Pending Microbiology Cultures at Hospital Discharge

Pending Microbiology Cultures at Hospital Discharge

And Post-Hospital Patient Outcomes in Medicare Patients Discharged To Sub-Acute Care

by

Stacy Erin Walz

A dissertation submitted in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

(Population Health)

at the

UNIVERSITY OF WISCONSIN – MADISON

2012

Date of final oral examination: 11/06/12

The dissertation is approved by the following members of the Final Oral Committee:

Maureen A. Smith, Associate Professor, Population Health Sciences

Amy J.H. Kind, Assistant Professor, Medicine

John Mullahy, Professor, Population Health Sciences

Ajay K. Sethi, Associate Professor, Population Health Sciences

Donald Wiebe, Associate Professor, Pathology & Laboratory Medicine

i ABSTRACT

Each year, >20% of Medicare patients are re-hospitalized within 30 days, costing over

$17 billion. Pneumonia, septicemia, and urinary tract infections are common healthcare-

associated infections, and are among the top 10 reasons for re-hospitalizations in these

patients. Microbiology cultures are key tools used to detect infections, and >27% of general

medicine and sub-acute care patients are discharged from the hospital with a pending blood,

urine, or sputum culture. Whether there is a link between pending microbiology cultures at

hospital discharge and re-hospitalization, emergency department (ED) visits, or death within 30

days, remains unknown.

We retrospectively analyzed Medicare and laboratory data for 773 stroke, hip fracture,

and cancer patients discharged from a single large academic medical center to sub-acute care

in 2003-2008. Multinomial logistic regression models were used to examine relationships

between pending cultures at discharge, and death, re-hospitalization, or ED visits within 30

days. All models control for patient sociodemographics and patient medical history.

Patients with preliminary results available at discharge for their pending culture had

greater odds (1.8) of being re-hospitalized or visiting the ED for an infection within 30 days as

compared to those with no pending culture. Patients with normal final culture results returning

after discharge had greater odds (2.0) of dying within 30 days as compared to those with no

pending culture. Results were statistically significant at the 0.10 level.

In conclusion, pending microbiology cultures at discharge may be related to poor post-

hospital patient outcomes, and represent a targeted area for improvement in communication

and follow-up.

ii ACKNOWLEDGMENTS

Committee members

Maureen A. Smith, MD, PhD, MPH

Amy J. H. Kind, MD, PhD

John Mullahy, PhD

Ajay K. Sethi, PhD, MHS

Donald Wiebe, PhD

Funding Sources

This project was supported by a National Institute on Aging Beeson Career Development Award

(K23AG034551 [Kind- PI] funded by the National Institute on Aging in combination with the John

A Hartford Foundation, the Atlantic Philanthropies, the Starr Foundation and the American

Federation for Aging Research) and by a K-L2 through the National Institute of Health grant

1KL2RR025012-01[Kind-PI] [Institutional Clinical and Translational Science Award (UW-

Madison) 1UL1RR025011 (KL2) program of the National Center for Research Resources,

National Institute of Health]. Additional support was provided by the University of Wisconsin

(UW) Hartford Center of Excellence in Geriatrics, the Geriatrics Research Education and

Clinical Center at Madison VA Hospital, the University of Wisconsin Hospitals and Clinics, the

UW Health Innovation Program and the Community-Academic Partnerships core of the

University of Wisconsin Institute for Clinical and Translational Research (UW ICTR), grant

1UL1RR025011 from the Clinical and Translational Science Award (CTSA) program of the

National Center for Research Resources, National Institutes of Health.

Date of IRB Clearance

iii Initially approved M-2006-1108 as “The Hospital Discharge Summary Quality Assessment

Project” on 4/24/2006 by the University of Wisconsin Health Sciences Minimal Risk IRB. This

clearance was renewed annually and project modifications were submitted and approved by the

IRB to expand sample size, variables collected and link to Medicare data. Most recent IRB

renewal for this project, with name change to "Quality Assessment of Discharge Summaries

(QUADS) Project" was on 10/24/2012.

iv TABLE OF CONTENTS

Abstract........................................................................................................................................ i

Acknowledgments ....................................................................................................................... ii

Introduction/Specific Aims .......................................................................................................... 1

Background/Literature Review ................................................................................................... 4

Conceptual Model ...................................................................................................................... 8

Methods ....................................................................................................................................10

Manuscript #1: Pending microbiology cultures with and without preliminary results available at hospital discharge and post-hospital patient outcomes in Medicare patients discharged to sub-acute care .................................................................................................................................14

Manuscript #2: Pending microbiology cultures with and without preliminary results available at hospital discharge and re-hospitalizations or emergency department visits for infections in Medicare patients discharged to sub-acute care .......................................................................30

Manuscript #3: Final microbiology culture results available after hospital discharge and post-hospital patient outcomes in Medicare patients discharged to sub-acute care ..........................49

Conclusion ................................................................................................................................67

Bibliography ..............................................................................................................................73

Appendices ...............................................................................................................................77

Appendix A: Laboratory Information System Abstraction Form ..............................................77

Appendix B: Laboratory Information System Abstraction Manual...........................................93

Appendix C: Laboratory Information System Abstraction Reliabilities .................................. 104

Appendix D: JGIM Paper ..................................................................................................... 105

Appendix E: Editorial Response to JGIM Paper ................................................................... 111

Appendix F: Parametric Survival Analyses .......................................................................... 113

Appendix References .......................................................................................................... 119

v

TABLES AND FIGURES

Table 1.1. Study Sample Characteristics for Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities, 2003-2008 (N=773) ................................................................................................................... 24

Table 1.2. Multinomial Logistic Regression Analyses of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer and Pending Microbiology Cultures Discharged to Sub-acute Care Facilities, 2003-2008 (N=768) ................................................................................................................... 26


Table 2.2. Multinomial Logistic Regression Analyses of Reasons for Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities, and Pending Microbiology Cultures, 2003-2008 (N=773) ............................................................................... 44


Table 3.2. Multinomial Logistic Regression Analyses of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities and Final Results of Microbiology Cultures, 2003-2008 (N=768) .................................................................................................... 62

Appendix Table. Parametric Survival Analyses of a Combined Outcome of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer and Pending Blood, Urine, or Sputum Cultures Discharged to Sub-acute Care Facilities, 2003-2008, (N=768) .................................. 118

1 INTRODUCTION/SPECIFIC AIMS

Approximately 20% of all hospitalized Medicare patients will be re-hospitalized or visit the

emergency department (ED) within 30 days of hospital discharge, and these visits account for

over $17 billion in payments each year (1). Sepsis, urinary tract infection, and pneumonia are in

the top ten reasons for re-hospitalizations in this population (1), and these types of infections

are often nosocomial in origin. Healthcare-associated infections (HAI) are associated with

higher healthcare costs, considerable patient morbidity and mortality, and may indicate poor

quality of care (2). Patients discharged to sub-acute care (skilled nursing, rehabilitation, long-

term care facilities) are especially vulnerable because they have complex medical problems and

are often unable to advocate for themselves (3). The underlying healthcare system factors that

cause re-hospitalizations and ED visits after discharge for these patients are poorly understood,

but may be related to poor communication between the inpatient and outpatient settings.

Outpatient physicians may need to follow-up on tests ordered in the hospital where the results

are not known (i.e., “pending”) at hospital discharge. In particular, the results of microbiology

cultures are still pending at discharge for 25% of patients discharged to sub-acute care (4), and

cultures are a critical tool used to identify infections. Outpatient physicians caring for these

patients after discharge may not be aware that these cultures were performed during the

hospital stay. If the final culture result is determined after discharge to be clinically important

(i.e., would change patient care) and the outpatient physician is unaware of this result, patients

may receive less-than-optimal outpatient care, have missed opportunities to diagnose and treat

infections at an early stage, and their condition may ultimately worsen, leading to re-

hospitalization, ED visit, or death. Despite these concerns, no studies have examined whether

patients with pending cultures have poorer outcomes.

2

In our previous work, pending laboratory tests in general were rarely communicated at

discharge (4). This dissertation serves as the first step in understanding what information is

most important to communicate and when. It may be possible to identify patients at discharge

for whom pending labs should be followed more closely, or we may find that it is important to

communicate clinically-important final results of pending cultures that become available after

discharge. For example, preliminary culture results (on which clinical decisions may be made)

may be available to the hospital physician at discharge. In our prior work, preliminary results

were not available for 82% of pending urine cultures and 19% of pending blood cultures (4). If

knowing preliminary results at discharge predicts poor patient outcomes, the availability of

preliminary results could potentially be used to trigger closer follow-up of some patients.

The goal of this dissertation is to determine whether an outcome of re-hospitalization, ED visit,

and/or death is related to a lack of preliminary information on culture results at discharge, or

abnormal final culture results that become available after discharge. Because they are likely to

be most vulnerable, we examine Medicare patients discharged to sub-acute care with principal

discharge diagnoses of stroke, hip fracture or cancer. The most common diagnoses in sub-

acute care are stroke and hip fracture; cancer patients are included also because they are at

increased risk of infection. Our long-term goal is to determine whether re-hospitalizations, ED

visits, or deaths caused by sub-optimal diagnosis and management of infections can be

identified. If so, potential interventions such as enhancing communication between inpatient

and outpatient physicians for high-risk patients could be considered.

The specific aims of this study are to:

3 1) Examine whether having preliminary results available at discharge for pending blood, urine

and sputum cultures is related to re-hospitalization, ED visit, or death, for any reason, within

30 days after discharge.

HA: We expect that patients with pending cultures without preliminary results available at

discharge will have a greater likelihood of re-hospitalization, ED visit or death.

2) Examine whether having preliminary results available at discharge for pending blood, urine

and sputum cultures is related to re-hospitalization or ED visit for an infection, specifically,

within 30 days after discharge.

HA: We expect that patients with pending cultures without preliminary results available at

discharge will have a greater likelihood of re-hospitalization or ED visit for infection.

3) Examine whether having clinically important final results for pending blood, urine and

sputum cultures is related to re-hospitalization, ED visit, or death, within 30 days after

discharge.

HA: We expect that patients with abnormal final results from pending cultures will have a

greater likelihood of re-hospitalization, ED visit or death.

To accomplish these aims, we will link data on pending microbiology cultures from the

laboratory information system of a large Midwestern academic hospital to Medicare claims and

enrollment data for patients discharged to sub-acute care with principal discharge diagnoses of

stroke, hip fracture or cancer.

4 BACKGROUND/LITERATURE REVIEW

Re-hospitalizations are common and costly

A “bounce-back” is considered a movement from a less intense to a more intense health care

setting (i.e., from a skilled nursing facility or home to an acute care hospital) within 30 days of

hospital discharge (5). Approximately 20% of Medicare patients experience a re-hospitalization

or emergency department (ED) visit with 30 days of discharge (1, 5, 6), accounting for over $17

billion in Medicare payments each year (1). Re-hospitalizations in particular are perceived as a

failure of the system, and as such, the Centers for Medicare and Medicaid Services (CMS) have

restructured hospital payments to financially encourage re-hospitalization prevention efforts (7).

Given the growing financial burden and undesirable patient health outcomes, health care

systems need to identify the factors that influence re-hospitalizations in hopes of creating

targeted interventions.

Patients discharged to sub-acute care facilities are at a high risk of bouncing-back

Sub-acute care is considered skilled nursing, rehabilitation, and long-term care facilities.

Individuals discharged to sub-acute care facilities have complex medical problems that need to

be followed closely, and they are often unable to advocate for themselves (3). Patients

discharged to sub-acute care are at high risk of re-hospitalization or ED visit within 30 days of

discharge (7, 8). In particular, discharge to a skilled nursing facility is a strong predictor of

bouncing-back in acute stroke patients (8). Patients with primary diagnoses of stroke and hip

fracture represent some of the most common populations and geriatric syndromes in sub-acute

care (9, 10). Patients with cancer diagnoses are at high risk for infection (11).

Infections are a common reason for re-hospitalization

5 Only recently have studies begun to identify infections as a common reason for re-

hospitalization, in both Medicare and non-Medicare patients. Thirteen percent of re-

hospitalizations in Medicare patients are for infections, with 50% of these being blood and

urinary tract infections (1). A recent study in Pennsylvania revealed that of all patients

discharged from hospitals in that state in 2009, 6.2% were re-hospitalized within 30 days for an

infection or complication related to an infection (12).

Healthcare-associated infections

Infections acquired during a hospitalization include both those associated with a device (i.e.,

ventilator-associated pneumonia or catheter-related urinary tract infection) and those caused by

multi-drug resistant microorganisms (i.e., vancomycin-resistant Entercoccus or methicillin-

resistant Staphylococcus aureus). Healthcare-associated infections (HAI) are responsible for

significant patient morbidity and mortality and increased healthcare costs and litigations (2, 13,

14). Despite extensive infection-prevention efforts employed in most hospitals, HAIs continue to

be a problem (15). Since 2008, HAIs have been targeted by CMS financial penalties to

hospitals because they are considered to be largely preventable.

Pending microbiology tests at discharge are common

A lab test that is ordered during hospitalization for which the final result is not available at

discharge is considered a pending lab test. Patients discharged to sub-acute care facilities

frequently (32%) leave the hospital with a pending test (4). In a study of general medicine

patients, 10% of pending test results were deemed potentially actionable (16), meaning patient

care would have been modified based upon the result. Our previous work, among others,

highlighted that pending microbiology cultures are especially common (16, 17). Microbiology

cultures are designed to detect an infectious process, and clinically-important culture results will

6 impact the care a patient receives. Laboratories often provide preliminary culture results to

clinicians as organisms are detected, and clinical decisions may be made based upon

preliminary results (18-20).

Pending microbiology tests at discharge are poorly communicated

Despite pending lab tests being common among both general medicine (16) and sub-acute care

patients (4), they are not frequently communicated at discharge (4, 17). Ideally, the existence of

a pending microbiology culture would be communicated at discharge so that necessary follow-

up can occur in the outpatient setting. The hospital discharge summary is the only mandated

discharge document, and it is intended to inform the outpatient provider what happened to their

patient in the hospital so they can plan for outpatient care needs (21). From our previous work

and others, pending lab tests in general are frequently (89%) omitted from discharge summaries

(4, 17). When final microbiology culture results become available, they are routed to the

hospital-based provider (22), who usually differs from the outpatient provider. Once the patient

has been discharged, however, the hospital-based provider no longer oversees the patient’s

care, and the continuity of care is interrupted (23). Despite the existence of electronic

laboratory information systems (LIS), integration with electronic medical records (EMR) systems

is problematic because there are dozens of manufacturers for each of these systems, and

interoperability issues are only beginning to be addressed (24). Also, patients may be

discharged to a sub-acute care facility or an outpatient provider not electronically linked to the

hospital’s system, so electronic information exchange is not always possible (25). If the

provider caring for the patient outside the hospital is unaware of the existence of a pending

microbiology culture or its final results, they may not take the necessary medical action.

Poor communication of pending tests may result in poor care

7 Lab testing related mistakes, such as losing a test result to follow-up, have been associated with

treatment delays, diagnostic errors, and malpractice claims in outpatient settings (26-28).

Missed test results have a negative impact on five of the six aims of the Institute of Medicine’s

Crossing the Quality Chasm: timeliness, safety, patient-centeredness, efficiency, and

effectiveness (29). Pending lab tests at hospital discharge are a patient safety issue because

they are easily lost to follow-up, and become an example of missed test results.

