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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 2.1. 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) ................................................................................................................... 42
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 3.1. 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) ................................................................................................................... 60
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.
9
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
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 (12). 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
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.
Variable Definitions
Laboratory information system (LIS) data was obtained on each patient to allow for the
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.
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
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.
Most control variables were obtained from Medicare data. Patient sociodemographics
included age at index hospitalization, gender, and Medicaid enrollment status. Year of hospital
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
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 (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).
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
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
Pending culture with preliminary
results
Pending culture without
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)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(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)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Hierarchical condition category score
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
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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.
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 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)
diagnosis code in the first position on the acute hospitalization discharge diagnosis list was used
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
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.
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.
Institutional, physician, and supplier claims and demographic/enrollment data was
obtained from Medicare and linked to hospital administrative data, LIS data, and discharge
summaries by a combination of Medicare identification number, gender, age, race, and
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.
Variable Definitions
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.
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 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.
Most control variables were obtained from Medicare data. Patient sociodemographics
included age at index hospitalization, gender, and Medicaid enrollment status. Year of hospital
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, 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
Analyses were performed using SAS 9.2 and STATA 12 (14, 16). Basic frequencies
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.
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. 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.
42 Table 2.1. 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
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
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 7 3 4 0.017
Comorbid conditions
Alzheimer’s disease 11 10 15 13 0.390
Rheumatoid arthritis 6 6 6 4 0.864
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
43
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
Solid tumor without metastasis 9 9 7 9 0.904
Hierarchical condition category score
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
Internal Medicine 30 29 39 21
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)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(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
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)
Primary Discharge Diagnosis
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
Alzheimer’s disease -- 1.1 (0.6 - 2.0) -- 0.4 (0.2 - 1.3)
Rheumatoid arthritis -- 2.0 (1.0 - 4.3) -- 1.1 (0.3 - 3.3)
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)
Internal Medicine -- 1.3 (0.8 - 2.3) -- 1.1 (0.6 - 2.2)
Neurology & other specialties -- 1.5 (0.8 - 2.9) -- 1.4 (0.6 - 3.1)
*Adjusted by including all control variables in the model
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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.
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 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
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 (9). 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.
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.
Institutional, physician, and supplier claims and demographic/enrollment data was
obtained from Medicare and linked to hospital administrative data, LIS data, and discharge
summaries by a combination of Medicare identification number, gender, age, race, and
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.
Variable Definitions
Laboratory information system (LIS) data was obtained on each patient to allow for the
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
Most control variables were obtained from Medicare data. Patient sociodemographics
included age at index hospitalization, gender, and Medicaid enrollment status. Year of hospital
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
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, 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).
Discharging physician specialty was also included as a control variable. Physician
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
Analyses were performed using SAS 9.2 and STATA 12 (10, 13). Basic frequencies
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.
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 (6, 8), laboratories
may consider implementing testing methodologies with shorter turn-around times. Molecular
methods for bacterial identification are becoming more commonplace and economical, and can
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.
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. 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.
60 Table 3.1. 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
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
Patient demographic characteristics
Age
Average age, in years, at discharge (SD) 79 (10) 79 (10) 79 (10) 76 (11) 0.395
< 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
Patient medical history
Primary discharge diagnosis
Hip fracture 54 52 67 59
Stroke 40 42 30 33
Cancer 6 6 3 7 0.034
Comorbid conditions
Alzheimer’s disease 11 11 14 15 0.438
Rheumatoid arthritis 6 6 5 9 0.458
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
Hypertension 56 57 53 59 0.673
Hypothyroidism 20 20 23 15 0.486
Psychoses 8 8 5 13 0.162
Renal failure 10 11 8 9 0.676
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
Hierarchical condition category score
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
Internal Medicine 30 29 36 24
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)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Pending Culture Status
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)
Primary Discharge Diagnosis
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
Alzheimer’s disease -- 0.3 (0.1 - 1.0) -- 1.0 (0.4 - 2.4)
Rheumatoid arthritis -- 1.2 (0.3 - 4.6) -- 1.8 (0.9 - 3.7)
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)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(CI)
Unadjusted Odds Ratio
(CI)
*Adjusted Odds Ratio
(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)
Internal Medicine -- 1.4 (0.6 - 3.1) -- 1.3 (0.8 - 2.1)
Neurology and Other Specialties -- 1.1 (0.4 - 2.7) -- 1.7 (0.9 - 3.1)
*Adjusted by including all control variables in the model
64 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. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360:1418-1428.
