcomes

Secondary outcomes are reported in Table 12. In addition to increased mortality rates,

inadequately treated patients were more likely to require transfer to the ICU (26.0% vs. 7.0%; p

< 0.001), with a total of 621 ICU patient days. The average in-hospital length of stay was also

statistically greater among those receiving IET, although the average ICU length of stay did not

differ significantly. The total number of inpatient days among patients receiving IET was more

than twofold compared to those receiving adequate therapy (8 697 vs. 4 305 days).

Table 12. Outcomes of patients receiving adequate (AET) vs. inadequate empiric antibiotic therapy (IET)

AET IET p

No. of Patients 143 (%) 169 (%)

Nonsurvivors 10 (7.0) 42 (24.9) <0.001 Length of stay (mean days) 30.1 ± 47.8 51.5 ± 56.8 <0.001 Total inpatient days 4 305 8 697 Transfers to ICU 10 (7.0) 44 (26.0) <0.001 Length of ICU stay (mean days) 10.6 ± 16.4 14.2 ± 17.7 0.549 Total ICU days 109 621

45

4.0 DISCUSSION

This single-centre retrospective study demonstrated that inadequate empiric antibiotic therapy

occurs in approximately half (169/312) of our hospitalized solid-organ transplant recipients

being treated for infection. Approximately one-quarter (42/169) of patients receiving IET did not

survive their hospital stay, which is in relative agreement with mortality rates ranging from 31-

69% among critically ill patients receiving IET (Table 1). Controlling for several confounders,

solid-organ transplant patients with infections were at greater risk for in-hospital mortality the

longer they received IET (adjusted OR, 1.012 per one-hour increment). It may be more

significant to look at increments other than one-hour units. For example, an increase in IET by

24 hours increases the adjusted OR to 1.33, assuming that the logit function is linear for this

continuous covariate. Despite the importance of antibiotic use in this immunosuppressed

population, the benefits of adequate empiric therapy have been unclear. Given that our institution

accounts for approximately 40% of all new transplant recipients in the province of Ontario, and

15% of all new transplant patients in Canada, these findings cannot be taken lightly (61). Results

from this study indicate that in addition to critically ill patients, solid-organ transplant recipients

also experience inadequate empiric therapy at a rate that has a significant clinical effect on

hospital mortality. Moreover, inadequately treated patients required a more resource intensive

hospital stay, with increased ICU transfers and longer durations of stay.

Among patients and cultures treated with inadequate empiric therapy, we also identified potential

risk factors for its administration. Patients with prior antibiotic use, specifically

fluoroquinolones, and isolates that were multi-drug resistant, were more likely to have received

IET. In addition to being a potential risk factor for IET, prior antibiotic use was also found to be

an independent determinant of hospital mortality (adjusted OR, 3.56), though multi-drug

46

resistance was not retained in the final regression model. Late initiation of empiric therapy was

the most common reason for the administration of IET, however, resistance to therapy still

accounted for approximately one-quarter of all IET cases. Resistance is especially problematic in

the context of relatively pathogenic isolates such as P. aeruginosa (50), which was the most

common multi-drug resistant gram-negative isolated from 14 of the 52 non-survivors. A number

of studies have examined the relationship between antibiotic use and the development of

antibiotic resistance (29;67;68). Vanderkooi et al. (68) aimed to identify risk factors, including

previous antibiotic therapy, which were predictive of antibiotic resistance in invasive S.

pneumoniae infections. The authors found that patients who had previously received courses of

trimethoprim-sulfamethoxazole, macrolides, and fluoroquinolones, were at least four times as

likely to have an infection with an isolate that was resistant to the same class of antibiotics. They

also observed that previous use of agents from any antibiotic class (except fluoroquinolones) was

associated with infections due to isolates that were also resistant to agents from other classes,

including penicillin. Moreover, healthcare-associated infections were more likely to have a

fluoroquinolone-resistant isolate cultured (adjusted OR, 12.9; 95% CI, 3.95 to 43.8; p < 0.001

and 9.94; 95% CI, 2.22 to 44.6; p = 0.003, respectively for infections acquired in a nursing home

or hospital). The emergence of resistant bacteria, specifically gram-negatives, to 3rd-Generation

Cephalosporins, Ciprofloxacin, and other antibiotics, continues to constitute a major barrier to

adequate therapy (13). Furthermore, colonization with resistant pathogens may predispose

certain patients to re-infection with these resistant organisms (9).