8 CONCEPTUAL MODEL The conceptual model proposes that the existence of pending cultures without preliminary

results available at hospital discharge may be associated with suboptimal post-hospital

diagnosis and management and poor patient outcomes, as possible infections may be missed.

Pending cultures that ultimately have clinically important final culture results indicating an

infection present in the peri-discharge period may receive suboptimal attention and result in

poor patient outcomes.

By looking specifically at commonly pending tests (blood, urine and sputum cultures), plus

additional information that may be available about these cultures at discharge (preliminary

results), we may identify whether these components are potential targets for discharge

communication and process improvement. By addressing these questions, we are not

necessarily proposing that patients stay in the hospital longer, waiting for final culture results to

arrive. Our goal is to determine whether having information on preliminary results is sufficient to

make the decision to discharge a patient without modifying the plan for outpatient follow-up, or if

the discharge process could be modified to follow patients at risk for poor outcomes more

closely.

In this model, poor patient outcomes are also influenced by the potentially confounding patient

factors of age, diagnosis, severity of illness, co-morbidity, and socioeconomic factors, and

hospital-based provider factors such as medical specialty of the physician discharging the

patient and day of the week of discharge.

10

METHODS

Study Sample

Hospitalized Medicare patients at a single large academic medical center with a primary

discharge diagnosis of stroke, pelvis/hip/femur fracture, or cancer who were discharged to sub-

acute care facilities from January 1, 2003, through December 31, 2008, were identified. These

discharge diagnoses were chosen because they represent common primary diagnoses in sub-

acute care patients (10). The International Classification of Diseases, 9th edition (ICD-9)

diagnosis code in the first position on the acute hospitalization discharge diagnosis list was used

to establish primary diagnosis. ICD-9 codes 431, 432, 434, and 436 were used to identify

stroke; 805.6, 805.7, 806.6, 806.7, 808, and 820 were used to identify pelvis/hip/femur fracture

(hereafter called “hip fracture”); and 153, 153.0-153.9, 154, 154.1 (colon and rectal), 162, 162.0-

162.9 (lung), 174, 174.0-174.9 (female breast), 185, and 185.0-185.9 (prostate) were used to

identify cancer.

Administrative data were used to identify discharges to sub-acute care facilities (skilled

nursing, rehabilitation, or long-term care) and discharge year. Prior to exclusions, the sample

size was 824. A small number of subjects (n=12) experienced more than one eligible

hospitalization during the 2003-2008 study period, and each of these hospitalizations was

treated as a separate event.

Hospital discharge summaries were obtained and examined for each patient. If it was

clear from the discharge summary that the patient was not discharged to sub-acute care, did not

have a diagnosis of hip fracture, stroke, or cancer, or was discharged to hospice or comfort

care, they were excluded from the study (n=51).

Institutional, physician, and supplier claims and demographic/enrollment data was

obtained from Medicare and linked to hospital administrative data, LIS data, and discharge

11 summaries by a combination of Medicare identification number, gender, age, race, and

admission and discharge dates of the index hospitalization. The linkage was accomplished

using SAS version 9.2 (30). Patients were excluded if they were a railroad retiree or enrolled in

a Medicare HMO, or if we were unable to match them to the Medicare data. The final sample

after exclusions was 773. The Institutional Review Board at the University of Wisconsin

approved this study.

Variable Definitions

Laboratory information system (LIS) data was obtained on each patient to allow for the

identification of pending microbiology cultures, preliminary results availability at hospital

discharge, and final culture results returning after discharge. Three trained medical abstractors,

using standardized abstraction protocols and forms, reviewed all LIS data for the presence or

absence of all types of pending laboratory tests. Six percent of randomly selected LIS records

were re-abstracted by a different trained abstractor. Cohen’s phi for abstractor reliability was

0.9 for the presence/absence of pending lab tests, and kappa was 0.9 for number of pending lab

tests per patient.

Urine culture results were considered preliminary if >24 hours had elapsed between

culture request and hospital discharge; blood and sputum culture results were considered

preliminary if >48 hours had elapsed. Final culture results were considered normal if there was

no growth of microorganisms, or if the laboratory deemed the specimen to be contaminated.

Culture results were deemed abnormal if one or more significant microorganisms were

identified. We focused on blood, urine, and sputum cultures because they were the most

common types of pending cultures.

12

The outcome variables were created using information within the Medicare data.

Inpatient Medicare claims were used to identify acute care re-hospitalizations within 30 days of

discharge from the index hospitalization of interest. A qualifying acute care re-hospitalization

was defined as any acute care stay that was not within a long-term care hospital, an inpatient

rehabilitation hospital, or a hospital specialty unit, and was not for rehabilitation (DRG 462).

Emergency department (ED) visits within 30 days of discharge that did not result in a

subsequent hospitalization were also identified using Medicare claims data. The Medicare

denominator file was used to determine dates of death for patients who died within 30 days of

discharge.

The reason for re-hospitalization or ED visit was created by capturing the first through

eighth diagnosis codes provided for re-hospitalization or ED visit, then categorizing each of

them using the Agency for Healthcare Research and Quality’s (AHRQ) Clinical Classification

Software (CCS). The following single-level CCS categories appearing anywhere in the first

through eighth diagnoses were considered to be a re-hospitalization or ED visit for infection: 1

(Tuberculosis), 2 (Septicemia), 3 (Bacterial infection, unspecified site), 4 (Mycoses), 8 (Other

infections, including parasitic), 76 (Meningitis), 78 (Other CNS infections), 122 (Pneumonia),

123 (Influenza), 124 (Acute and chronic tonsillitis), 125 (Acute bronchitis), 126 (Other upper

respiratory infections), 129 (Aspiration pneumonitis), 135 (Intestinal infection), 148 (Peritonitis

and intestinal abscess), 159 (Urinary tract infections), 197 (Skin and subcutaneous tissue

infections), and 201 (Infective arthritis and osteomyelitis). CCS categories related to an

inflammatory process or mechanical obstruction issue were not considered infections. All other

CCS categories not listed above were considered to be re-hospitalization or ED visit for

something other than infection.

Most control variables were obtained from Medicare data. Patient sociodemographics

included age at index hospitalization, gender, and Medicaid enrollment status. Year of hospital

13 discharge was included to account for secular trends. Disease severity during index

hospitalization was represented by a combined indicator variable for mechanical ventilation

(CPT 94656, 94657; ICD-9 96.7x) and placement or revision of a gastrostomy tube (CPT

43750, 43760, 43761, 43832, 43246; ICD-9 43.11). Using methods established by CMS, we

created a new enrollee CMS hierarchical condition category (HCC) score as a measure of risk

adjustment, using ICD-9 codes gathered 30 days prior to index hospitalization plus all codes

from the index hospitalization itself. Using information from the index hospitalization only,

comorbid conditions other than Alzheimer’s disease and dementia were identified using

methods established by Elixhauser (31). Alzheimer’s disease was identified using the definition

proposed by the Chronic Conditions Warehouse (CCW), and dementia was identified using

methods established by Taylor (32). Of the conditions identified, we included those that were

present in >5% of the sample and contributed to each of the models (p-values <0.2).

Discharging physician specialty was also included as a control variable. Physician

specialty was abstracted from publically available data, and grouped into the categories of

internal medicine, neurology, and surgery (includes neurological, ear/nose/throat, urology,

cardiothoracic, orthopedic, general, and plastic). A small percentage (4%) of the study sample

was discharged by a physician specialist type not included in the above categories, and these

were included in the neurology category.

Analyses

Analyses were performed using SAS 9.2 and STATA 12 (30, 33). Basic frequencies

were determined for all patient sociodemographic, patient medical history, and provider

characteristics. Multinomial logistic regression analyses were performed, evaluating the three

levels of each explanatory variable in relation to the three categories of each outcome variable,

including patient sociodemographic, patient medical history, and provider characteristics for

control.

14 MANUSCRIPT #1: PENDING MICROBIOLOGY CULTURES WITH AND WITHOUT PRELIMINARY RESULTS AVAILABLE AT HOSPITAL DISCHARGE AND POST-HOSPITAL PATIENT OUTCOMES IN MEDICARE PATIENTS DISCHARGED TO SUB-ACUTE CARE

This manuscript addresses specific aim #1: Examine whether having preliminary results

available at discharge for pending blood, urine and sputum cultures is related to re-

hospitalization, ED visit, or death, for any reason, within 30 days after discharge.

ABSTRACT

Background: Prevention of frequent (20%) and costly (>$17 billion/year) re-hospitalizations in

Medicare patients has become a prime focus in healthcare recently. Previous studies have

found that pending microbiology cultures at hospital discharge are common (27%) in both

general medicine and sub-acute care patients, and re-hospitalization for infection occurs within

30 days in about 13% of these. Whether there is a link between pending microbiology cultures

at hospital discharge and re-hospitalization, ED visits, or death remains unknown.

Objective: To determine if leaving the hospital with a pending microbiology culture with or

without preliminary results available predicts re-hospitalization, ED visit, or death within 30 days

of discharge, for common sub-acute care populations.

Design: Retrospective cohort study

Participants: Stroke, hip fracture, and cancer patients discharged from a single large academic

medical center to sub-acute care, 2003-2008 (N=773)

Main Measures: Multinomial logistic regression analyses of a three-category explanatory

variable on a three-category outcome variable, controlling for patient sociodemographics,

patient medical history, and discharging physician specialty.

Key Results: Patients discharged from the hospital with preliminary results available for their

pending microbiology culture had a non-significant, but notable odds ratio of 1.6 for dying within

15 30 days, but did not have greater re-hospitalization or ED visits, after controlling for patient

sociodemographics, patient medical history, and discharging physician specialty.

Conclusions: Pending microbiology cultures with preliminary results available at discharge may

be related to increased odds of dying, but not re-hospitalization or ED visit. Pending cultures

may represent a potential target for improved follow-up and communication of test results post-

discharge.

16 INTRODUCTION

Approximately 20% of Medicare patients experience a re-hospitalization or emergency

department (ED) visit within 30 days of hospital discharge, accounting for over $17 billion in

Medicare payments each year (1-3). Re-hospitalizations are perceived as a failure of the

healthcare system, and as such, the Centers for Medicare and Medicaid Services (CMS) are

restructuring hospital reimbursements to financially encourage re-hospitalization prevention

efforts (4). Patients discharged to sub-acute care facilities, such as skilled nursing homes and

rehabilitation facilities, are at especially high risk of poor post-hospital outcomes due to their

highly complex medical problems and reduced ability to advocate for themselves (2, 5).

Thirteen percent of re-hospitalizations in Medicare patients are for infections (3).

Infections are detected by performing microbiology cultures in the laboratory. Laboratories often

provide preliminary culture results to clinicians as organisms are detected, and clinical decisions

may be based upon preliminary results (6-8). Cultures ordered while the patient is in the

hospital for which final results are not available at discharge are considered pending. Pending

microbiology cultures are common in patients discharged to sub-acute care (9). Previous

studies have shown that pending tests in general are poorly communicated at discharge, which

can impact the follow-up of the test result and subsequent medical action (10, 11).

The objective of this study is to determine if leaving the hospital with a pending

microbiology culture with or without preliminary results available predicts re-hospitalization, ED

visit, or death within 30 days of discharge. Because they are likely to be more vulnerable, we

examine Medicare patients discharged to sub-acute care with principal diagnoses of stroke, hip

fracture, or cancer.

METHODS

Study Sample

17



acute care facilities from January 1, 2003, through December 31, 2008, were identified. These








identify cancer.






Hospital discharge summaries were obtained and examined for each patient. If it was

clear from the discharge summary that the patient was not discharged to sub-acute care, did not

have a diagnosis of hip fracture, stroke, or cancer, or was discharged to hospice or comfort

care, they were excluded from the study (n=51).



summaries by a combination of Medicare identification number, gender, age, race, and

admission and discharge dates of the index hospitalization. The linkage was accomplished

using SAS version 9.2 (13). Patients were excluded if they were a railroad retiree or enrolled in

18 a Medicare HMO, or if we were unable to match them to the Medicare data. The final sample

after exclusions was 773. The Institutional Review Board at the University of Wisconsin

approved this study.



identification of pending microbiology cultures, with or without preliminary results available at

hospital discharge. Three trained medical abstractors, using standardized abstraction protocols

and forms, reviewed all LIS data for the presence or absence of pending laboratory tests. Six

percent of randomly selected LIS records were re-abstracted by a different trained abstractor.

Cohen’s phi for abstractor reliability was 0.9 for the presence/absence of pending lab tests, and

kappa was 0.9 for number of pending lab tests per patient.

Patients were placed into one of three categories for the main explanatory variable: (0)

no pending culture at discharge, (1) pending blood, urine, or sputum culture at discharge with

preliminary results available, and (2) pending blood, urine, or sputum culture at discharge

without preliminary results available. Urine culture results were considered preliminary if >24

hours had elapsed between culture request and hospital discharge; blood and sputum culture

results were considered preliminary if >48 hours had elapsed. We focused on blood, urine, and

sputum cultures because they were the most common types of pending cultures.







subsequent hospitalization were also identified using Medicare claims data. The Medicare

19 denominator file was used to determine dates of death for patients who died within 30 days of

discharge. The three variables were used to create a three category outcome variable: (0) no

outcome of interest within 30 days of discharge from index hospitalization, (1) death within 30

days of discharge, or (2) re-hospitalization or ED visit without death within 30 days of discharge.



discharge was included to capture secular trends. Disease severity during index hospitalization

was represented by a combined indicator variable for mechanical ventilation (CPT 94656,

94657; ICD-9 96.7x) and placement or revision of a gastrostomy tube (CPT





comorbid conditions other than Alzheimer’s disease and dementia were identified using

methods established by Elixhauser (14). Alzheimer’s disease was identified using the definition

proposed by the Chronic Conditions Warehouse (CCW), and dementia was identified using

methods established by Taylor (15). Of the conditions identified, we included those that were

present in >5% of the sample and contributed to the overall model (p-values <0.2).


specialty was abstracted from publically available data, and grouped into the categories of

internal medicine, neurology, and surgery (includes neurological, ear/nose/throat, urology,

cardiothoracic, orthopedic, general, and plastic). A small percentage (4%) of the study sample

was discharged by a physician specialist type not included in the above categories, and these

were included in the neurology category.

Analyses

20 Analyses were performed using SAS 9.2 and STATA 12 (13, 16). Basic frequencies

were determined for all patient sociodemographic, patient medical history, and provider

characteristics. Multinomial logistic regression analyses were performed, evaluating the three

levels of the explanatory variable in relation to the three categories of the outcome variable,

including patient sociodemographics, patient medical history, and provider characteristics for

control. Odds ratios and 95% confidence intervals are provided.

RESULTS

Patient and Provider Characteristics

Table 1 provides an overview of the study sample characteristics. Nearly 9% (n=68) of

the study sample left the hospital with a pending blood, urine, or sputum culture that had no

preliminary results available at discharge. One quarter of the study sample experienced one or

more of the outcomes of interest within 30 days of discharge from the index hospitalization.

Patients in the study were 77 years old (SD 10 years) on average, mostly female (65%), and

primarily diagnosed with hip fracture (54%), followed by stroke (40%) and cancer (6%). A

variety of contributing co-morbid conditions were identified, including Alzheimer’s disease,

rheumatoid arthritis, congestive heart failure, dementia, and renal failure, among others. Index

hospitalization discharging provider specialties were most often surgical (39%), followed closely

by internal medicine (30%), and neurology and other specialties (31%).