3. 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.
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. 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.
7. 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.
8. 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.
9. 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.
10. SAS Statistical Software [Computer program]. Version 8.2. Cary, NC: SAS Institute; 2002.
11. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36:8-27.
12. 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.
13. Stata Statistical Software [Computer program]. Version 12. College Station, TX: StataCorp LP; 2011.
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.
15. 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.
16. 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.
17. Schumm WR. Statistical requirements for properly investigating a null hypothesis. Psychol Rep. 2010;107:953-971.
18. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141:533-536.
19. 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.
20. 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.
21. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Arch Intern Med. 2007;167:1305-1311.
22. 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.
23. 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.
24. 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.
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.
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 (4, 16), laboratories
may consider adopting testing methodologies with shorter turn-around times. Molecular
methods for bacterial identification are becoming more commonplace and economical, and can
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.
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. 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
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19. 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.
20. 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.
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.
22. 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.
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.
30. SAS Statistical Software [Computer program]. Version 8.2. Cary, NC: SAS Institute; 2002.
31. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36:8-27.
32. 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.
33. Stata Statistical Software [Computer program]. Version 8.0. College Station, TX: Stata Corporation; 1999.
34. Schumm WR. Statistical requirements for properly investigating a null hypothesis. Psychol Rep. 2010;107:953-971.
35. 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.
36. 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.
37. 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.
38. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141:533-536.
39. 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.
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
If NO, skip to item 408
407.02 Test Name __________________________ v407t1l1tn
80
407.02 Date Received in Lab (MM/DD/YYYY) v407t1l2rc
______/ ______/ __________
407.02 Date Result Reported (MM/DD/YYYY) v407t1l3rp
______/ ______/ __________
407.02 Result _______________________________ v407t1l4rs
407.02 Reporting Units _______________________ v407t1l5un
407.02 Result Flag (Circle one) v407t1l6fl
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)
407.02 Test Category (Circle one) v407t1l7tc
1……………..Hematology 8……………..Immunology
2……………..Coagulation 9……………..Molecular Diagnostic
3……………..Chemistry 10…………….Reference Lab (incl. WSLH)
4………………Urinalysis 11…………….Transfusion
81
5………………Endocrinology 12…………….Histocompatibility
6………………Flow Cytometry 13…………….Microbiology
7………………Toxicology 14…………….Miscellaneous
407.02 Another pending laboratory test to record? v407t1l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.03 Test Name __________________________ v407t2l1tn
407.03 Date Received in Lab (MM/DD/YYYY) v407t2l2rc
______/ ______/ __________
407.03 Date Result Reported (MM/DD/YYYY) v407t2l3rp
______/ ______/ __________
407.03 Result _______________________________ v407t2l4rs
407.03 Reporting Units _______________________ v407t2l5un
407.03 Result Flag (Circle one) v407t2l6fl
0…………….No flag
1…………….L (low numeric result)
2…………….LL (critically low result)
3…………….H (high numeric result)
4…………….HH (critically high result)
82
5…………….A (abnormal text result)