In addition to prior antibiotic usage, we observed that treatment of episodes of previous graft

rejection, liver transplant recipients, patients with intra-abdominal infections, central IV

catheterization, and mechanical ventilation were also more likely to be associated with IET. The

47

increased use of novel immunosuppressive agents along with the use of antimicrobial agents,

continues to alter the epidemiology of infections in solid-organ transplant recipients (15;69). The

use of potent anti-rejection agents, including anti-thymocyte globulin use, may increase the net

state of immunosuppression in transplant recipients, depressing cell-mediated immunity and

increasing their susceptibility to infectious complications (70). The use of anti-thymocyte

globulin has been associated with a greater overall risk of infection, especially for opportunistic

viral infections, such as CMV (69). However, a recent observational study of renal transplant

recipients revealed that anti-thymocyte globulin was also associated with an increased risk for

bacterial infections (adjusted OR, 3.3; 95% CI, 1.3 to 7.9; p = 0.009) (15). Previous studies have

reported that 7-11% of bacteremias among liver transplant recipients were polymicrobial in

nature (14;71). Liver transplant recipients and those with intra-abdominal infections tend to

acquire polymicrobial infections that may be more difficult to treat empirically, resulting in

inadequate therapy (18). Increased mortality rates have been associated with Enterococcus sp.

isolated from polymicrobial intra-abdominal infections (72), and in addition to drainage of any

focal collections, providing specific therapy directed at this pathogen does seem to be justified

(73). We observed that all 13 intra-abdominal isolates of Enterococcus sp. were treated

inadequately and that 6/13 were in fact resistant to penicillins. Thus, local resistance levels

among gram-positive organisms, such as Enterococcus sp., in addition to gram-negatives should

be considered carefully before initiation of therapy for intra-abdominal infections. Several

studies have demonstrated associations between central IV catheterization or increasing duration

of mechanical ventilation and the occurrence of bloodstream infections or VAP, respectively.

These studies have found that the longer the duration of central vein catheterization or

mechanical ventilation, the more likely it is for patients to develop bloodstream or pulmonary

infections with resistant bacteria (40;74;75).

48

4.1 Study Limitations

Several limitations for this study were identified, including selection and recall biases. These

limitations were minimized through the use of inclusion criteria, and the use of different data

sources to validate the data collected. Since this study utilized retrospectively collected data,

there is a possibility of a recall bias when data was collected from the respective data sources.

The impact of this bias was lessened by the availability of redundant data sources including

electronic and paper medical records, in addition to clinical pharmacists’ patient profiles. In

addition, the inclusion of some less virulent contaminants as pathogens may have decreased the

overall attributable mortality among all pathogens.

Selection bias may occur if the patients from whom microbiological samples are obtained are not

typical of the entire population with infections. For example, physicians may be more likely to

take samples from patients who have recurrent symptoms or significant co-morbidities. This may

skew the data towards more frequent reporting of certain infections over others. To reduce this

possibility, we excluded duplicate isolates of the same organism, and only included the first

isolate cultured.

Given that this study was conducted in a single centre, the results reported are reflective of

institutional-specific practices and standards of care, which may differ from other centres. Since

randomization would not have been ethical and blinding difficult to achieve, this could result in

exposures being linked to hidden confounders. Eligibility criteria and outcome assessments can

be standardised, although measurement bias is a possibility if, for example, the outcome was

detected more accurately in the prospective cohort.

49

The definition of adequate therapy utilized in vitro susceptibility as the main requirement.

Determining the impact of inadequate empiric therapy poses a problem of characterization, in

that there seems to be varying definitions of what constitutes inadequate therapy.

Pharmacokinetic and pharmacodynamic considerations, such as dosage, duration of treatment,

concentration breakpoints, and tissue penetration, could also be important in defining adequate

treatment. Adequate therapy may much more than merely a laboratory result indicating in vitro

susceptibility. Additionally, the timing of antibiotic treatment may also be an important

consideration. We examined a single time point of 24 hours to define adequate therapy, although

it has been well accepted that antibiotics should be given as soon as possible. It seems that the

effective timeframe for administrating antibiotics in some infections is relatively narrow (11),

and other time points less than this cut-off may also be significant. We identified our primary

outcome as in-hospital mortality, an outcome that can be measured explicitly. However, we were

not able to directly associate the outcome to the infection in question, although implying

infectious causation can be subjective (76).

4.2 Implications of Inadequate Empiric Therapy

The increased mortality rates associated with inadequate therapy suggest that starting empiric

therapy at the earliest signs of infection may be beneficial. Unfortunately, if the empiric therapy

chosen does not demonstrate in vitro susceptibility, a change of antibiotic therapy after

susceptibility results are reported does not seem to improve clinical outcomes (9;77;78). As

previously described, the benefits of starting adequate empiric therapy early in the course of the

infectious episode (< 24 hours) seem to be significant, and the results of this study appear to

support these previous findings. This will require a higher degree of clinical suspicion, both in

50

terms of timely therapy and antibiotic coverage for organisms, such as some gram-positive

bacteria, which may not be initially suspected. We observed that Enterococcus and Pseudomonas

species, mostly from intra-abdominal and pulmonary sources respectively, were the most

common isolates from non-survivors. They were also observed to be some of the most common

multi-drug resistant isolates as well. For patients at risk for these types of infections, and having

received prior antibiotic therapy, initiation of empiric therapy with either a different class of

antibiotics, or the use of combination therapy may be more prudent (79). However, in order to

thwart the development of resistance, therapy should be continuously reassessed in order to

prescribe the narrowest spectrum of coverage possible, and to discontinue therapy as soon as it is

clinically appropriate.