Multinomial logistic regression analyses

The results of the multinomial regression analyses are presented in Table 2. Patients

discharged from the hospital with preliminary results available for their pending microbiology

cultures had an odds ratio of 1.6 for death compared to no outcome, after controlling for patient

sociodemographics, patient medical history, and discharging provider specialty. Although this

21 result is not statistically significant at the 0.05 level, the magnitude of the odds ratio is notable.

Congestive heart failure and dementia co-morbidities significantly increased the odds of dying

as compared to no outcome. Hierarchical condition category score, provider specialties

categorized as neurology and “other,” and co-morbid conditions of psychoses and renal failure

significantly increased the odds of re-hospitalization or ED visit without death, as compared to

no outcome.

DISCUSSION

Leaving the hospital with a pending culture for which preliminary results are available

may be related to increased odds of dying within 30 days of discharge from the index

hospitalization as compared to no outcome, but not related to the odds of being re-hospitalized

or visiting the ED without dying within 30 days of discharge. Despite the main results of this

study not being statistically significant, they provide important information for discussion.

The overarching problem of hospital readmissions and the billions of dollars being spent

on those readmissions is complicated and multi-factorial. Hospital administrators and

researchers around the U.S. are clamoring to identify the most impactful factors that influence

readmissions. Pending microbiology cultures, with or without preliminary results available at

discharge, may be one piece of the puzzle, but perhaps not a large enough piece to find a

statistically significant link to the post-discharge outcomes of interest. Nonetheless, pending

tests at discharge may represent a potential target for improvement, and an opportunity for

interdisciplinary collaboration. It is a relatively new phenomenon for laboratory representatives

to be asked to think outside the four walls of the laboratory itself and participate in

collaboratively solving problems that occur outside the laboratory.

A number of potential tactics could be used to address pending laboratory tests at

hospital discharge, some of which involve improving peri-discharge communication, others

22 focusing on information technology, and still others that step back further to address laboratory

test ordering behaviors and test methodologies used in the lab. Many studies have elucidated

that communication of pertinent information, not just pending laboratory tests, during the peri-

discharge period is poor (9, 11, 17-20). One potential solution to improve communication during

this critical period is assignment of a dedicated professional, such as a nurse case manager, to

oversee the discharge process and personally communicate key information to the next setting

of care and the post-hospital provider of care. With the hospital discharge summary being the

only mandated form of communication directed to the next provider of care (19), opportunities

may exist to improve the quality of information contained therein. With the increasing use of

electronic medical records (EMRs), and improved linkages between EMRs and laboratory

information systems (LIS), the potential to automatically populate fields in the hospital discharge

summary with information that exists in these electronic databases is great.

Some studies have explored using other electronic means to manage laboratory test

results. One group created a separate electronic system called “Results Manager” with mixed

success in an outpatient setting (21), and another devised an automated email to communicate

the results of pending tests to inpatient providers (22). However, neither of these studies dealt

with the issue that the physician who orders the test during the patient’s hospitalization is

usually not the same physician caring for the patient post-discharge (1, 23). Formal hospital

policies may need to be developed to designate the party responsible for following up with a test

that is pending at discharge.

With microbiology cultures being by far the most common type of pending laboratory test

in both general medicine patients and patients discharged to sub-acute care (9, 10), laboratories

may consider implementing testing methodologies with shorter turn-around times. Molecular

methods for bacterial identification are becoming more commonplace and economical, and can

improve turn-around times to a matter of hours versus days. And despite the fact that

23 laboratory data provides more than 70% of the objective data a physician can use in his or her

clinical decision-making (24), there is data to suggest that occasionally the wrong test is

ordered, or unnecessary repeat testing is requested (25, 26). If the laboratory and physicians

can work together to improve test ordering behaviors, perhaps a reduction in the prevalence of

pending laboratory tests at hospital discharge can be realized.

Our approach has some limitations. We used data from a single, large, academic

medical center, and this may limit the generalizability of the results. We used a conservative

definition of “pending,” and may have underestimated the number of patients leaving the

hospital with pending cultures by missing those with final results returning the same day as

discharge. We may also have had limited statistical power to fully characterize the relationship

between pending microbiology cultures and poor post-hospital patient outcomes. However, this

is the first study examining a potential relationship, and may serve as a springboard to larger

studies, using data from multiple hospitals and medical centers, in the future.

In conclusion, this particular study found a non-significant, but notable relationship

between pending microbiology cultures with preliminary results available at discharge and death

within 30 days, but no relationship between pending cultures and re-hospitalization or ED visit

within 30 days. Despite the lack of statistical significance, the findings highlight that pending

cultures at discharge are prevalent, and may represent a target to address the serious problem

of dying within 30 days of initial hospital discharge. Future studies should involve a larger

sample and explore post-hospital patient outcomes pre- and post-implementation of a strategy

for either reducing microbiology cultures pending at discharge or improving their communication

to the physician in the next setting of care.

24 Table 1.1. Study Sample Characteristics for Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities, 2003-2008 (N=773)

Characteristic

Total

No pending culture

Pending culture with preliminary

results

Pending culture without

preliminary results

p-value

N=773 N=611 N=94 N=68

Outcome within 30 days post-discharge

None 75 74 74 79

Death 7 6 10 6

Re-hospitalization or ED visit only 19 19 16 15 0.626

Patient demographic characteristics

Age

Average age, in years, at discharge (SD) 77 (10) 79 (10) 79 (11) 77 (10) 0.413

< 65 y, % 14 14 14 21

65-74 y, % 19 18 19 22

75-84 y, % 37 38 35 29

≥ 85 y, % 30 30 32 28 0.641

Female, % 65 64 63 75 0.190

Medicaid, % 13 12 15 18 0.423

Year of discharge

2003 17 17 23 12

2004 16 15 23 13

2005 15 15 11 21

2006 17 17 13 25

2007 18 19 16 12

2008 18 18 14 18 0.151

Patient medical history

Primary Discharge Diagnosis, %

Hip fracture 54 52 60 72

Stroke 40 42 37 24

Cancer 6 6 3 4 0.017

Comorbid conditions, %

Alzheimers disease 11 10 15 13 0.356

Rheumatoid arthritis 6 6 6 4 0.864

Congestive heart failure 19 20 11 22 0.072

Dementia 21 20 29 22 0.178

Diabetes with chronic complications 8 8 6 6 0.655

Hypertension 56 57 46 66 0.030

Hypothyroidism 20 20 19 21 0.974

Psychoses 8 8 7 7 0.982

Renal failure 10 11 6 12 0.391

Valvular disease 13 14 7 13 0.252

25

Characteristic

Total

No pending culture


results


preliminary results

p-value

N=773 N=611 N=94 N=68

Hierarchical condition category score

Score 30 days prior to discharge date (SD) 1.2 (0.3) 1.2 (0.3) 1.1 (0.3) 1.1 (0.3) 0.440

Mechanical ventilation or Gastrostomy tube, % 7 8 5 4 0.434

Provider variables

Specialty, %

Surgery 39 38 35 57

Internal Medicine 30 29 39 21

Neurology & Other Specialties 31 33 26 22

26 Table 1.2. Multinomial Logistic Regression Analyses of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer and Pending Microbiology Cultures Discharged to Sub-acute Care Facilities, 2003-2008 (N=768)

Death within 30 days of

discharge (n=52)

Re-hospitalization or ED visit within 30 days of discharge

(n=143)

Unadjusted Odds Ratio

(CI)

*Adjusted Odds Ratio

(CI)


(CI)


(CI)

Pending Culture Status

No pending culture 1.0 (Reference) 1.0 (Reference)

Pending blood, urine, or sputum culture with preliminary results available at discharge

1.5 (0.7 - 3.2) 1.6 (0.7 - 3.7) 0.8 (0.5 - 1.5) 0.9 (0.5 - 1.7)

Pending blood, urine, or sputum culture without preliminary results available at discharge

0.9 (0.3 - 2.5) 1.3 (0.4 - 4.0) 0.7 (0.4 - 1.4) 0.7 (0.3 - 1.5)

Characteristics

Age

< 65 y -- 1.0 (Reference) -- 1.0 (Reference)

65-74 y -- 0.8 (0.2 - 3.4) -- 1.1 (0.5 - 2.1)

75-84 y -- 2.3 (0.6 - 8.1) -- 0.9 (0.5 - 1.8)

≥ 85 y -- 1.9 (0.4 - 8.8) -- 0.6 (0.3 - 1.5)

Female -- 0.7 (0.3 - 1.4) -- 1.2 (0.8 - 1.9)

Medicaid -- 1.0 (0.3 - 3.4) -- 0.9 (0.4 - 1.7)

Primary Discharge Diagnosis

Stroke -- 1.0 (Reference) -- 1.0 (Reference)

Hip fracture -- 0.7 (0.3 - 1.5) -- 1.5 (0.8 - 2.6)

Cancer -- 1.8 (0.5 - 6.8) -- 1.9 (0.8 - 4.7)

Year of hospital discharge -- 1.1 (0.9 - 1.4) -- 0.9 (0.8-1.0)

Comorbid conditions

Alzheimer’s disease -- 0.3 (0.1 - 1.0) -- 1.0 (0.4 - 2.4)

Rheumatoid arthritis -- 1.2 (0.3 - 4.3) -- 1.8 (0.9 - 3.7)

Congestive heart failure -- 3.1 (1.5 - 6.6) -- 0.9 (0.5 - 1.6)

Dementia -- 2.9 (1.3 - 6.3) -- 0.9 (0.5 - 1.8) Diabetes with chronic

complications -- 0.2 (0.0 - 1.3) -- 1.1 (0.5 - 2.1)

Hypertension -- 0.8 (0.4 - 1.5) -- 1.5 (1.0 - 2.3)

Hypothyroidism -- 0.5 (0.2 - 1.2) -- 0.8 (0.5 - 1.4)

Psychoses -- 0.9 (0.2 - 3.1) -- 2.4 (1.2 - 4.6)

Renal failure -- 1.6 (0.6 - 4.2) -- 2.6 (1.4 - 4.8)

Valvular disease -- 0.4 (0.1 - 1.2) -- 1.4 (0.8 - 2.5)

27

Death within 30 days of

discharge (n=52)

Re-hospitalization or ED visit within 30 days of discharge

(n=143)


(CI)


(CI)


(CI)


(CI)


Score 30 days prior to discharge date -- 2.4 (0.4 - 17.4) -- 3.3 (1.1 - 9.8)

Mechanical ventilation or Gastrostomy tube -- 2.3 (0.8 - 6.4) -- 1.2 (0.6 - 2.6)

Provider Specialty

Surgery -- 1.0 (Reference) -- 1.0 (Reference)

Internal Medicine -- 1.4 (0.6 - 3.2) -- 1.3 (0.8 - 2.1)

Neurology and Other Specialties -- 1.1 (0.4 - 2.8) -- 1.8 (0.9 - 3.1)

*Adjusted by including all control variables in the model

28 REFERENCES

1. Coleman EA. Falling through the cracks: Challenges and opportunities for improving transitional care for persons with continuous complex care needs. J Am Geriatr Soc. 2003;51:549-555.

2. Kind AJ, Smith MA, Frytak JR, Finch MD. Bouncing back: Patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke. J Am Geriatr Soc. 2007;55:365-373.

3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360:1418-1428.

4. Medicare Payment Advisory Commission (U.S.). Report to the Congress : improving incentives in the Medicare program. Washington, DC: Medicare Payment Advisory Commission; 2009.

5. Sahyoun NR, Pratt LA, Lentzner H, Dey A, Robinson KN. The changing profile of nursing home residents: 1985-1997. Aging Trends. 2001 Mar;4:1-8.

6. Berild D, Mohseni A, Diep LM, Jensenius M, Ringertz SH. Adjustment of antibiotic treatment according to the results of blood cultures leads to decreased antibiotic use and costs. J Antimicrob Chemother. 2006;57:326-330.

7. McIsaac WJ, Moineddin R, Ross S. Validation of a decision aid to assist physicians in reducing unnecessary antibiotic drug use for acute cystitis. Arch Intern Med. 2007;167:2201-2206.

8. Swanson JM, Wood GC, Croce MA, Mueller EW, Boucher BA, Fabian TC. Utility of preliminary bronchoalveolar lavage results in suspected ventilator-associated pneumonia. J Trauma. 2008;65:1271-1277.

9. Walz SE, Smith M, Cox E, Sattin J, Kind AJ. Pending laboratory tests and the hospital discharge summary in patients discharged to sub-acute care. J Gen Intern Med. 2011;26:393-398.

10. Roy CL, Poon EG, Karson AS, Ladak-Merchant Z, Johnson RE, Maviglia SM, et al. Patient safety concerns arising from test results that return after hospital discharge. Ann Intern Med. 2005;143:121-128.

11. Were MC, Li X, Kesterson J, Cadwallader J, Asirwa C, Khan B, et al. Adequacy of hospital discharge summaries in documenting tests with pending results and outpatient follow-up providers. J Gen Intern Med. 2009;24:1002-1006.

12. Deutsch A, Fiedler RC, Iwanenko W, Granger CV, Russell CF. The Uniform Data System for Medical Rehabilitation report: patients discharged from subacute rehabilitation programs in 1999. Am J Phys Med Rehabil. 2003;82:703-711.

29 13. SAS Statistical Software [Computer program]. Version 8.2. Cary, NC: SAS Institute;

2002.

14. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36:8-27.

15. Taylor DH, Jr., Fillenbaum GG, Ezell ME. The accuracy of Medicare claims data in identifying Alzheimer's disease. J Clin Epidemiol. 2002;55:929-937.

16. Stata Statistical Software [Computer program]. Version 12. College Station, TX: StataCorp LP; 2011.

17. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141:533-536.

18. Kind A, Smith M. Documentation of Mandated Discharge Summary Components in Transitions from Acute to Sub-Acute Care. AHRQ Patient Safety: New Directions and Alternative Approaches. 2008;2:179-188.

19. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care. JAMA. 2007;297:831-841.

20. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Archives of Internal Medicine. 2007;167:1305-1311.

21. Poon EG, Wang SJ, Gandhi TK, Bates DW, Kuperman GJ. Design and implementation of a comprehensive outpatient Results Manager. J Biomed Inform. 2003;36:80-91.

22. Dalal AK, Schnipper JL, Poon EG, Williams DH, Rossi-Roh K, Macleay A, et al. Design and implementation of an automated email notification system for results of tests pending at discharge. J Am Med Inform Assoc. 2012;19:523-528.

23. Wahls T, Haugen T, Cram P. The continuing problem of missed test results in an integrated health system with an advanced electronic medical record. Jt Comm J Qual Patient Saf. 2007;33:485-492.

24. Forsman RW. Why is the laboratory an afterthought for managed care organizations? Clin Chem. 1996;42:813-816.

25. Astion ML, Shojania KG, Hamill TR, Kim S, Ng VL. Classifying laboratory incident reports to identify problems that jeopardize patient safety. Am J Clin Pathol. 2003;120:18-26.

26. Plebani M. Exploring the iceberg of errors in laboratory medicine. Clin Chim Acta. 2009;404:16-23.

30

MANUSCRIPT #2: PENDING MICROBIOLOGY CULTURES WITH AND WITHOUT PRELIMINARY RESULTS AVAILABLE AT HOSPITAL DISCHARGE AND RE-HOSPITALIZATIONS OR EMERGENCY DEPARTMENT VISITS FOR INFECTIONS IN MEDICARE PATIENTS DISCHARGED TO SUB-ACUTE CARE

This manuscript addresses specific aim #2: Examine whether having preliminary results

available at discharge for pending blood, urine and sputum cultures is related to re-

hospitalization or ED visit for an infection, within 30 days after discharge.