In an effort to control the increase in antibiotic resistance, and possibly improve adequate

treatment, multiple strategies have emerged (80). To this end, development of a system for

reporting antibiotic susceptibility patterns for specific units on a regular basis in order to capture

local temporal pattern changes could be used as a guide to initiate adequate therapy. Antibiotic

susceptibility data are often aggregated into antibiograms, which are helpful tools that

summarize commonly cultured organisms and their susceptibility to routinely used antibiotics.

Unfortunately, they may not necessarily predict the susceptibility patterns from a particular

patient since reported data are rarely stratified by other patient factors. One such factor includes

the location of the hospitalized patient. The types of pathogens associated with nosocomial

infections in ICUs from different institutions, along with their antibiotic susceptibility profiles,

have been shown to vary (35). Furthermore, others have discovered variability in the

susceptibility profiles of micro-organisms among surgical, trauma, and medical ICUs within a

single large teaching hospital (36). This suggests that hospitals may not only need to develop

51

their own systems for reporting patterns of antibiotic susceptibility, but may also need to take

into account unit-specific patterns of antibiotic resistance. Such information may help clinicians

develop more rational prescribing practices that will avoid inadequate antibiotic treatment of

hospitalized patients. A joint committee of the Society for Healthcare Epidemiology of America

and the Infectious Diseases Society of America has developed a set of recommendations for the

prevention and reduction of antimicrobial resistance in hospitals (80). These recommendations

include the monitoring of antimicrobial use on a regular basis, in addition to monitoring the

relationship between antimicrobial use and resistance. These responsibilities should be assigned

through practice guidelines or other institutional policies.

5.0 CONCLUSIONS

We found that inadequate empiric therapy is common and appears to be an important

determinant of hospital mortality among Canadian solid-organ transplant patients. No studies to

date have tackled the subject of inadequate empiric antibiotic use in transplant recipients and its

relationship to hospital mortality. Moreover, little advancement has been made in increasing the

proportion of hospitalized patients receiving adequate empiric antibiotic treatment. Development

of an antibiogram reporting system may help clinicians develop more rational prescribing

practices that will reduce the unnecessary administration of broad-spectrum drugs and avoid

inadequate antibiotic treatment in transplant patients. Future studies may concentrate on

stratification of antibiogram data by additional patient characteristics that might be useful in

improving empiric treatment selections. Efforts aimed at identifying patients at risk and reducing

the occurrence of inadequate empiric therapy may improve outcomes.

52

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58

7.0 PUBLICATIONS AND ABSTRACTS TO DATE Abstract/oral presentation: American Transplant Congress, June 2006, Boston, MA Inadequate Empiric Antibiotic Therapy among Solid-Organ Transplant Patients: Incidence and Impact on Hospital Mortality. Hamandi B, Holbrook A, Humar A, Brunton J, Papadimitropoulos M, Wong G. Transplantation 2006 Jul 15;82(1)S3:442-3.

59

8.0 APPENDICES

Appendix I – Literature Review Search Strategy

Database: Ovid MEDLINE(R) 1950 - Search Strategy:

# Search History 1 organ transplantation/ or heart transplantation/ or kidney transplantation/ or liver transplantation/ or exp

lung transplantation/ or pancreas transplantation/ 2 exp Hospitals/ 3 exp Intensive Care Units/ 4 Critical Illness/ 5 exp Critical Care/ 6 Inpatients/ 7 intensive care.tw. 8 icu.tw. 9 or/1-8 10 exp Bacterial Infections/ 11 exp Anti-Bacterial Agents/ 12 exp Infection/ 13 exp Anti-Infective Agents/ 14 exp Pneumonia/ 15 Septicemia.tw. 16 (bacter: adj2 infect:).tw. 17 or/10-16 18 9 and 17 19 exp Morbidity/ 20 exp Mortality/ 21 exp Hospitalization/ 22 exp epidemiologic studies/ 23 exp Prognosis/ 24 exp risk/ 25 or/19-24 26 18 and 25 27 exp Drug Administration Schedule/ 28 ((adequa: or inadequa: or appropriate: or inappropriate: or condordant or discordant) adj2 (antibiotic: or

antibacterial or antimicrobial or therapy)).mp. 29 or/27-28 30 26 and 29 31 limit 30 to english language

60

Appendix II – Raw Data

pt Patient identification number death 1 = died in-hospital; 0 = did not die in-hospital adeq Number of hours of IET lungtx 1 = lung transplant recipient; 0 = not lung transplant recipient abxuse 1 = previous antibiotic use; 0 = no previous antibiotic use icu 1 = icu-related infection; 0 = no ICU-related infection pulmon 1 = pulmonary infection; 0 = no pulmonary infection mdr 1 = MDR infection; 0 = no MDR infection apache APACHE-II score within 24 hours of admission vent Number of days on mechanical ventilation cvc 1 = presence of CVC; 0 = absence of CVC icutx 1 = required ICU transfer; 0 = did not require ICU transfer iculos ICU length of stay in days

pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos

1 0 56 0 1 0 0 0 21 0 0 0 0

2 0 2 0 0 0 0 1 19 0 0 0 0

3 0 2 0 0 1 0 0 20 1 1 0 0

4 1 5 0 1 0 0 0 34 1 0 0 0

5 0 96 0 0 0 0 1 21 0 1 0 0

6 0 49 0 0 1 0 1 17 1 1 0 0

7 0 2 0 0 0 0 0 20 0 0 0 0

8 0 185 0 0 1 0 1 13 13 1 1 13

9 0 3 1 0 0 1 0 16 5 0 0 0

10 0 33 0 1 0 0 1 19 0 0 0 0

11 0 179 0 0 0 0 0 19 0 0 0 0

12 0 3 1 1 0 1 1 19 0 0 0 0

13 0 3 0 0 0 0 1 20 0 0 0 0

14 1 168 0 1 1 1 1 20 115 1 0 0

15 0 2 0 1 0 0 0 19 0 0 0 0

16 0 2 0 1 0 0 0 21 0 1 0 0

17 0 1 0 0 0 0 0 18 0 0 0 0

18 0 13 0 0 0 0 1 20 0 1 0 0

19 0 85 0 0 0 0 1 21 0 1 0 0

20 0 187 0 0 0 0 1 19 0 1 1 21

21 1 185 1 1 1 1 1 27 16 0 1 21

22 0 9 0 0 0 0 1 19 0 0 0 0

23 0 2 1 1 0 1 1 19 0 0 0 0

24 0 29 0 1 0 0 1 12 0 1 1 1

25 0 5 0 0 0 0 1 21 0 0 0 0

26 0 174 0 0 0 0 0 19 0 0 0 0

27 0 52 0 0 0 0 0 20 0 1 1 1

28 0 10 0 1 0 0 1 18 0 0 0 0

29 0 1 1 0 0 1 0 17 3 0 0 0

30 0 3 1 1 1 1 1 10 42 1 0 0

31 0 4 0 0 0 0 0 10 3 1 0 0

32 1 6 1 1 1 0 1 26 34 1 0 0

33 0 186 0 0 0 0 0 20 0 0 0 0

34 0 23 0 1 0 0 1 19 0 0 0 0

35 0 92 0 0 0 1 1 18 0 0 0 0

36 0 3 1 1 1 1 1 10 2 0 0 0

37 0 2 0 1 0 0 1 21 0 0 0 0

38 0 16 0 0 0 0 0 19 0 0 0 0

39 0 60 0 0 0 0 1 18 0 0 0 0

40 0 179 0 0 0 0 0 14 1 0 0 0

41 0 3 0 0 0 0 0 21 0 0 0 0

61


42 1 182 0 1 1 1 1 18 1 0 0 0

43 0 27 0 1 0 0 1 20 0 0 0 0

44 0 103 0 1 0 1 1 19 0 0 0 0

45 0 93 0 0 1 0 1 23 2 0 1 1

46 0 10 0 0 0 0 0 10 2 0 1 3

47 1 191 1 1 0 0 1 20 0 1 0 0

48 0 177 0 0 0 1 0 20 0 0 0 0

49 0 1 1 1 0 1 1 17 0 0 0 0

50 0 3 1 1 0 1 0 14 6 0 0 0

51 0 3 0 0 0 0 0 15 1 0 0 0

52 0 2 1 0 0 1 0 19 0 0 0 0

53 0 2 0 0 0 0 0 18 0 0 0 0

54 0 27 0 1 0 0 1 21 0 0 0 0

55 0 173 0 0 0 0 1 18 0 1 0 0

56 1 8 0 1 1 0 1 33 1 1 1 4

57 0 91 0 0 0 0 1 21 0 0 0 0

58 0 2 0 0 0 1 1 22 0 0 0 0

59 0 170 0 1 0 0 1 20 0 1 1 1

60 0 12 0 0 0 1 0 20 0 0 0 0

61 0 6 0 1 0 0 0 19 0 0 0 0

62 0 85 1 1 0 1 1 27 6 0 0 0

63 0 21 0 1 0 0 0 19 0 0 0 0

64 0 180 0 1 0 0 0 12 3 1 0 0

65 1 14 0 1 1 1 1 32 1 1 1 20

66 0 24 0 1 0 0 1 18 0 1 0 0

67 0 3 0 0 0 0 1 20 0 0 0 0

68 0 1 0 0 0 0 0 20 0 0 0 0

69 0 3 0 0 0 0 0 10 1 0 0 0

70 1 65 1 1 1 1 1 23 40 1 1 9

71 1 170 1 1 1 1 1 20 209 1 1 24

72 0 16 1 0 0 0 0 19 0 0 0 0

73 1 10 0 0 0 0 1 39 1 1 0 0

74 0 23 1 0 1 1 0 12 2 1 1 2

75 0 21 0 0 0 0 1 7 1 0 0 0

76 0 5 1 1 0 0 1 20 0 0 0 0

77 0 14 0 1 0 0 0 18 0 0 0 0

78 0 191 0 1 0 0 0 16 1 0 1 10

79 0 1 0 1 0 0 0 20 0 0 0 0

80 0 31 0 0 0 1 1 19 0 0 0 0

81 0 11 0 1 0 0 0 20 0 0 0 0

82 0 1 0 1 0 0 1 23 0 0 1 1