ABSTRACT

Background: Thirteen percent of re-hospitalizations of Medicare patients within 30 days of

hospital discharge are for an infection. Prevention of costly (>$17 billion/year) re-hospitalizations

in Medicare patients has become a prime focus in healthcare recently. Previous studies have

found that pending microbiology cultures at hospital discharge are common (27%) in both

general medicine and sub-acute care patients. Whether there is a link between pending

microbiology cultures at hospital discharge and re-hospitalization or emergency department

(ED) visit for infection-related reasons remains unknown.

Objective: To determine if leaving the hospital with a pending microbiology culture with or

without preliminary results available predicts re-hospitalization or ED visit for an infection-related

reason within 30 days of discharge for common sub-acute care populations.




Main Measures: Multinomial logistic regression models of a three category explanatory variable

on a three category outcome variable, controlling for patient sociodemographics and patient

medical history.

31 Key Results: Patients discharged from the hospital with preliminary results available for their

pending microbiology culture had an odds ratio of 1.8 for visiting the ED or being re-hospitalized

for an infection within 30 days as compared to patients experiencing no adverse outcome after

controlling for patient sociodemographics and patient medical history.

Conclusions: Pending microbiology cultures with preliminary results available at discharge were

related to increased odds of re-hospitalization or ED visit for an infection within 30 days.

Pending cultures may represent a potential target for improved follow-up and communication of

test results post-discharge.

32 INTRODUCTION

Over $17 billion is spent each year by Medicare for the approximately 20% of Medicare

patients experiencing a re-hospitalization or emergency department (ED) visit within 30 days of

hospital discharge (1-3). The Centers for Medicare and Medicaid Services (CMS) have begun

restructuring hospital reimbursements to financially promote re-hospitalization prevention efforts

(4). Because of their highly complex medical problems and lesser ability to advocate for

themselves, patients discharged to sub-acute care facilities, such as skilled nursing homes and

rehabilitation facilities, are at especially high risk of being re-hospitalized or visiting the ED

within 30 days (2, 5).

Sepsis, pneumonia, and urinary tract infections are among the top 10 reasons for re-

hospitalizations in Medicare patients (3), and are common nosocomial infections (6). Infections

are in part detected by performing microbiology cultures in the laboratory. A pending culture is

one that is ordered while the patient is in the hospital and for which the final result is not

available at discharge. However, laboratories often provide preliminary culture results to

clinicians as organisms are detected, and clinical decisions may be based upon preliminary

results (7-9). Previous studies have shown that pending cultures are common in both sub-acute

care and general medicine patients, and are poorly communicated at discharge (10-12).

Follow-up of the test result and subsequent medical action may be impaired.

The objective of this study is to determine if leaving the hospital with a pending

microbiology culture with or without preliminary results available predicts re-hospitalization or

ED visit within 30 days of discharge for an infection. Because they are likely to be more

vulnerable, we examine Medicare patients discharged to sub-acute care with principal

diagnoses of stroke, hip fracture, or cancer.

METHODS

33 Study Sample

We identified hospitalized Medicare patients at a single large academic medical center

with a primary discharge diagnosis of stroke, pelvis/hip/femur fracture, or cancer who were

discharged to sub-acute care facilities from January 1, 2003, through December 31, 2008.

These discharge diagnoses were chosen because they represent common primary diagnoses in

sub-acute care patients (13). The International Classification of Diseases, 9th edition (ICD-9)


to establish primary diagnosis. Stroke was identified with ICD-9 codes 431, 432, 434, and 436;

pelvis/hip/femur fracture (hereafter called “hip fracture”) was identified with codes 805.6, 805.7,

806.6, 806.7, 808, and 820; and cancer was identified by codes 153, 153.0-153.9, 154, 154.1

(colon and rectal), 162, 162.0-162.9 (lung), 174, 174.0-174.9 (female breast), 185, and 185.0-

185.9 (prostate).

Discharges to sub-acute care facilities (skilled nursing, rehabilitation, or long-term care)

and discharge year were identified using administrative data. Prior to exclusions, the sample




We obtained and examined hospital discharge summaries for each patient. We

excluded 51 patients from the study if it was clear from the discharge summary that the patient

was not discharged to sub-acute care, did not have a diagnosis of hip fracture, stroke, or

cancer, or were discharged to hospice or comfort care.




admission and discharge dates of the index hospitalization. The linkage was performed using

34 SAS 9.2 (14). Patients were excluded if they were a railroad retiree or enrolled in a Medicare

HMO or if we were unable to match them to the Medicare data. The final sample after

exclusions was 773. The Institutional Review Board at the University of Wisconsin approved

this study.


Identification of pending microbiology cultures, with or without preliminary results

available at hospital discharge, involved obtaining laboratory information system (LIS) data on

each patient. Patients were placed into one of three categories for the main explanatory

variable: (0) no pending culture at discharge, (1) pending blood, urine, or sputum culture at

discharge with preliminary results available, and (2) pending blood, urine, or sputum culture at

discharge without preliminary results available. Urine culture results were considered

preliminary if >24 hours had elapsed between culture request and hospital discharge; blood and

sputum culture results were considered preliminary if >48 hours had elapsed. We focused on

blood, urine, and sputum cultures because they were the most common types of pending

cultures.







subsequent hospitalization were also identified using Medicare claims data.

The reason for re-hospitalization or ED visit was created by capturing the first through

eighth diagnosis codes provided for re-hospitalization or ED visit, then categorizing each of

them using the Agency for Healthcare Research and Quality’s (AHRQ) Clinical Classification

35 Software (CCS). The following single-level CCS categories appearing anywhere in the first

through eighth diagnoses were considered to be a re-hospitalization or ED visit for infection: 1

(Tuberculosis), 2 (Septicemia), 3 (Bacterial infection, unspecified site), 4 (Mycoses), 8 (Other

infections, including parasitic), 76 (Meningitis), 78 (Other CNS infections), 122 (Pneumonia),

123 (Influenza), 124 (Acute and chronic tonsillitis), 125 (Acute bronchitis), 126 (Other upper

respiratory infections), 129 (Aspiration pneumonitis), 135 (Intestinal infection), 148 (Peritonitis

and intestinal abscess), 159 (Urinary tract infections), 197 (Skin and subcutaneous tissue

infections), and 201 (Infective arthritis and osteomyelitis). CCS categories related to an

inflammatory process or mechanical obstruction issue were not considered infections. All other

CCS categories not listed above were considered to be re-hospitalization or ED visit for

something other than infection. The Medicare denominator file was used to identify dates of

death for patients who died within 30 days of discharge. A three category outcome variable was

created: (0) no outcome of interest within 30 days of discharge from index hospitalization, (1)

death, or ED visit or re-hospitalization for some other reason, or (2) ED visit or re-hospitalization

for infection, with or without subsequent death.



discharge was included to account for secular trends. Disease severity during index

hospitalization was represented by a combined indicator variable for mechanical ventilation

(CPT 94656, 94657; ICD-9 96.7x) and placement or revision of a gastrostomy tube (CPT





comorbid conditions, except Alzheimer’s disease, were identified using methods established by

36 Elixhauser (15). The definition proposed by the Chronic Conditions Warehouse (CCW) was

used to identify patients with Alzheimer’s. Of the conditions identified, we included those that

were present in >5% of the sample and contributed to the overall model (p-values <0.2).

Analyses


were determined for all patient sociodemographic and patient medical history variables.

Multinomial logistic regression was performed evaluating the three category explanatory

variable in relation to the three category outcome variable, including patient sociodemographic

and patient medical history variables for control. Odds ratios and 95% confidence intervals are

provided.

RESULTS

Patient Characteristics

Study sample characteristics are provided in Table 1. Nearly 9% (n=68) of the patients

in the study were discharged with a pending blood, urine, or sputum culture for which no

preliminary results were available, and over 12% (n=94) had a pending culture at discharge with

preliminary results available. Patients in the study were mostly female (65%), 77 years old (SD

10 years) on average, and primarily diagnosed with hip fracture (54%), followed by stroke (40%)

and cancer (6%). A variety of contributing co-morbid conditions were identified, including

Alzheimer’s disease, rheumatoid arthritis, hypothyroidism, psychoses, and renal failure. Five

percent of the sample resided in a nursing home prior to index hospitalization, and 7% were on

a mechanical ventilator or had a gastrostomy tube placed or revised during the index

hospitalization.

Multinomial logistic regression

37

Table 2 presents the results of the multinomial logistic regression analyses. Patients

discharged from the hospital without preliminary results available for their pending microbiology

cultures had an odds ratio of 0.9 for death or re-hospitalization or ED visit for a non-infection

reason, and an odds ratio of 0.7 for re-hospitalization or ED visit for an infection, both compared

to patients experiencing no adverse outcome, after controlling for patient sociodemographics

and patient medical history. These results were not statistically significant. Interestingly,

patients discharged with a pending culture for which preliminary results were available had an

odds ratio of 1.8 for re-hospitalization or ED visit for an infection as compared to patients with no

outcome, and this result was statistically significant at the 0.10 level.

DISCUSSION

We did not detect a statistically significant relationship between pending cultures without

preliminarily available results at discharge and post-hospital patient outcomes as we

hypothesized. However, we did find a significant relationship (at the 0.10 level) between

pending cultures with preliminary results available at discharge and re-hospitalization or ED visit

for an infection, which deserves some discussion.

Not all pending cultures, with or without preliminary results available at discharge, are

created “equal.” Some may immediately change patient care, while others may simply confirm

what is already suspected. It is also possible that a preliminary result may be misleading,

suggesting that the culture was normal when in fact it ultimately was not. We could not capture

whether the discharging physician saw the preliminary culture results even if they were

available, or whether treatment was initiated, changed, or discontinued based on preliminarily

available results. For this study, we defined “preliminary available” strictly on how much time

had elapsed between date of specimen collection and date of hospital discharge, and we did not

38 examine what the preliminarily available results actually were (e.g., “normal,” “negative,” “gram

negative rod”).

Pneumonia, sepsis, and urinary tract infections are common nosocomial infections, often

related to devices such as catheters (6), and are among the top ten reasons for re-

hospitalization in both medical and surgical Medicare patients (3), which is why we chose to

focus on re-hospitalizations and ED visits for infections in this study. Although we did not

concretely identify the microbiology cultures in our study specific to nosocomial infections, given

the advanced age, primary diagnoses, and common devices used in treating the diagnoses of

our study population, it is highly likely that many of our study subjects’ cultures were ordered to

assist in identification of healthcare-associated infections. Microbiology cultures are a critical

tool physicians use to identify infectious microorganisms and the antibiotic treatments that will

be successful. Perhaps a patient leaving the hospital with a pending culture, regardless of

preliminary results being available or the results themselves, could become a “marker” of need

for closer or sooner follow-up or increased communication across settings of care.

With limited power to detect small differences among the groups’ impact on the

outcomes, we paid more attention to the magnitude of the odds ratios in the presence of a less

conservative alpha (0.10 versus the classic 0.05). Despite alphas of 0.05 being heralded in the

literature as the “gold standard” for declaring a significant relationship between two variables, a

less conservative alpha is sometimes indicated (17). Less conservative alphas increase the

chances of committing a Type I error, or saying there is a relationship between two variables

when in fact there isn’t. One has to weigh the impact of making a Type I error against the

gravity of the outcome. Given the outcome of re-hospitalization or ED visit for infection within 30

days of discharge, it seems reasonable to take the higher chances of saying the presence of a

pending culture at hospital discharge has an impact even if it might not. Additionally,

addressing pending cultures at hospital discharge may represent “low-hanging fruit”; that is,

39 techniques and tools to improve their communication and reduce the incidence may already be

in existence.

To improve communication of a pending culture’s presence to the next setting of care,

several potential methods could be used. Pertinent information, not just related to pending

laboratory tests, is often poorly communicated at discharge (11, 12, 18-21). If the existence of a

pending culture at discharge can serve as a “marker” of increased risk of re-hospitalization or

ED visit for an infection, perhaps the assignment of a dedicated professional, such as a nurse

case manager, to personally communicate critical information to the next setting of care could

be explored. Various automated means of communication may also be useful to improve

communication in lieu of or in addition to a personal phone call from a dedicated professional.

Some groups have tested electronic systems to manage laboratory test results or to send

emails regarding pending tests at discharge with mixed success (22, 23). A key element

missing from these studies is that the physician caring for the patient post-discharge is usually

not the same physician who orders the test during the patient’s hospitalization (1, 24). Formal

hospital policies to designate the party responsible for following up with a test that is pending at

discharge may be required.

If we consider a culture ordered during hospitalization as a proxy for a possible

healthcare-associated infection (HAI), it may be necessary to develop an algorithm for

appropriate hospital discharge when a culture is pending at the time of discharge. For instance,

if a pending blood culture is the fourth one ordered during the hospitalization and the last two

were negative, it would be deemed less important than a single urine culture ordered on a

catheterized patient the day before discharge for which no preliminary results were available.

The latter example may prompt the physician to consider postponing the hospital discharge until

at least some preliminary culture results are available to review. This approach doesn’t prevent

40 HAIs, but it may prevent expensive re-hospitalizations and ED visits for infections after

discharge.

Some institutions have had profound success in reducing the number of HAIs with

seemingly “simple” interventions such as surgical safety and device placement checklists that

require healthcare professionals to “wash their hands thoroughly” and “sterilize the site with

chlorhexidine” (25-27). These checklists are not just about performing all the steps; they are

more about empowering other healthcare professionals to “call someone out” if they fail to

perform a given step. This empowerment is also related to creating a “culture of safety” in

healthcare.





discharge. The study did not have strong statistical power; however, this is the first study

examining a potential relationship between pending laboratory tests at hospital discharge and

post-hospital infections. To improve detection of post-discharge infections and subsequently

improve our power, we may add outpatient visits for an infection to ED visits and re-

hospitalizations in future research.

In conclusion, this study revealed a statistically significant relationship (at the 0.10 level)

between pending microbiology cultures with preliminary results available at discharge, and re-

hospitalization or ED visit for an infection within 30 days. The findings highlight that pending

cultures at discharge are prevalent, and may be a small piece of the problem of re-

hospitalizations and ED visits within 30 days of initial hospital discharge, particularly for

infections. Future studies should add outpatient visits for infection to further capture the

41 outcome, and explore post-hospital patient outcomes pre- and post-implementation of a strategy

to improve identification and communication of pending microbiology cultures at discharge.