83 0 34 0 1 0 1 0 27 1 1 0 0

84 0 1 1 1 0 1 0 18 0 0 0 0

85 0 68 0 1 0 0 1 20 0 0 0 0

86 0 54 0 1 0 0 0 19 0 0 0 0

87 0 26 0 0 0 0 1 18 0 1 0 0

88 0 3 0 0 0 0 0 21 0 0 0 0

89 0 180 0 1 0 0 1 20 0 0 1 1

90 1 30 0 0 0 1 0 37 5 0 1 6

91 0 50 0 0 0 0 1 18 4 0 1 7

92 0 10 0 1 0 0 1 21 0 0 0 0

93 0 31 0 0 0 0 0 18 0 0 0 0

94 0 5 0 1 0 0 0 19 0 0 0 0

95 0 1 0 0 0 1 0 19 0 0 0 0

96 0 17 0 0 0 0 1 20 0 1 0 0

97 0 75 1 0 0 1 1 16 3 0 0 0

98 0 54 0 0 0 0 0 20 0 0 0 0

99 0 16 0 1 0 0 1 18 0 0 0 0

100 0 102 0 1 1 0 1 13 2 0 0 0

101 0 1 0 0 1 1 0 23 51 0 1 54

102 0 1 0 1 0 0 1 18 0 0 0 0

103 0 3 1 1 0 1 1 17 0 0 0 0

62


104 0 191 0 1 0 0 1 19 0 1 0 0

105 0 30 0 0 0 0 1 18 0 0 0 0

106 0 192 0 0 1 0 1 17 5 1 0 0

107 0 27 0 1 0 0 0 19 0 0 0 0

108 1 31 0 0 1 0 0 20 0 1 0 0

109 1 120 0 0 0 1 1 39 2 1 1 2

110 0 59 0 1 0 0 0 17 0 0 0 0

111 0 60 0 1 0 0 0 18 0 0 0 0

112 0 2 1 1 0 1 0 20 0 0 0 0

113 1 32 0 0 0 0 0 19 0 0 0 0

114 1 188 0 0 1 1 1 28 40 1 1 28

115 0 78 0 1 0 0 1 20 0 0 0 0

116 0 1 0 1 0 0 0 17 0 0 0 0

117 1 181 0 1 0 0 1 18 0 1 1 7

118 0 3 0 0 0 0 1 35 9 1 0 0

119 0 2 0 1 0 0 0 18 0 0 0 0

120 0 50 0 1 0 0 1 20 0 1 0 0

121 0 50 0 0 0 0 0 18 0 0 0 0

122 0 51 1 1 1 1 1 31 24 1 0 0

123 0 15 0 0 0 0 0 19 0 0 0 0

124 0 36 0 0 0 0 1 20 0 1 0 0

125 1 66 1 1 1 0 1 15 69 1 0 0

126 0 2 0 0 0 0 0 20 0 0 0 0

127 0 86 0 0 0 0 1 20 0 0 0 0

128 1 68 0 1 1 0 1 25 52 1 1 1

129 0 16 0 1 0 0 1 20 0 1 0 0

130 0 22 0 0 0 0 1 21 0 1 0 0

131 1 184 0 1 0 0 1 20 0 1 0 0

132 0 58 1 1 0 1 1 20 0 0 0 0

133 0 3 0 0 0 0 0 21 0 0 0 0

134 0 56 0 0 0 0 1 18 0 1 0 0

135 0 2 0 1 0 1 0 20 0 0 0 0

136 0 69 0 1 0 0 1 11 1 0 1 1

137 1 32 1 1 0 1 1 25 44 0 1 44

138 0 8 0 1 0 0 1 18 0 0 0 0

139 0 1 0 0 1 0 0 16 1 0 0 0

140 0 173 1 1 0 0 1 11 4 0 0 0

141 0 192 0 0 0 0 0 19 0 0 0 0

142 1 33 1 1 1 0 1 20 1 1 0 0

143 1 10 0 0 1 0 1 39 1 0 0 0

144 1 35 1 1 0 1 0 19 0 0 0 0

145 0 17 1 1 0 1 1 23 16 0 0 0

146 0 2 1 1 0 0 0 18 0 0 0 0

147 0 58 0 0 0 1 0 15 1 0 0 0

148 0 2 0 1 1 1 1 29 11 1 0 0

149 0 30 0 0 0 1 1 20 0 0 0 0

150 0 106 0 0 0 0 1 25 1 1 0 0

151 0 62 0 1 0 0 0 19 0 0 0 0

152 0 16 0 1 0 0 1 18 0 0 0 0

153 0 171 0 1 0 0 1 19 0 1 1 4

154 0 2 0 0 1 0 0 7 1 0 0 0

155 1 70 1 1 1 1 1 19 112 1 0 0

156 0 11 0 1 0 0 0 19 0 1 0 0

157 0 3 1 0 1 1 0 30 17 1 0 0

158 0 35 0 1 0 0 1 19 0 0 0 0

159 1 173 0 0 0 0 1 24 1 1 1 5

160 0 70 0 1 0 0 1 18 0 0 0 0

161 0 2 0 0 0 0 0 9 1 0 1 5

162 0 181 0 1 0 0 0 20 0 0 0 0

163 0 3 0 1 0 0 1 20 0 0 0 0