Characteristic

Total

No pending culture


results


preliminary results

p-value N=773 N=611 N=94 N=68

Outcome

None 75 74 74 79

Other re-hospitalization or ED visit with or without death, or death only 16 17 12 15

Re-hospitalization or ED visit for infection, with or without death 9 9 14 6 0.315


Age


< 65 y, % 14 14 14 21

65-74 y, % 19 18 19 22

75-84 y, % 37 38 35 29

≥ 85 y, % 30 30 32 28 0.641

Female, % 65 64 63 75 0.190

Medicaid, % 13 12 15 18 0.423

Year of discharge

2003 17 17 23 12

2004 16 15 23 13

2005 15 15 11 21

2006 17 17 13 25

2007 18 19 16 12

2008 18 18 14 18 0.151


Primary discharge diagnosis


Stroke 40 42 37 24

Cancer 6 7 3 4 0.017

Comorbid conditions

Alzheimer’s disease 11 10 15 13 0.390




Psychoses 8 8 7 7 0.982


43

Characteristic

Total

No pending culture


results


preliminary results

p-value N=773 N=611 N=94 N=68

Solid tumor without metastasis 9 9 7 9 0.904


Score 30 days prior to discharge date 1.2 (0.3) 1.2 (0.3) 1.1 (0.3) 1.1 (0.3) 0.440

Mechanical ventilation or Gastrostomy tube 7 8 5 4 0.434

Provider Specialty

Surgery 39 38 35 57


Neurology & other specialties 31 33 26 22 0.008

44 Table 2.2. Multinomial Logistic Regression Analyses of Reasons for Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities, and Pending Microbiology Cultures, 2003-2008 (N=773)

Other re-hospitalization or ED visit, with or without death, or

death only (N=124) Re-hospitalization or ED visit for infection, with or without death

(N=71)


(CI)


(CI)


(CI)


(CI)


No pending culture 1.0 (Reference) 1.0 (Reference)

Pending blood, urine, or sputum culture with preliminary results available at discharge

0.7 (0.4 - 1.4) 0.8 (0.4 - 1.6) 1.6 (0.8 - 3.0) 1.8 (0.9 - 3.5)

Pending blood, urine, or sputum culture without preliminary results available at discharge

0.8 (0.4 - 1.7) 0.9 (0.4 - 1.9) 0.6 (0.2 - 1.8) 0.7 (0.2 - 2.0)

Characteristic

Age

< 65 y -- 1.0 (Reference) -- 1.0 (Reference)

65-74 y -- 1.3 (0.6 - 2.8) -- 0.7 (0.3 - 1.9)

75-84 y -- 1.1 (0.5 - 2.4) -- 1.2 (0.5 - 3.0)

≥ 85 y -- 0.8 (0.3 - 1.5) -- 1.0 (0.3 - 3.2)

Female -- 1.1 (0.7 - 1.8) -- 0.9 (0.5 - 1.7)

Medicaid -- 0.7 (0.3 - 1.5) -- 1.2 (0.5 - 2.9)


Stroke -- 1.0 (Reference) -- 1.0 (Reference)

Hip fracture -- 1.2 (0.6 - 2.1) -- 1.2 (0.6 - 2.6)

Cancer -- 2.0 (0.8 - 5.0) -- 1.7 (0.5 - 6.0)

Discharge year -- 1.0 (0.9 - 1.1) -- 0.9 (0.7 - 1.0)

Comorbid conditions



Hypertension -- 1.2 (0.8 - 1.8) -- 1.5 (0.9 - 2.7)

Hypothyroidism -- 0.8 (0.4 - 1.3) -- 0.7 (0.3 - 1.4)

Psychoses -- 1.5 (0.7 - 3.1) -- 2.8 (1.2 - 6.3)

Renal failure -- 2.1 (1.1 - 3.9) -- 3.3 (1.5 - 6.9)

Solid tumor without metastases -- 0.9 (0.5 - 1.9) -- 0.4 (0.1 - 1.3)

Hierarchical condition category score Score 30 days prior to discharge

date -- 4.7 (1.5 - 15.0) -- 2.1 (0.5 - 9.4) Mechanical ventilation or Gastrostomy tube -- 0.9 (0.4 - 2.1) -- 2.4 (1.0 - 5.5)

45

Provider Specialty

Surgery -- 1.0 (Reference) -- 1.0 (Reference)


Neurology & other specialties -- 1.5 (0.8 - 2.9) -- 1.4 (0.6 - 3.1)


46 REFERENCES




4. Medicare Payment Advisory Commission (U.S.). Report to the Congress: Improving Incentives in the Medicare Program. Washington, DC: Medicare Payment Advisory Commission; 2009.

5. Sahyoun NR, Pratt LA, Lentzner H, Dey A, Robinson KN. The changing profile of nursing home residents: 1985-1997. Aging Trends. 2001;4:1-8.

6. Emerson CB, Eyzaguirre LM, Albrecht JS, Comer AC, Harris AD, Furuno JP. Healthcare-associated infection and hospital readmission. Infect Control Hosp Epidemiol. 2012;33:539-544.







47 13. Deutsch A, Fiedler RC, Iwanenko W, Granger CV, Russell CF. The Uniform Data

System for Medical Rehabilitation report: patients discharged from subacute rehabilitation programs in 1999. Am J Phys Med Rehabil. 2003;82:703-711.

14. SAS Statistical Software [Computer program]. Version 8.2. Cary, NC: SAS Institute; 2002.



17. Schumm WR. Statistical requirements for properly investigating a null hypothesis. Psychol Rep. 2010;107:953-971.




21. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Arch Intern Med. 2007;167:1305-1311.




25. Gawande A. The checklist: if something so simple can transform intensive care, what else can it do? New Yorker. 2007:86-101.

26. Haynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat AH, Dellinger EP, et al. A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med. 2009;360:491-499.

48 27. Weiser TG, Haynes AB, Lashoher A, Dziekan G, Boorman DJ, Berry WR, et al.

Perspectives in quality: designing the WHO Surgical Safety Checklist. Int J Qual Health Care. 2010;22:365-370.

49

MANUSCRIPT #3: FINAL MICROBIOLOGY CULTURE RESULTS AVAILABLE AFTER HOSPITAL DISCHARGE AND POST-HOSPITAL PATIENT OUTCOMES IN MEDICARE PATIENTS DISCHARGED TO SUB-ACUTE CARE

This manuscript addresses specific aim #3: Examine whether having abnormal final results for

pending blood, urine and sputum cultures is related to re-hospitalization, ED visit, or death,

within 30 days after discharge.

ABSTRACT

Background: Re-hospitalizations in Medicare patients are both frequent (20%) and costly (>$17

billion/year). Previous studies have found that pending microbiology cultures at hospital

discharge are common (27%) in both general medicine and sub-acute care patients, and re-

hospitalization for infection occurs within 30 days in about 13% of Medicare patients. Final

microbiology culture results available after hospital discharge may be related to re-

hospitalization, ED visits, or death in these patients.

Objective: To determine if final microbiology culture results returning after hospital discharge

predict re-hospitalization, ED visit, or death within 30 days, for common sub-acute care

populations.




Main Measures: Multinomial logistic regression models of a three-category explanatory variable

on a three-category outcome variable, controlling for patient sociodemographics, patient

medical history, and discharging provider specialty.

Key Results: Patients with normal microbiology culture results returning after discharge from the

hospital had an odds ratio of 2.0 for death within 30 days post-discharge as compared to

50 patients with no adverse outcome (p-value < 0.10), after controlling for patient

sociodemographics, patient medical history, and discharging provider specialty.

Conclusions: Normal microbiology culture results returning after hospital discharge were related

to increased odds of death within 30 days, but not to re-hospitalization or ED visits. Abnormal

culture results returning after hospital discharge were not related to any of the outcomes.

Improved communication and follow-up of microbiology culture results that return after

discharge may be valuable.

51 INTRODUCTION

Re-hospitalization or emergency department (ED) visit within 30 days of hospital

discharge occurs in approximately 20% of Medicare patients, accounting for more than $17

billion in Medicare payments each year (1-3). The Centers for Medicare and Medicaid Services

(CMS) are restructuring hospital reimbursements to promote re-hospitalization prevention efforts

(4). Patients discharged to sub-acute care facilities (skilled nursing homes and rehabilitation

facilities) are at especially high risk of being re-hospitalized because they are less able to

advocate for themselves and have complex medical problems (3, 5).

Thirteen percent of Medicare patients are re-hospitalized for infections (2). Cultures in

the laboratory are a key diagnostic tool to detect infections. Because microorganisms do not

grow quickly, and most labs rely on traditional identification techniques involving incubation,

cultures ordered while the patient is in the hospital may be pending at discharge. Pending

microbiology cultures are common in general medicine patients (6, 7) and in patients discharged

to sub-acute care (8). Laboratories generally route final culture results to the ordering inpatient

clinician, who is often not the same clinician caring for the patient post-discharge. Previous

studies have shown that pending tests in general are poorly communicated at discharge (6-8),

which can impact the follow-up of the test result and subsequent medical action.

The objective of this study is to determine if patients with normal or abnormal final

microbiology culture results returning after hospital discharge experience an increased risk of

re-hospitalization, ED visit, or death within 30 days of discharge. Because they are likely to be

more vulnerable, we examine Medicare patients discharged to sub-acute care with principal

diagnoses of stroke, hip fracture, or cancer.

METHODS

Study Sample

52



acute care facilities from January 1, 2003 through December 31, 2008 were identified. These








identify cancer.






After obtaining and examining hospital discharge summaries for each patient, 51

patients were excluded from the study if it was clear that the patient was not discharged to sub-

acute care, did not have a diagnosis of hip fracture, stroke, or cancer, or were discharged to

hospice or comfort care.




admission and discharge dates of the index hospitalization. SAS 9.2 (10) was used to perform

the linkage. Patients were excluded if they were a railroad retiree or enrolled in a Medicare

53 HMO or if we were unable to match them to the Medicare data. The final sample after

exclusions was 773. The Institutional Review Board at the University of Wisconsin approved

this study.



identification of microbiology cultures and the results that returned after hospital discharge.

Patients were placed into one of three categories for the main explanatory variable: (0) no

pending culture at discharge, (1) normal blood, urine, or sputum culture results returning after

discharge, and (2) abnormal blood, urine, or sputum culture results returning after discharge.

Culture results were considered normal if there was no growth of microorganisms, or if the

laboratory deemed the specimen to be contaminated. Culture results were deemed abnormal if

one or more significant microorganisms were identified. We focused on blood, urine, and

sputum cultures because they were the most common types of pending cultures.

The outcome variable was created using information within the Medicare data. Inpatient

Medicare claims were used to identify acute care re-hospitalizations within 30 days of discharge

from the index hospitalization of interest. A qualifying acute care re-hospitalization was defined

as any acute care stay that was not within a long-term care hospital, an inpatient rehabilitation

hospital, or a hospital specialty unit, and was not for rehabilitation (DRG 462). Emergency

department (ED) visits within 30 days of discharge that did not result in a subsequent

hospitalization were also identified using Medicare claims data. The Medicare denominator file

was used to determine dates of death for patients who died within 30 days of discharge. The

three variables were used to create a three category outcome variable: (0) no outcome of

interest within 30 days of discharge from index hospitalization, (1) death within 30 days, or (2)

re-hospitalization or ED visit without death within 30 days.

54



discharge was included to capture secular trends. Disease severity during index hospitalization

was represented by a combined indicator variable for mechanical ventilation (CPT 94656,

94657; ICD-9 96.7x) and placement or revision of a gastrostomy tube (CPT





comorbid conditions, except for Alzheimer’s disease and dementia, were identified using

methods established by Elixhauser (11). The definition proposed by the Chronic Conditions

Warehouse (CCW) was used to identify patients with Alzheimer’s, and dementia was identified

using methods established by Taylor (12). Of the conditions identified, we included those that

were present in >5% of the sample and significantly contributed to the overall model (p-value

<0.2).


specialty was abstracted from publicly available data, and grouped into the categories of internal

medicine, neurology, and surgery (includes neurological, ear/nose/throat, urology,

cardiothoracic, orthopedic, general, and plastic). Four percent of the study sample was

discharged by a physician specialist type not included in the above categories, and these were

added to the neurology category.

Analyses


were determined for all patient sociodemographic, patient medical history, and discharging

physician specialty. Multinomial logistic regression was performed evaluating the three-

55 category explanatory variable in relation to the three-category outcome variable, including

patient sociodemographic, patient medical history, and discharging physician specialty variables

for control. Odds ratios and 95% confidence intervals are provided.

RESULTS

Patient and Provider Characteristics

Table 1 provides the study sample characteristics. Nearly 7% (n=54) of the patients in

the study had abnormal results from a blood, urine, or sputum culture return after hospital

discharge, and over 14% (n=110) had normal results from a blood, urine, or sputum culture

return after hospital discharge. Nearly 7% (n=52) of patients died, and 19% (n=143) were re-

hospitalized or visited the ED within 30 days. Patients were primarily diagnosed with hip

fracture (54%), followed by stroke (40%) and cancer (6%), were 77 years old on average (SD

10 years), and were mostly female (65%). A variety of contributing co-morbid conditions were

identified, including Alzheimer’s disease, congestive heart failure, dementia, hypertension, and

renal failure, among others. Seven percent of the sample was on a mechanical ventilator or had

a gastrostomy tube placed or revised during the index hospitalization.

Multinomial logistic regression

The multinomial logistic regression analyses are presented in Table 2. Patients with

normal final culture results returning after discharge from the hospital had an odds ratio of 2.0

for dying within 30 days, statistically significant at the 0.10 level. Abnormal final culture results

returning after discharge appeared to have no statistically significant effect on death or re-

hospitalization or ED visit within 30 days.

DISCUSSION

56

Although we hypothesized that abnormal final culture results returning after discharge

would be related to patient outcomes, it was interesting to discover a significant relationship

between normal final results and death within 30 days of discharge.

Some possible explanations for these results exist. Perhaps the abnormal final culture

results did not have the impact we anticipated because the discharging physicians made

treatment decisions based upon preliminary results made available to them at discharge.

Certainly some clinical decisions can be made with the aid of preliminary, and not final, results

on microbiology cultures (14-16), such as initiation or change to antibiotic treatment. It may be

an indication of the physician’s gestalt—that because these particular patients were frailer or

sicker, the physician had a lower threshold of checking for infection, and more of the final

culture results were normal. Another possibility is that normal final culture results returning after

discharge are an indicator of some underlying issue that we cannot measure, perhaps an

underlying sickness that may lead us to further adjust for co-morbidities and disease severity.

These patients may have simply been sicker overall, with an infection not being the primary

medical problem.

Microbiology cultures pending at hospital discharge, for which final results are not

available, are prevalent (6, 8) and may be one small piece in the larger problem of re-

hospitalizations affecting one-fifth of Medicare patients (2). With limited power to detect small

differences among the groups’ impact on the outcomes, we paid more attention to the

magnitude of the odds ratios in the presence of a less conservative alpha (0.10 versus the

classic 0.05). Despite alphas of 0.05 being heralded in the literature as the “gold standard” for

declaring a significant relationship between two variables, a less conservative alpha is an option

(17). Less conservative alphas increase the chances of committing a Type I error, or saying

there is a relationship between two variables when in fact there isn’t. One has to weigh the

impact of making a Type I error against the gravity of the outcome. Given the outcome of re-

57 hospitalization, ED visit, or death within 30 days of discharge, it seems reasonable to take the

higher chances of saying that a final culture result returning after hospital discharge might be

related even if it is not. Additionally, addressing final culture results returning after discharge

may represent “low-hanging fruit”; that is, techniques and tools to reduce the incidence are

already in existence and can be modified to target this problem.

A number of possible ideas could be explored to address pending laboratory tests at

hospital discharge. The could involve improving communication during the peri-discharge

period, focusing on advances in information technology, and modifying laboratory test ordering

behaviors and test methodologies used in the lab. Many studies have reported that

communication of critical information, not just pending laboratory tests, during the peri-discharge

period is poor (7, 8, 18-21). One potential solution to improve communication during this critical

period is to assign a dedicated professional, such as a nurse case manager, to oversee the

discharge process and personally communicate key information to the next setting of care and

the post-hospital provider of care. With the hospital discharge summary being the only

mandated form of communication directed to the next provider of care (20), opportunities may

exist to improve the quality of information contained therein. With the increasing use of

electronic medical records (EMR), and improved linkages between EMR and laboratory

information systems (LIS), the potential to automatically populate fields in the hospital discharge

summary with information that exists in the LIS and other electronic databases is great.