164 1 36 1 0 1 1 1 29 24 1 0 0

165 0 1 0 1 1 0 1 21 9 1 0 0

63


166 0 4 0 1 0 0 1 19 0 0 0 0

167 0 86 0 0 0 0 1 17 3 0 1 3

168 0 57 0 1 1 0 1 15 2 0 1 2

169 0 188 0 0 0 0 1 9 1 0 0 0

170 0 176 0 0 1 1 1 10 2 1 0 0

171 0 93 0 1 0 0 1 13 1 0 1 37

172 0 6 0 1 0 0 1 15 1 1 1 10

173 0 1 1 0 0 0 1 20 0 0 0 0

174 1 71 1 0 0 0 0 19 0 0 1 1

175 0 5 0 0 0 0 0 18 0 0 0 0

176 0 2 0 1 0 0 1 19 0 0 0 0

177 0 25 1 0 0 1 1 12 1 0 0 0

178 0 185 0 0 0 1 0 20 1 0 0 0

179 0 190 0 1 0 0 1 28 2 1 1 5

180 0 1 0 0 0 0 0 18 0 0 0 0

181 0 3 1 1 1 0 1 29 44 1 0 0

182 0 54 0 0 0 0 1 17 0 0 0 0

183 0 50 1 1 0 1 0 20 1 0 0 0

184 0 89 0 0 0 0 0 18 0 0 0 0

185 0 3 1 0 0 1 0 19 0 0 0 0

186 1 177 1 1 0 0 1 17 0 0 0 0

187 0 49 1 1 0 1 1 12 1 0 0 0

188 0 189 0 0 0 0 1 19 0 0 0 0

189 0 2 1 0 0 1 0 20 13 0 0 0

190 1 127 1 1 1 1 1 17 1 0 0 0

191 0 13 0 0 0 1 0 19 0 0 0 0

192 0 31 1 0 1 1 1 19 41 1 0 0

193 0 92 0 0 0 0 1 18 0 0 1 1

194 0 3 1 1 1 1 0 9 1 0 0 0

195 0 78 0 1 0 0 0 19 0 0 0 0

196 0 183 0 0 0 0 0 18 0 0 0 0

197 0 60 0 0 0 0 1 18 0 1 0 0

198 0 175 0 0 0 0 0 21 0 0 0 0

199 0 6 0 0 0 0 1 17 0 1 0 0

200 0 3 1 1 0 1 1 19 0 0 0 0

201 1 39 0 1 1 1 0 19 0 0 0 0

202 0 85 0 0 0 0 0 16 1 1 0 0

203 1 148 0 1 1 1 1 28 13 1 1 13

204 0 2 0 0 0 0 0 18 0 0 0 0

205 0 4 0 0 0 0 0 17 0 0 0 0

206 0 3 1 1 1 1 0 11 1 0 0 0

207 1 71 1 0 1 1 0 39 29 1 1 3

208 0 57 0 0 1 0 1 17 2 1 0 0

209 1 192 1 1 0 1 1 37 1 0 0 0

210 0 188 0 1 0 1 1 23 26 1 1 43

211 0 169 1 1 1 1 1 18 30 0 0 0

212 1 155 1 0 1 1 0 34 28 1 0 0

213 0 181 0 0 0 0 0 21 0 0 0 0

214 0 23 0 0 0 1 0 13 1 0 0 0

215 0 34 0 1 0 0 0 13 13 0 0 0

216 0 1 1 0 0 1 0 19 0 0 1 1

217 1 161 1 1 1 1 1 20 0 1 0 0

218 1 39 0 1 1 0 1 30 11 1 1 11

219 0 3 1 0 0 0 0 15 1 0 0 0

220 1 17 1 1 0 0 1 12 23 1 0 0

221 0 94 0 1 1 1 1 22 0 1 1 75

222 0 14 0 0 0 0 0 20 0 0 0 0

223 0 25 1 0 1 1 1 19 59 1 1 56

224 0 19 0 0 1 0 1 21 5 0 0 0

225 0 2 1 0 1 1 1 12 9 0 0 0

226 0 24 0 0 0 0 0 18 0 0 0 0

227 0 1 1 1 0 1 0 23 1 0 0 0

64


228 0 50 0 1 0 0 0 19 0 0 0 0

229 0 105 1 1 0 0 0 21 0 0 0 0

230 0 6 0 0 0 0 0 18 0 1 0 0

231 0 63 0 1 1 0 1 17 4 1 1 7

232 0 1 1 0 1 1 0 10 1 1 0 0

233 0 24 1 1 0 1 0 13 1 0 0 0

234 1 39 0 1 0 0 0 30 3 1 0 0

235 0 9 1 0 1 1 1 22 69 1 0 0

236 0 3 1 0 1 1 0 20 6 0 0 0

237 0 191 0 0 0 0 0 21 0 0 0 0

238 0 3 1 1 0 0 1 23 9 0 1 10

239 0 59 1 1 0 1 1 17 4 1 1 6

240 0 186 0 0 0 1 0 19 0 0 0 0

241 0 2 0 0 0 0 0 20 0 0 0 0

242 0 53 1 0 0 1 1 17 17 0 0 0

243 0 13 0 1 0 0 1 11 2 1 0 0

244 0 33 1 1 0 0 0 20 0 0 0 0

245 0 2 1 1 1 1 0 16 1 0 0 0

246 1 41 0 1 0 0 0 18 0 0 1 60

247 0 2 0 0 0 0 0 18 0 0 0 0

248 0 1 1 1 0 1 0 8 1 0 1 1

249 0 175 1 0 0 1 1 19 3 0 1 4

250 1 184 1 1 1 1 1 25 51 1 0 0

251 0 32 0 1 0 0 1 15 10 1 1 14

252 0 1 1 1 0 1 0 16 0 0 0 0

253 0 171 0 0 0 0 1 16 1 1 0 0

254 0 3 1 0 0 1 1 17 0 0 0 0

255 0 98 0 1 1 1 1 20 0 0 1 30

256 0 2 0 0 1 0 1 29 14 1 0 0

257 0 56 0 0 0 0 1 18 0 0 0 0

258 1 178 0 1 1 0 1 15 48 1 0 0

259 0 185 1 1 0 1 1 16 22 0 0 0

260 1 133 1 1 1 0 0 24 126 1 0 0

261 0 100 0 1 0 0 0 19 0 0 0 0

262 0 30 0 0 0 0 1 18 5 1 1 5

263 0 49 0 0 0 0 1 18 0 1 0 0

264 0 72 1 1 1 1 1 21 1 0 0 0

265 1 135 1 1 1 0 1 20 27 1 0 0

266 0 31 0 0 0 0 0 16 0 1 0 0

267 0 20 0 1 1 0 1 9 13 1 0 0

268 0 5 0 0 0 0 1 19 0 0 0 0

269 1 43 1 0 1 1 1 31 15 1 0 0

270 0 3 1 0 1 1 1 16 53 1 0 0

271 0 15 1 1 0 1 1 17 0 0 0 0

272 0 53 0 0 0 0 0 12 1 0 0 0

273 0 28 0 0 0 0 1 18 0 0 0 0

274 1 72 0 1 1 1 1 32 142 1 0 0

275 0 51 0 0 0 0 0 18 0 0 0 0

276 0 1 1 0 0 1 0 20 0 0 0 0

277 0 2 1 0 0 1 0 20 0 0 0 0

278 1 44 0 0 0 0 1 23 3 1 0 0

279 0 5 0 0 0 0 0 17 0 1 0 0

280 0 57 0 1 1 0 1 13 2 1 1 2

281 0 84 0 0 0 0 0 23 2 0 0 0

282 0 178 1 0 1 1 0 17 3 0 1 14

283 0 1 1 0 1 0 1 20 0 1 0 0

284 1 44 1 0 1 1 1 37 18 1 0 0

285 0 2 1 0 0 1 0 18 0 0 0 0

286 0 6 0 0 1 0 0 13 4 1 0 0

287 0 171 0 0 0 0 1 18 0 0 0 0

288 1 12 0 1 1 1 0 37 23 1 0 0

289 0 25 1 0 0 1 0 10 1 0 0 0

65


290 0 2 1 1 0 1 1 7 1 0 0 0

291 0 24 0 1 0 0 0 18 0 0 0 0

292 0 2 0 0 0 0 0 16 0 0 0 0

293 0 23 1 1 0 1 0 39 19 1 0 0

294 1 13 1 1 0 1 1 19 7 0 0 0

295 0 23 0 0 1 1 0 14 3 0 0 0

296 0 52 0 1 1 0 0 18 4 1 0 0

297 0 79 0 1 0 0 1 19 0 0 0 0

298 0 55 1 0 0 1 1 17 0 0 0 0

299 0 28 0 0 0 0 1 20 0 0 0 0

300 0 10 0 1 0 0 1 20 0 0 0 0

301 0 72 1 0 1 1 1 20 1 1 1 17

302 0 3 0 0 0 0 0 13 1 0 0 0

303 0 30 0 1 0 0 1 21 1 1 1 7

304 0 77 0 0 0 0 1 21 0 0 0 0

305 1 24 1 1 1 0 1 33 19 1 0 0

306 0 1 0 0 0 1 0 15 5 0 0 0

307 0 34 0 1 1 1 1 18 0 1 0 0

308 0 25 0 0 0 0 1 20 0 0 0 0

309 0 185 0 0 0 0 1 15 1 1 0 0

310 1 47 0 0 1 1 1 27 12 1 0 0

311 0 24 0 1 0 0 0 17 0 0 0 0

312 0 52 0 1 1 0 1 15 1 1 0 0

66

Appendix III – Multivariate Logistic Regression Modelling

Block 0: Beginning Block

Classification Table(a,b)

Observed Predicted

death Percentage Correct

survivor nonsurvivor Step 0

death survivor 260 0 100.0

nonsurvivor 52 0 .0 Overall Percentage 83.3

a Constant is included in the model. b The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B)