Some studies have explored using other electronic means to manage laboratory test

results. One group created a separate electronic system called “Results Manager” with mixed

success in an outpatient setting (22), and another devised an automated email system to

communicate the results of pending tests to inpatient providers (23). However, neither of these

studies dealt with the issue that the physician who orders the test during the patient’s

hospitalization is usually not the same physician caring for the patient post-discharge (1, 24).

58 Formal hospital policies may need to be created to designate the party responsible for following

up with a test result that returns after discharge.



may consider implementing testing methodologies with shorter turn-around times. Molecular


reduce turn-around times from days to hours. And despite the fact that laboratory test results

provide more than 70% of the objective data a physician uses in his or her clinical decision-

making (25), there is data to suggest that occasionally the wrong test is ordered, or

unnecessary repeat testing is requested (26, 27). If the laboratory and physicians can work

together to improve test ordering behaviors, perhaps a reduction in the prevalence of pending

laboratory tests at hospital discharge can be realized.





discharge. Our identification of abnormal and normal final culture results did not capture

whether the results should have been acted upon, or change the care the patient was receiving.

However, we did identify final results that suggested poor specimen collection and grouped

them with the normal instead of the abnormal final culture results, which is a small first step in

parsing out more clinically important results from less important results. Future work could

incorporate a chart review to assess the “actionability” of abnormal laboratory test results, and

examine their relationship with poor post-hospital patient outcomes. This study did not have

strong statistical power and only detected one statistically significant relationship between

normal final culture results returning after discharge and poor post-hospital patient outcomes.

59 However, this is the first study examining a potential relationship, and may serve as a

springboard to larger studies, using data from multiple hospitals and medical centers in the

future.

In conclusion, this particular study revealed a statistically significant relationship between

patients with normal final microbiology culture results returning after discharge, and death within

30 days of discharge, as compared to patients with no adverse outcome. The findings highlight

that pending cultures at discharge are prevalent, and may be a small piece of the overall

problem of re-hospitalizations, ED visits, and death within 30 days of initial hospital discharge.

Future studies should involve a larger sample and investigate the “actionability” of laboratory

test results and their relationship with poor post-hospital patient outcomes.


Characteristic

Total No pending

culture

Normal or negative final culture results

Abnormal final culture

results

p-value N=773 N=609 N=110 N=54

Outcome within 30 days post-discharge

None 75 74 75 78

Death 7 6 10 4

Re-hospitalization or ED visit only 19 19 15 19 0.439


Age


< 65 y, % 14 13 17 17

65-74 y, % 19 18 18 24

75-84 y, % 37 38 29 39

≥ 85 y, % 30 30 35 20 0.311

Female, % 65 64 63 80 0.064

Medicaid, % 13 12 15 17 0.514

Year of discharge

2003 17 17 20 15

2004 16 15 20 17

2005 15 15 13 19

2006 17 16 16 22

2007 18 19 15 15

2008 18 18 16 13 0.881


Primary discharge diagnosis


Stroke 40 42 30 33

Cancer 6 6 3 7 0.034

Comorbid conditions

Alzheimer’s disease 11 11 14 15 0.438


Congestive heart failure 19 20 16 13 0.295

Dementia 21 20 25 28 0.235

Diabetes with chronic complications 8 8 5 7 0.574



Psychoses 8 8 5 13 0.162


Valvular disease 13 14 14 2 0.044

61

Characteristic

Total No pending

culture

Normal or negative final culture results

Abnormal final culture

results

p-value N=773 N=609 N=110 N=54


Score 30 days prior to discharge date 1.2 (0.3) 1.2 (0.3) 1.2 (0.3) 1.1 (0.3) 0.598

Mechanical ventilation or Gastrostomy tube 7 8 4 7 0.286

Provider specialty

Surgery 39 38 40 52


Neurology & other specialties 31 33 24 24 0.102

62 Table 3.2. Multinomial Logistic Regression Analyses of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer Discharged to Sub-acute Care Facilities and Final Results of Microbiology Cultures, 2003-2008 (N=768)

Death within 30 days

(n=52)

Re-hospitalization or ED visit within 30 days

(n=143)


(CI)


(CI)


(CI)


(CI)


No pending culture 1.0 (Reference) 1.0 (Reference) Blood, urine, or sputum culture with normal or negative final results returning post-discharge

1.5 (0.8 - 3.1) 2.0 (0.9 - 4.3) 0.7 (0.4 - 1.3) 0.8 (0.4 - 1.4)

Blood, urine, or sputum culture with abnormal final results returning post-discharge

0.6 (0.1 - 2.4) 0.6 (0.1 - 2.7) 0.9 (0.4 - 1.9) 0.9 (0.4 - 1.9)

Characteristic

Age

< 65 y 1.0 (Reference) 1.0 (Reference)

65-74 y -- 0.9 (0.2 - 3.7) -- 1.1 (0.5 - 2.1)

75-84 y -- 2.5 (0.7 - 9.2) -- 0.9 (0.5 - 1.8)

≥ 85 y -- 2.0 (0.4 - 9.7) -- 0.6 (0.3 - 1.5)

Female -- 0.7 (0.3 - 1.4) -- 1.2 (0.8 - 1.9)

Medicaid -- 1.1 (0.3 - 3.5) -- 0.9 (0.4 - 1.7)

Discharge year -- 1.1 (0.9 - 1.4) -- 0.9 (0.8 - 1.0)


Stroke 1.0 (Reference) 1.0 (Reference)

Hip fracture -- 0.6 (0.3 - 1.5) -- 1.4 (0.8 - 2.6)

Cancer -- 1.7 (0.4 - 6.6) -- 1.9 (0.8 - 4.7)

Comorbid conditions



Congestive heart failure -- 3.2 (1.5 - 6.7) -- 0.9 (0.5 - 1.6)

Dementia -- 2.9 (1.3 - 6.3) -- 0.9 (0.5 - 1.8)

Diabetes with chronic complications -- 0.2 (0.0 - 1.3) -- 1.1 (0.5 - 2.1)

Hypertension -- 0.8 (0.4 - 1.4) -- 1.5 (1.0 - 2.3)

Hypothyroidism -- 0.4 (0.2 - 1.1) -- 0.8 (0.5 - 1.4)

Psychoses -- 0.9 (0.2 - 3.4) -- 2.3 (1.2 - 4.5)

Renal failure -- 1.6 (0.6 - 4.4) -- 2.6 (1.4 - 4.8)

Valvular disease -- 0.4 (0.1 - 1.1) -- 1.4 (0.8 - 2.6)

Hierarchical condition category score Score 30 days prior to discharge

date -- 2.2 (0.3 - 15.0) -- 3.2 (1.1 - 9.7)

63

Death within 30 days

(n=52)

Re-hospitalization or ED visit within 30 days

(n=143)


(CI)


(CI)


(CI)


(CI)

Mechanical ventilation or Gastrostomy tube -- 2.4 (0.9 - 6.8) -- 1.2 (0.6 - 2.6)

Provider Specialty

Surgery 1.0 (Reference) 1.0 (Reference)


Neurology and Other Specialties -- 1.1 (0.4 - 2.7) -- 1.7 (0.9 - 3.1)


64 REFERENCES




4. Medicare Payment Advisory Commission (U.S.). Report to the Congress: Improving Incentives in the Medicare Program. Washington, DC: Medicare Payment Advisory Commission; 2009.

5. Sahyoun NR, Pratt LA, Lentzner H, Dey A, Robinson KN. The changing profile of nursing home residents: 1985-1997. Aging Trends. 2001;4:1-8.









65 14. Berild D, Mohseni A, Diep LM, Jensenius M, Ringertz SH. Adjustment of antibiotic

treatment according to the results of blood cultures leads to decreased antibiotic use and costs. J Antimicrob Chemother. 2006;57:326-330.







21. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Arch Intern Med. 2007;167:1305-1311.




25. Forsman RW. Why is the laboratory an afterthought for managed care organizations? Clin Chem. 1996;42:813-816.

26. Astion ML, Shojania KG, Hamill TR, Kim S, Ng VL. Classifying laboratory incident reports to identify problems that jeopardize patient safety. Am J Clin Pathol. 2003;120:18-26.

66 27. Plebani M. Exploring the iceberg of errors in laboratory medicine. Clin Chim Acta.

2009;404:16-23.

67 CONCLUSION

Two primary findings from this work can be highlighted: pending microbiology cultures

with preliminary results available at hospital discharge may be related to increased odds of re-

hospitalization or ED visit for an infection within 30 days, and normal final culture results

returning after discharge may be related to increased odds of dying within 30 days. These two

findings were not significant at the classic alpha level of 0.05, but at the 0.10 level.

With limited power to detect small differences among the groups’ impact on the

outcomes, we paid more attention to the magnitude of the odds ratios in the presence of a less

conservative alpha (0.10 versus the classic 0.05). Despite alphas of 0.05 being heralded in the

literature as the “gold standard” for declaring a significant relationship between two variables,

one can certainly provide an argument for using a less conservative alpha (34). Less

conservative alphas increase the chances of committing a Type I error, or saying there is a

relationship between two variables when in fact there isn’t. One has to weigh the impact of

making a Type I error against the gravity of the outcome. Given the serious outcomes of re-

hospitalization, ED visit, or death within 30 days of discharge, it seems reasonable to take the

higher chances of saying a pending culture at hospital discharge or a final culture result

returning after discharge is related to the outcome even if it might not be. It is possible that the

negative effects of pending microbiology cultures at discharge are simply not detectable at a

population level, but in the clinical world, even a single patient harmed is one patient too many.

The relationship between pending cultures with preliminary results available at discharge

and increased likelihood of re-hospitalization or ED visit for infection in Manuscript #2, and the

lack of a relationship between the same explanatory variable and re-hospitalization or ED visit

for any reason in Manuscript #1, supports our focus on post-hospital infections, and may nudge

future work to stay focused on infections. Our work expanded the outcome definition to include

68 ED visits in addition to re-hospitalizations for infection. To characterize the post-hospital

infection outcome variable even further, we can consider inclusion of outpatient visits for

infection in future studies. Since microbiology cultures are a critical tool physicians use to

identify the presence of an infection and decide which antibiotic will be most useful for

treatment, perhaps pending microbiology cultures at discharge could become a “marker” for the

need for closer and/or improved follow-up during the peri- and post-discharge periods.

In a study of Medicare patients re-hospitalized within 30 days after discharge,

pneumonia, sepsis, and urinary tract infections were among the top ten reasons for re-

hospitalization (1). Pneumonia, sepsis, and urinary tract infections, particularly related to

devices such as catheters and ventilators, are common nosocomial infections (13). Although

we did not concretely identify the microbiology cultures in our study specific to nosocomial

infections, given the advanced age, primary diagnoses, and common devices used in treating

the diagnoses of our study population, it is highly likely that many of our study subjects’ cultures

were ordered to assist in identification of healthcare-associated infections.

The relationship between normal final culture results returning after discharge and an

increased likelihood of death within 30 days in Manuscript #3 was a somewhat unexpected

finding, although some possible explanations exist. It may be an indication of the physician’s

gestalt; that because these particular patients were frailer or sicker, the physician had a lower

threshold of checking for infection, and more of the final culture results were normal. It may be

that normal final culture results returning after discharge are an indicator of some underlying

variable that we cannot measure, perhaps an underlying illness or marker of frailty that may

lead us to further adjust for co-morbidities and disease severity in future work. Regardless of

the precise reason for the finding in Manuscript #3, which we may or may not be able to identify,

culture results returning after discharge can similarly serve as a “marker” for the need for closer

and/or improved follow-up during the post-discharge period.

69

On one end of the spectrum, we can view pending cultures at discharge and final

culture results returning after discharge as “markers” of risk for re-hospitalization, ED visit, or

death within 30 days. On the other end of the spectrum, we can view these pending cultures as

failures of the system, requiring a different perspective when proposing solutions.

If we view them as “markers,” then steps to identify high risk patients and initiate a more

focused follow-up during the peri- and post-discharge periods may be initiated. Several studies

have explored using electronic means, such as email or stand-alone systems linked to the EMR,

to manage laboratory test results or alert physicians to the existence of pending lab tests at

discharge (35, 36). A significant missing piece is that neither of these studies dealt with the

issue that the physician who orders the test during the patient’s hospitalization is usually not the

same physician caring for the patient post-discharge (22, 37). Despite this, most laboratories

are unable to route results to anyone but the ordering physician with current systems. So not

only do the electronic systems by which results, preliminary or final, are sent need modification

to communicate with the correct physician, formal hospital policies may need to be created to

designate the party responsible for following up with a test result that returns after discharge.

Another important aspect that seems to be missing from these electronic systems is they are

created without the input of the end user, and as such, fall short in features and functionality the

end user needs. Without interdisciplinary conversations about what physicians need, what

information technology can provide, and how existing laboratory information systems and

electronic medical records already connect, an electronic system to identify patients with

pending cultures or communicate culture results post-discharge may not work as intended.

Although it is probably unavoidable that an electronic system will have a role in

identifying patients with pending cultures at discharge and in communicating final results after

discharge, there is something to be said for a “warmer” form of communication. Many studies

have reported that communication of critical information, not just pending cultures, during the

70 peri-discharge period is poor (4, 17, 21, 38-40). One potential solution to improve

communication during this critical period is to assign a dedicated professional, such as a nurse

case manager, to oversee the discharge process and personally communicate key information

to the next setting of care. If use of this dedicated professional is limited to identified “high-risk”

patients, resources are preserved and costs can be kept reasonable. Perhaps the existence of

a pending culture at discharge would serve as a “trigger” for this more personal, targeted form of

communication during this critical period.



may consider adopting testing methodologies with shorter turn-around times. Molecular


reduce turn-around times from days to hours. Perhaps a simple reduction in the prevalence of

pending cultures at hospital discharge and final culture results returning after discharge can be

realized if different methodologies are put in place.

If we view pending cultures at hospital discharge more as a failure of the system, a

different approach is required to address the problem. A microbiology culture is ordered to aid

in identifying an infection, likely healthcare-associated if ordered at least 48 hours into a hospital

stay. If healthcare-associated infections are still viewed as largely preventable, why are they

still prevalent? Some institutions have had profound success in reducing the number of HAIs

with seemingly “simple” interventions such as surgical safety and device placement checklists

that require healthcare professionals to “wash their hands thoroughly” and “sterilize the site with

chlorhexidine” (41-43). These checklists are not just about performing all the steps; they are

more about empowering other healthcare professionals to “call someone out” if they fail to

perform a given step. This empowerment is also related to creating a “culture of safety” in

healthcare.

71

If we consider a culture ordered during hospitalization as a proxy for a possible

healthcare-associated infection, it may be necessary to develop an algorithm for appropriate

hospital discharge when a culture is pending at the time of discharge. For instance, if a pending

blood culture is the fourth one ordered during the hospitalization and the last two were negative,

it would be deemed less important than a single urine culture ordered on a catheterized patient

the day before discharge for which no preliminary results were available. The latter example

may prompt the physician to consider postponing the hospital discharge until at least some

preliminary culture results are available to review. This approach doesn’t prevent HAIs, but it

may prevent expensive re-hospitalizations and ED visits for infections after discharge.





discharge. Our identification of abnormal and normal final culture results did not capture

whether the results should have been acted upon, or change the care the patient was receiving.

However, we did identify final results that suggested poor specimen collection and grouped

them with the normal instead of the abnormal final culture results, which is a small first step in

parsing out more clinically important results from less important results. Future work could

incorporate a chart review to assess the “actionability” of laboratory test results, and examine

the relationship between actionable results and poor post-hospital patient outcomes. As

mentioned previously, adding outpatient visits for infection within 30 days to re-hospitalizations

and ED visits will improve our power and strengthen our argument.

Future research on pending cultures at hospital discharge should assess whether or not

poor post-hospital patient outcomes are reduced in number or severity after the introduction of

an intervention, understanding that interventions are resource-heavy in dollars, people, and

72 time. Regardless of whether we view pending cultures at hospital discharge as “markers” of

high-risk patients or as failures of the system, we need to be able to readily identify them in real

time. This identification process will likely need to be electronic, and will require that many

multidisciplinary conversations occur to discuss information technology capability, end-user

functionality, and linkage of existing systems. The development of an identification tool would

need to occur prior to implementation of any sort of improved peri-discharge communication

protocol or hospital discharge algorithm. I envision these studies being performed within a

healthcare system or facility that has an interest in identifying patients at high risk of poor post-

hospital outcomes and is willing to assist in both developing and implementing interventions to

address this problem. And of course, a multi-year source of funding would need to be identified

long before initiation of a project of this magnitude. But with a willing researcher with good

research skills and the ability to build and maintain relationships with partners in healthcare,

both on the clinical and administrative ends of the spectrum, a strong, successful proposal can

be submitted for funding.

In conclusion, this project revealed statistically significant relationships (at the 0.10 level)

between normal final microbiology culture results returning after discharge and death within 30

days of discharge, and pending cultures with preliminary results available at discharge and re-

hospitalization or ED visit for infection within 30 days. The findings highlight that pending

cultures at discharge are prevalent, and may be a small piece of the overall problem of re-

hospitalizations, ED visits, and death within 30 days of initial hospital discharge.

73

BIBLIOGRAPHY


2. Kang CI, Kim SH, Park WB, Lee KD, Kim HB, Kim EC, et al. Bloodstream infections caused by antibiotic-resistant gram-negative bacilli: risk factors for mortality and impact of inappropriate initial antimicrobial therapy on outcome. Antimicrob Agents Chemother. 2005;49:760-766.

3. Sahyoun NR, Pratt LA, Lentzner H, Dey A, Robinson KN. The changing profile of nursing home residents: 1985-1997. Aging Trends. 2001 Mar;4:1-8.


5. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital care transitions: Patterns, complications, and risk identification. Health Serv Res. 2004;39:1449-1465.

6. Kind AJH, Smith MA, Pandhi N, Frytak JR, Finch M. Bouncing-back: Rehospitalization in patients with complicated transitions in the first thirty days after hospital discharge for acute stroke. Home Health Care Services Quarterly. 2007;26:37-55.

7. Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy. Washington, DC: MedPAC; 2007.


9. Deutsch A, Fiedler RC, Granger CV, Russell CF. The Uniform Data System for Medical Rehabilitation report of patients discharged from comprehensive medical rehabilitation programs in 1999. Am J Phys Med Rehabil. 2002;81:133-142.


11. Vento S, Cainelli F. Infections in patients with cancer undergoing chemotherapy: aetiology, prevention, and treatment. Lancet Oncol. 2003;4:595-604.

12. Pennsylvania Health Care Cost Containment Council. The Impact of Healthcare-associated Infections in Pennsylvania, 2009. Harrisburg, PA: Commonwealth of Pennsylvania; 2011.

13. Emerson CB, Eyzaguirre LM, Albrecht JS, Comer AC, Harris AD, Furuno JP. Healthcare-associated infection and hospital readmission. Infect Control Hosp Epidemiol. 2012;33:539-544.

74 14. Goldenberg SD, Volpe H, French GL. Clinical negligence, litigation and healthcare-

associated infections. J Hosp Infect. 2012;81:156-162.

15. Umscheid CA, Mitchell MD, Doshi JA, Agarwal R, Williams K, Brennan PJ. Estimating the proportion of healthcare-associated infections that are reasonably preventable and the related mortality and costs. Infect Control Hosp Epidemiol. 2011;32:101-114.






21. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297:831-841.


23. Arora VM, Farnan JM. Care transitions for hospitalized patients. The Medical clinics of North America. 2008;92:315-324, viii.

24. Harrison JP, McDowell GM. The role of laboratory information systems in healthcare quality improvement. Int J Health Care Qual Assur. 2008;21:679-691.

25. Coleman EA, K. May, R.E. Bennett, D. Dorr, J. Harvell. Report on Health Information Exchange in Post-Acute and Long-Term Care. U.S. Department of Health and Human Services. 2007;Contract #HHS-100-03-0028:1-61.

26. Wahls TL, Cram PM. The frequency of missed test results and associated treatment delays in a highly computerized health system. BMC Fam Pract. 2007;8:32.

75 27. Kravitz RL, Rolph JE, Petersen L. Omission-related malpractice claims and the limits of

defensive medicine. Med Care Res Rev. 1997;54:456-471.

28. Shojania KG, Duncan BW, McDonald KM, Wachter RM, Markowitz AJ. Making health care safer: a critical analysis of patient safety practices. Evid Rep Technol Assess (Summ). 2001:i-x, 1-668.

29. Institute of Medicine - Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academy Press; 2001.




33. Stata Statistical Software [Computer program]. Version 8.0. College Station, TX: Stata Corporation; 1999.







40. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Archives of Internal Medicine. 2007;167:1305-1311.

41. Gawande A. The checklist: if something so simple can transform intensive care, what else can it do? New Yorker. 2007:86-101.

76 42. Haynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat AH, Dellinger EP, et al. A surgical

safety checklist to reduce morbidity and mortality in a global population. N Engl J Med. 2009;360:491-499.

43. Weiser TG, Haynes AB, Lashoher A, Dziekan G, Boorman DJ, Berry WR, et al. Perspectives in quality: designing the WHO Surgical Safety Checklist. Int J Qual Health Care. 2010;22:365-370.

77 APPENDICES

Appendix A: Laboratory Information System Abstraction Form

Tool for abstracting pending laboratory results within laboratory

logs and patient laboratory reports

Version December 2011

1. Record unique ID:

_____________

2. Study Subject ID Number: v001sID

__________________________________________

400. Abstractor ID v400uID

STACY…...………………..………………………1

AMY……………………………………………….2

PATRICK………………………………………….3

401. Data entry ID v401deID

MARISSA………..……………………………….1

ENTRY PERSON B……………………………….2

ENTRY PERSON C……………………………….3

ENTRY PERSON D……………………………….4

402. Pending Labs LIS Abstraction Date (MM/DD/YYYY): v402AbsDt

______/ ______/ __________

78

403. Admission date (MM/DD/YYYY): v403AdmDt

______/ ______/ __________

404. Discharge date (MM/DD/YYYY): v404DCdt

______/ ______/ __________

405. Patient’s year of birth (YYYY): v405YoB

__________

406. Patient’s gender (M=Male or F=Female): v406PtSex

M F

407. Specific laboratory tests pending according to LIS: v407LabP

1………………YES 0…………………NO

If NO, skip to item 409

407.01 Test Name __________________________ v407t0l1tn

407.01 Date Received in Lab (MM/DD/YYYY) v407t0l2rc

______/ ______/ __________

407.01 Date Result Reported (MM/DD/YYYY) v407t0l3rp

______/ ______/ __________

407.01 Result _______________________________ v407t0l4rs

79

407.01 Reporting Units _______________________ v407t0l5un

407.01 Result Flag (Circle one) v407t0l6fl

0…………….No flag

1…………….L (low numeric result)

2…………….LL (critically low result)

3…………….H (high numeric result)

4…………….HH (critically high result)

5…………….A (abnormal text result)

6…………….AA (critical text result)

407.01 Test Category (Circle one) v407t0l7tc

1……………..Hematology 8……………..Immunology

2……………..Coagulation 9……………..Molecular Diagnostic

3……………..Chemistry 10…………….Reference Lab (incl. WSLH)

4………………Urinalysis 11…………….Transfusion

5………………Endocrinology 12…………….Histocompatibility

6………………Flow Cytometry 13…………….Microbiology

7………………Toxicology 14…………….Miscellaneous

407.01 Another pending laboratory test to record? v407t0l8sk

1………………YES 0…………………NO


407.02 Test Name __________________________ v407t1l1tn

80


______/ ______/ __________


______/ ______/ __________

407.02 Result _______________________________ v407t1l4rs















81





1………………YES 0…………………NO


407.03 Test Name __________________________ v407t2l1tn


______/ ______/ __________


______/ ______/ __________

407.03 Result _______________________________ v407t2l4rs








82












1………………YES 0…………………NO


407.04 Test Name __________________________ v407t3l1tn


______/ ______/ __________


______/ ______/ __________

407.04 Result _______________________________ v407t3l4rs

83



















1………………YES 0…………………NO


407.05 Test Name __________________________ v407t4l1tn

84


______/ ______/ __________


______/ ______/ __________

407.05 Result _______________________________ v407t4l4rs















85





1………………YES 0…………………NO


407.06 Test Name __________________________ v407t5l1tn


______/ ______/ __________


______/ ______/ __________

407.06 Result _______________________________ v407t5l4rs








86












1………………YES 0…………………NO


407.07 Test Name __________________________ v407t6l1tn


______/ ______/ __________


______/ ______/ __________

407.07 Result _______________________________ v407t6l4rs

87



















1………………YES 0…………………NO


407.08 Test Name __________________________ v407t7l1tn

88


______/ ______/ __________


______/ ______/ __________

407.08 Result _______________________________ v407t7l4rs















89





1………………YES 0…………………NO


407.09 Test Name __________________________ v407t8l1tn


______/ ______/ __________


______/ ______/ __________

407.09 Result _______________________________ v407t8l4rs








90












1………………YES 0…………………NO


407.10 Test Name __________________________ v407t9l1tn


______/ ______/ __________


______/ ______/ __________

407.10 Result _______________________________ v407t9l4rs

91



















1………………YES 0…………………NO


If YES, record data on additional LIS abstraction sheets

92

408. Ordering Provider Name v408OrdProv

_______________________________________

409. Verify patient’s year of birth (YYYY) v409YoBChk

____________

410. Verify patient’s gender (M or F) v410PtSexChk

____________

411. Verify study subject ID v411sIDchk

____________________

412. Verify Abstractor ID v412uIDchk

STACY…...………………..………………………1

AMY……………………………………………….2

PATRICK………………………………………….3

93 Appendix B: Laboratory Information System Abstraction Manual

Tool for abstracting pending laboratory results within laboratory

logs and patient laboratory reports

MANUAL Version December 2011

Before entering any data into the EpiData form, first make sure the laboratory logs and reports are

complete. Because of the way the lab identifies and pulls the logs and reports from their system,

they occasionally pull incomplete records. Common problems are a missing log or missing report,

and a report and/or log that only contains tests ordered during the patient’s encounter in the

emergency department before being admitted. If you discover an incomplete record, please record it

in an Excel file found on ME-HIP1:

D:\HIP\DCSummary\Data_Sources\LIS_Laboratory\Raw_Data_LIS\UW_LIS_Data_2006_to_2008\

_LIS_Problem_List_2006-08. This file will be provided to the lab so they can complete the record.

Do not attempt abstraction on incomplete records.

To open the EpiData form, log on to Polk and open EpiData 3.1. Open the form found here:

P:\CCW_Local_DCSummary\EpiData_QUADS_LIS_Laboratory\_Laptop_or_Entered_Data_Dump

\LIS_data_form_version_2009_06_15

As you enter records into EpiData, please update the abstraction log found here:

\\Polk\data\CCW_Local_DCSummary\EpiData_QUADS_LIS_Laboratory\Documentation\LIS_Data

_Entry_Log_for_2006-08_data (temporary location during Polk’s upgrade is on Washington:

\\Washington\shared\CCW_Local_DCSummary\QUADS Laboratory Core\Data)

94

1. Record unique ID:

_____________

Do not record anything here. EpiData automatically creates this number as the data is entered.

2. Study Subject ID Number: v001sID

__________________________________________

Using the crosswalk on ME-HIP1 (filename

D:\HIP\DCSummary\Data_Sources\LIS_Laboratory\Raw_Data_LIS\UW_LIS_Data_2006_to_2008\

dcsummary_with_study_IDs, find the study subject’s medical record number and record the

corresponding study ID number here. Do not record the medical record number anywhere on this

form.

400. Abstractor ID v400uID

STACY…...………………..………………………1

AMY……………………………………………….2

PATRICK………………………………………….3

Type the number corresponding to the person who is performing the abstraction.

401. Data entry ID v401deID

COLLEEN.………..……………………………….1

JOYLYNN………..……………………………….2

ENTRY PERSON C (STACY)…………………….3

ENTRY PERSON D……………………………….4

Type the number corresponding to the person who is entering the data from the form into EpiData.

95

402. Pending Labs LIS Abstraction Date (MM/DD/YYYY): v402AbsDt

______/ ______/ __________

Record the date the LIS abstraction is taking place.

403. Admission date (MM/DD/YYYY): v403AdmDt

______/ ______/ __________

Using the crosswalk file on ME-HIP, record the corresponding date of admission for this particular

study subject.

404. Discharge date (MM/DD/YYYY): v404DCdt

______/ ______/ __________

Using the crosswalk file on ME-HIP, record the corresponding date of discharge for this particular

study subject.

405. Patient’s year of birth (YYYY): v405YoB

__________

Each study subject’s LIS data should be contained within at least 2 files, a log and a report, labeled

with the medical record number. Occasionally, a study subject’s log or report (or both) are so

lengthy that two or more separate files were created to accommodate all the information. In these

cases, the filenames will be followed by ‘-part1’, ‘-part2’ and so on, to accommodate the number of

separate files created. The report’s filename is simply the medical record number, and is a Word

document. The log’s filename is the medical record number followed by an “L”, and is a PDF

document.

96

In instances where the same study subject contributed more than one eligible hospital stay in the

dataset, the filenames are followed by “-1” or “-2” accordingly. Before proceeding any further,

verify that both the log and the report exist for the study subject for the correct hospital stay.

The patient’s year of birth is only found on the report, near the top of each page. Only record the

year of birth, not the month and/or date.

406. Patient’s gender (M=Male or F=Female): v406PtSex

M = 1 F = 2

The patient’s gender may be found on the report or the log, near the top of each page. Record the

corresponding number.

407. Specific laboratory tests pending according to LIS: v407LabP

1………………YES 0…………………NO


First, look at the log file. Near the top of each page there are ‘start’ and ‘end’ dates that were used to

search for the LIS information. The ‘start’ date should correspond with the date of admission. The

‘end’ date should correspond with one day after the date of discharge. This is not erroneous, and

was necessary for capturing all lab tests requested up to and including the day of discharge. Verify

that the ‘start’ and ‘end’ dates are correct for the study subject’s hospital stay.

Keeping in mind the date of discharge, scroll through all the pages of the log file while looking

exclusively at the ‘released on’ date for each test. The ‘released on’ date corresponds to when the

final lab test result was reported, and can be found on the far right side of the page. A ‘released on’

97

date that is a day after discharge or later corresponds with a lab test result that returned after the

patient left the hospital. If you find one or more lab tests that meet this criterion, circle “1” and

proceed to item 407 to record the specifics of the lab test(s).