Step 0

Constant -1.609 .152 112.246 1 .000 .200

Variables not in the Equation

Score df Sig. adeqhour 15.676 1 .000 apache 75.453 1 .000 icu(1) 46.813 1 .000

Variables

abxuse(1) 10.844 1 .001

Step 0

Overall Statistics 121.275 4 .000 Block 1: Method = Forward Stepwise (Wald)

Omnibus Tests of Model Coefficients

Chi-

square df Sig. Step 68.554 1 .000 Block 68.554 1 .000

Step 1

Model 68.554 1 .000 Step 24.602 1 .000 Block 93.156 2 .000

Step 2

Model 93.156 2 .000 Step 16.469 1 .000 Block 109.625 3 .000

Step 3

Model 109.625 3 .000 Step 9.207 1 .002 Block 118.833 4 .000

Step 4

Model 118.833 4 .000

67

Model Summary

Step -2 Log

likelihood

Cox & Snell R Square

Nagelkerke R Square

1 212.596(a) .197 .332 2 187.994(b) .258 .435 3 171.525(b) .296 .499 4 162.318(b) .317 .533

a Estimation terminated at iteration number 5 because parameter estimates changed by less than .001. b Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. Hosmer and Lemeshow Test

Step Chi-

square df Sig. 1 6.527 6 .367 2 8.595 7 .283 3 5.142 8 .742 4 5.537 8 .699

Contingency Table for Hosmer and Lemeshow Test

death = survivor death = nonsurvivor Total

Observed Expected Observed Expected Step 1

1 27 27.538 1 .462 28

2 33 33.419 2 1.581 35 3 21 21.347 2 1.653 23 4 44 42.813 3 4.187 47 5 44 44.506 6 5.494 50 6 47 47.584 8 7.416 55 7 33 28.399 2 6.601 35 8 11 14.396 28 24.604 39 Step 2

1 31 31.453 1 .547 32

2 26 25.952 1 1.048 27 3 41 40.841 2 2.159 43 4 44 45.099 4 2.901 48 5 46 44.515 2 3.485 48 6 33 30.612 1 3.388 34 7 20 21.794 8 6.206 28 8 13 16.549 19 15.451 32 9

6 3.185 14 16.815 20

68

death = survivor death = nonsurvivor Total

Observed Expected Observed Expected Step 3

1 31 31.711 1 .289 32

2 32 31.327 0 .673 32 3 30 30.152 1 .848 31 4 30 29.991 1 1.009 31 5 29 29.784 2 1.216 31 6 30 29.264 1 1.736 31 7 26 27.550 5 3.450 31 8 27 24.863 4 6.137 31 9 18 18.722 13 12.278 31 10 7 6.636 24 24.364 31 Step 4

1 32 31.805 0 .195 32

2 31 31.567 1 .433 32 3 30 30.451 1 .549 31 4 31 31.093 1 .907 32 5 29 29.612 2 1.388 31 6 29 28.993 2 2.007 31 7 31 27.931 0 3.069 31 8 25 25.443 6 5.557 31 9 16 17.714 15 13.286 31 10 6 5.392 24 24.608 30

Classification Table(a)

Observed Predicted

death Percentage Correct

survivor nonsurvivor Step 1


nonsurvivor 32 20 38.5 Overall Percentage 87.2 Step 2






nonsurvivor 22 30 57.7 Overall Percentage 90.1

a The cut value is .500

69

Variables in the Equation

B S.E. Wald df Sig. Exp(B) 95.0% C.I.for

EXP(B) Lower Upper Step 1(a)

apache .233 .035 45.291 1 .000 1.262 1.180 1.351

Constant -6.520 .770 71.732 1 .000 .001 Step 2(b)

apache .201 .033 38.050 1 .000 1.223 1.147 1.304

icu(1) 1.893 .383 24.417 1 .000 6.639 3.134 14.068 Constant

-6.563 .753 76.035 1 .000 .001

Step 3(c)

adeqhour .012 .003 15.729 1 .000 1.012 1.006 1.018

apache .220 .035 38.831 1 .000 1.246 1.163 1.336 icu(1) 1.922 .402 22.829 1 .000 6.833 3.106 15.029 Constant -7.837 .927 71.398 1 .000 .000 Step 4(d)

adeqhour .012 .003 14.311 1 .000 1.012 1.006 1.018

apache .227 .036 39.467 1 .000 1.255 1.169 1.348 icu(1) 1.836 .413 19.781 1 .000 6.273 2.793 14.090 abxuse(1) 1.270 .438 8.408 1 .004 3.562 1.509 8.406 Constant -8.698 1.034 70.696 1 .000 .000

a Variable(s) entered on step 1: apache. b Variable(s) entered on step 2: icu. c Variable(s) entered on step 3: adeqhour. d Variable(s) entered on step 4: abxuse. Variables not in the Equation

Score df Sig. adeqhour 19.131 1 .000 icu(1) 28.561 1 .000

Variables

abxuse(1) 11.841 1 .001

Step 1

Overall Statistics 53.232 3 .000 adeqhour 17.651 1 .000 Variables abxuse(1) 10.480 1 .001

Step 2

Overall Statistics 25.217 2 .000 Variables abxuse(1) 9.042 1 .003 Step

3 Overall Statistics 9.042 1 .003

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