If you find a ‘released on’ date that is a day after discharge or later and the lab test is a microbiology

test, item 407 will be easier to complete by looking at the report. The log is very difficult to read for

microbiology tests, and doesn’t contain actual results for microbiology tests. The report is the best

place to identify microbiology tests that may be pending.

Ignore all ‘Glucose, POC’ tests, as they are tests performed at the patient’s bedside, and results are

available instantly. Ignore any tests that appear under the header ‘Anatomic Pathology’ on the log;

these types of types are not being included in this study. Also ignore tests called ‘Lipemia index for

QA only’, ‘Hemolysis index for QA only’ and ‘Icterus index for QA only’; these are for laboratory

use only, as an index of the quality of the patient specimen.

If you do not find any lab test results that returned the day after discharge or later, circle “0” and

proceed to item 409.

407.01 Test Name _______________________________ v407t0l1tn

The test name appears on the left hand side of the log directly below an underlined, bolded header

called ‘test name’. Make sure the test name you record is associated with the ‘release date’ that is a

day after discharge or later.

98

If you are recording a microbiology test, look at the report instead. Microbiology tests are located

under the main category called ‘Cultures and Stains’. To record a culture test name, simply prefix

‘culture’ with the ‘specimen/source’ indicated on the report. If the ‘specimen/source’ is ‘blood’,

there are often multiple specimens submitted on the same individual at the same time, from different

sites in the body. Treat each of these as an individual laboratory test, recording the site as part of the

culture test name. Example: if the ‘specimen/source’ says ‘blood/left antecubital’, then you will

record ‘blood culture- left antecubital’ for ‘test name’.


______/ ______/ __________

There is a date associated with receipt of the sample in the laboratory. It is denoted as ‘received’ on

the log, and the date appears just to the right of the bolded word ‘received’.

If the test you are recording is a microbiology test, look at the report. The date that appears to the

right of ‘collection date’ is the date you want to record here.


______/ ______/ __________

The date the result is reported is the same as the ‘released on’ date. This date appears on the far right

side of the log.

If the test you are recording is a microbiology test, look at the report. The date to the right of ‘last

update’ is the date you want to record here.

99

407.01 Result _________________________________ v407t0l4rs

The lab test result appears about five lines below an underlined, bolded header called ‘accession’ on

the log. The result is usually numeric.

If the test you are recording is a microbiology test, look at the report instead. Make sure you record

only ‘final’ results, not ‘preliminary’ results or ‘culture comments’. If more than one organism is

isolated, there may be more than one ‘final’ result for a single culture. Record all organisms isolated

and reported as ‘final’. Separate organism names with a comma. Do not record results of antibiotic

sensitivities.

407.01 Reporting Units __________________________ v407t0l5un

The lab test reporting units appear about five lines below an underlined, bolded header called ‘Pr’ on

the log. Examples of reporting units include: g/dL, mL/dL, fL/RBC, mg/dL, M/uL, mmol/L, U/L,

ng/mL, and so on.

If the test you are recording is a microbiology test, look at the report instead. The units are

associated with a ‘amount/growth rate’ for each organism isolated, and are often recorded as a

number followed by ‘CFU/mL’. You may also see the ‘growth rate’ denoted as ‘minimal’,

‘moderate’, or ‘heavy’. You may also encounter no true units for microbiology tests; if you can find

no evidence of units, you can leave this field blank.


Flags only appear on the report, not on the log. If you find a test result that returns after the date of

discharge on the log, you will need to find the same test on the report to record the result flag. The

100

dates on the left hand side of the report, for all tests except microbiology tests, correspond with the

date the specimen was collected, which is not the same as the date the result was reported. Make

sure the collection dates and times correspond with the test you’ve identified on the log. On the

report, flags appear just to the right of the test result. The flags appearing on the report correspond

directly to the choices below. Circle the appropriate number according to the flag that appears for

the test result.

For microbiology tests, there really are no flags. However, if an organism or organisms were

isolated and reported as a ‘final’ result, circle ‘5’ below for ‘abnormal text result’. If there was ‘no

growth’ or ‘growth’ was recorded as ‘none’, circle ‘0’ below for ‘no flag’.









The test category is most easily gleaned from the report. The categories are bolded and in a larger

font as compared to the rest of the text on the report, and they are located on the left hand side. All

corresponding tests that fall under that category appear below it. The categories correspond directly

101

with the choices below. Circle the appropriate number according the category under which the test

falls.



3………………Chemistry 10…………….Reference Lab Testing (incl. WSLH)






1………………YES 0…………………NO


If there is another test result that returned the day after discharge or later, circle ‘1’ and proceed to

item 407.02 to record the specifics. If there is not another test result that returned the day after

discharge or later, circle ‘0’ and proceed to item 408.

.

.

.

.

408. Ordering Provider Name ________________________ v408OrdProv

The ordering provider name appears in the upper right hand part of the report. It is not found on the

log. The name that appears to the right of ‘Ord. Dr:’ is the name to record here. Record last name, a

comma, then first name. No need to record middle initial or credentials. If no name appears to the

102

right of ‘Ord. Dr.’, record the name of the attending provider, which appears to the right of ‘Att.

Dr.’

409. Verify patient’s year of birth (YYYY) v409YoBChk

____________

The patient’s year of birth is only found on the report, near the top of each page. Only record the

year of birth, not the month and/or date. Use the report to verify the year; do not copy from the

second page of this abstraction form.

410. Verify patient’s gender (M or F) v410PtSexChk

____________

The patient’s gender may be found on the report or the log, near the top of each page. Circle the

corresponding letter. Use only the report or the log to verify the gender; do not copy from the

second page of this abstraction form.

411. Verify study subject ID v411sIDchk

____________________

Using the crosswalk, find the study subject’s medical record number and record the corresponding

study ID number here. Use only the crosswalk to find this number; do not copy from the first page

of this abstraction form.

412. Verify Abstractor ID v412uIDchk

STACY…...………………..………………………1

AMY……………………………………………….2

PATRICK………………………………………….3

103

Circle the number corresponding to the person who performed the abstraction.

104 Appendix C: Laboratory Information System Abstraction Reliabilities

One trained medical abstractor, using standardized abstraction protocols, forms, and manuals,

reviewed all LIS data for the presence or absence of pending lab tests. Six percent of randomly

selected LIS data was re-abstracted by a second trained abstractor. Cohen’s phi for abstractor

reliability was 0.9 for the presence/absence of pending lab tests, and kappa was 0.9 for number

of pending lab tests per patient.

105 Appendix D: JGIM Paper

111 Appendix E: Editorial Response to JGIM Paper

113 Appendix F: Parametric Survival Analyses

Our original analytic plan involved using parametric survival analyses with a combined

outcome variable of death, re-hospitalization, or emergency department (ED) visit within 30

days of hospital discharge. As we examined the data more closely, we decided we needed to

parse out death from re-hospitalizations and ED visits because some recent studies suggested

that socioeconomic factors are related to readmissions, but not to death, within 30 days of

discharge. As such, we created a three-category outcome variable, and were no longer able to

employ parametric survival analyses. However, the results of parametric survival analyses are

presented here.

Overview of parametric survival analysis

Cox proportional hazards models for survival analyses are often used when there are no

assumptions regarding the shape of the underlying hazard over time. The hazard function

quantifies a multiplicative effect of the explanatory variable on the outcome and is assumed

constant over time (1).

In contrast, parametric survival models specify the distribution, or shape, of the

underlying hazard, and in so doing, can improve power and statistical efficiency if the chosen

distribution “fits” the data well (2). An additional advantage of specifying certain parametric

distributions is the ability to derive both a hazard ratio and a time ratio. A time ratio describes

the explanatory variable’s effect on the “time to event”; values greater than 1 imply the

explanatory variable prolongs the time to event, and values less than 1 imply the explanatory

variable speeds up the time to event. Another way to look at it is where values less than 1

reduce the survival time (accelerate failure), and values greater than 1 increase the survival

time (decelerate failure). Time ratios are not directly comparable to hazard ratios, but can tell a

similar story (3).

114

The parametric survival models where a time ratio can be derived are considered

“accelerated failure time” models, meaning that the effect of the explanatory variable is to

stretch or shrink the survival curve along the time axis by a constant, relative amount. Non-

parametric or semi-parametric models (like Cox) are considered proportional hazards models,

and produce hazard ratios (4).

Using Stata to perform parametric survival analyses

There are many distributions from which to choose for parametric survival analyses.

Stata offers six: exponential, Wiebull, log normal, log logistic, Gompertz, and generalized

gamma. Akaike Information Criterion (AIC) is a relative goodness of fit and tool for model

selection; log likelihood can also be used. In both cases, a lower number indicates a better fit.

Before initiating the Stata programs for survival analyses, you must define the

observation period (Stata calls it “analysis time _t”) and what constitutes a “failure” using

“stset”. A “failure” is essentially the outcome of interest, such as death, development of

disease, etc. The command looks like this:

stset observation_time_varname, failure(outcome_varname)

The Stata code needed to run these analyses is quite straightforward. The command is

“streg”, followed by the outcome variable name and main explanatory variable name (and any

other control variables you wish to include), and then the various options:

streg outcome_varname explanatory_varname, options

The first option to specify is the distribution you wish to fit, using “dist(distname)” in

the options list. “distname” is one of the following: exponential, weibull, gompertz,

lognormal, loglogistic, or gamma. Abbreviations are allowed; the minimum being

underlined.

115

If you are using one of the distributions that has a proportional hazard ratio

parameterization (exponential, Weibull, or Gompertz), you can choose whether you want the

actual coefficients (βk) or the hazard ratios (expβk) displayed. Put “nohr” in the options list if

you are using one of those three distributions and wish for actual coefficients to be displayed

instead of hazard ratios.

If you are using one of the distributions that has an accelerated failure-time

parameterization (exponential and Weibull only), you can opt for results to be displayed as time

ratios instead of hazard ratios. The likelihood function is the same for both hazard and time

ratios; it’s just a matter of modifying your interpretation. Put “time” in the options to request this

display.

Stata allows you to specify how you want the standard errors to be reported in the

options. You may choose from several, such as robust or jackknife, by adding the command

“vce(vcetype)” in the options, where “vcetype” is the specific type of calculation.

If you do not wish to see the iteration log preceding the results, you can put “nolog” in

the options.

Choosing the best distribution for the data

After setting the data you wish to use, run separate survival analyses for each of the

available distributions, using the Stata commands described above, keeping all other variables

constant. In other words, the only part that is different in each of these survival analyses is the

distribution you are asking Stata to fit for your data.

Examine the Akaike Information Criterion (AIC) or log likelihood from each of the

outputs. Both are relative “goodness of fit” indicators and tools for model selection. The AIC

and log likelihood do not automatically appear without some additional code. After the “streg”

116 command line, you must type a second line to request the AIC value for the model you just ran.

The second line is “estat ic”.

Regardless of which number you choose to examine, a lower number indicates a better

fit. The analysis with the lowest AIC or log likelihood number is the best “fit” for your data, and

that particular distribution should be specified for any subsequent analyses. For our data, the

Weibull distribution fit best.

Interpreting our results from parametric survival analyses

Parametric survival analysis results obtained on the data for manuscript #1

corresponded well to the multinomial logistic regression results we ultimately used in the

manuscript. Granted, the outcome for the survival analysis was a combined outcome of re-

hospitalization, ED visit, or death within 30 days of hospital discharge, versus no outcome, and

the multinomial logistic regression had a three-category outcome of death or re-

hospitalization/ED visit, versus no outcome. However, the “story” was similar.

No statistically significant findings were found for the relationship between the main

explanatory variable (pending blood, urine, or sputum culture with or without preliminary results

available at discharge, versus no pending culture) and the outcome variable in either analysis.

The parametric survival analysis showed a slight nod towards pending cultures with preliminary

results available at discharge prolonging the time to “event” (re-hospitalization, ED visit, or death

within 30 days), while the multinomial logistic regression showed slightly lower odds of re-

hospitalization or ED visit within 30 days, compared to no outcome. An odds ratio <1.0 and a

time ratio >1.0 essentially tells the same story: lower odds of the outcome and prolonging the

time to event, or outcome, as compared to those experiencing no outcome.

Conclusion

117 Despite not ultimately using parametric survival analysis in the final papers, it was a

worthwhile and intriguing technique to learn about and apply. Its usefulness is apparent, and

surely there will be opportunities in the future to employ the technique.

118 Appendix Table. Parametric Survival Analyses of a Combined Outcome of Re-hospitalization, ED Visit, or Death, in Medicare Patients with Primary Discharge Diagnoses of Stroke, Hip Fracture or Cancer and Pending Blood, Urine, or Sputum Cultures Discharged to Sub-acute Care Facilities, 2003-2008, (N=768)

Unadjusted Time Ratio (CI) *Adjusted Time Ratio (CI)

No pending culture

Pending blood, urine, or sputum culture with

preliminary results available at discharge 1.02 (0.96 - 1.08) 1.02 (0.95 - 1.09)

Pending blood, urine, or sputum culture without

preliminary results available at discharge 1.00 (0.92 - 1.08) 0.99 (0.91 - 1.09)

Age

< 65 y 1.00 (Reference)

65-74 y 1.00 (0.92 - 1.10)

75-84 y 1.00 (0.92 - 1.09)

≥ 85 y 1.07 (0.96 - 1.19)

Female 0.96 (0.91 - 1.02)

Medicaid 1.11 (1.02 - 1.22)


Stroke 1.00 (Reference)

Hip fracture 0.96 (0.91 - 1.03)

Cancer 0.88 (0.79 - 0.99)

Cormorbid conditions

Alzheimers disease 1.00 (0.92 - 1.10)

Rheumatoid arthritis 0.95 (0.87 - 1.03)

Congestive heart failure 1.01 (0.95 - 1.07)

Dementia 0.98 (0.92 - 1.05)

Diabetes with chronic complications 0.92 (0.83 - 1.02)

Hypertension 1.03 (0.98 - 1.08)

Hypothyroidism 0.98 (0.93 - 1.04)

Psychoses 0.95 (0.87 - 1.05)

Renal failure 1.01 (0.94 - 1.09)

Valvular disease 0.97 (0.90 - 1.05)


Score 30 days prior to discharge date 0.88 (0.77 - 1.00)

Mechanical ventilation or Gastrostomy tube 0.98 (0.90 - 1.03)

Provider Specialty

Surgery 1.00 (Reference)

Internal Medicine 1.00 (0.94 - 1.06)

Neurology and Other Specialities 0.98 (0.91 - 1.05)


Re-hospitalization, ED visit Death within 30 days of

discharge (n=195)

1.00 (Reference)

119

Appendix References

1. Sethi AK, Gange SJ. Parametric models for studying time to antiretroviral resistance

associated with illicit drug use. WMJ. 2009;108:266-268. 2. Estabrook R. Survival Analysis. Charlottesville, VA: University of Virginia Department of

Psychology; 2009. 3. Gardiner JC. Survival Analysis: Overview of Parametric, Nonparametric and

Semiparametric approaches and New Developments. SAS Global Forum 2010; 2010. 4. Jenkins SP. Survival Analysis course - Estimation: continuous time models (parametric

and Cox). Essex, United Kingdom: University of Essex Institute for Social and Economic Research; 2006.

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