6…………….AA (critical text result)
407.03 Test Category (Circle one) v407t2l7tc
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
407.03 Another pending laboratory test to record? v407t2l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.04 Test Name __________________________ v407t3l1tn
407.04 Date Received in Lab (MM/DD/YYYY) v407t3l2rc
______/ ______/ __________
407.04 Date Result Reported (MM/DD/YYYY) v407t3l3rp
______/ ______/ __________
407.04 Result _______________________________ v407t3l4rs
83
407.04 Reporting Units _______________________ v407t3l5un
407.04 Result Flag (Circle one) v407t3l6fl
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)
407.04 Test Category (Circle one) v407t3l7tc
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
407.04 Another pending laboratory test to record? v407t3l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.05 Test Name __________________________ v407t4l1tn
84
407.05 Date Received in Lab (MM/DD/YYYY) v407t4l2rc
______/ ______/ __________
407.05 Date Result Reported (MM/DD/YYYY) v407t4l3rp
______/ ______/ __________
407.05 Result _______________________________ v407t4l4rs
407.05 Reporting Units _______________________ v407t4l5un
407.05 Result Flag (Circle one) v407t4l6fl
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)
407.05 Test Category (Circle one) v407t4l7tc
1……………..Hematology 8……………..Immunology
2……………..Coagulation 9……………..Molecular Diagnostic
3……………..Chemistry 10…………….Reference Lab (incl. WSLH)
4………………Urinalysis 11…………….Transfusion
85
5………………Endocrinology 12…………….Histocompatibility
6………………Flow Cytometry 13…………….Microbiology
7………………Toxicology 14…………….Miscellaneous
407.05 Another pending laboratory test to record? v407t4l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.06 Test Name __________________________ v407t5l1tn
407.06 Date Received in Lab (MM/DD/YYYY) v407t5l2rc
______/ ______/ __________
407.06 Date Result Reported (MM/DD/YYYY) v407t5l3rp
______/ ______/ __________
407.06 Result _______________________________ v407t5l4rs
407.06 Reporting Units _______________________ v407t5l5un
407.06 Result Flag (Circle one) v407t5l6fl
0…………….No flag
1…………….L (low numeric result)
2…………….LL (critically low result)
3…………….H (high numeric result)
4…………….HH (critically high result)
86
5…………….A (abnormal text result)
6…………….AA (critical text result)
407.06 Test Category (Circle one) v407t5l7tc
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
407.06 Another pending laboratory test to record? v407t5l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.07 Test Name __________________________ v407t6l1tn
407.07 Date Received in Lab (MM/DD/YYYY) v407t6l2rc
______/ ______/ __________
407.07 Date Result Reported (MM/DD/YYYY) v407t6l3rp
______/ ______/ __________
407.07 Result _______________________________ v407t6l4rs
87
407.07 Reporting Units _______________________ v407t6l5un
407.07 Result Flag (Circle one) v407t6l6fl
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)
407.07 Test Category (Circle one) v407t6l7tc
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
407.07 Another pending laboratory test to record? v407t6l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.08 Test Name __________________________ v407t7l1tn
88
407.08 Date Received in Lab (MM/DD/YYYY) v407t7l2rc
______/ ______/ __________
407.08 Date Result Reported (MM/DD/YYYY) v407t7l3rp
______/ ______/ __________
407.08 Result _______________________________ v407t7l4rs
407.08 Reporting Units _______________________ v407t7l5un
407.08 Result Flag (Circle one) v407t7l6fl
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)
407.08 Test Category (Circle one) v407t7l7tc
1……………..Hematology 8……………..Immunology
2……………..Coagulation 9……………..Molecular Diagnostic
3……………..Chemistry 10…………….Reference Lab (incl. WSLH)
4………………Urinalysis 11…………….Transfusion
89
5………………Endocrinology 12…………….Histocompatibility
6………………Flow Cytometry 13…………….Microbiology
7………………Toxicology 14…………….Miscellaneous
407.08 Another pending laboratory test to record? v407t7l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.09 Test Name __________________________ v407t8l1tn
407.09 Date Received in Lab (MM/DD/YYYY) v407t8l2rc
______/ ______/ __________
407.09 Date Result Reported (MM/DD/YYYY) v407t8l3rp
______/ ______/ __________
407.09 Result _______________________________ v407t8l4rs
407.09 Reporting Units _______________________ v407t8l5un
407.09 Result Flag (Circle one) v407t8l6fl
0…………….No flag
1…………….L (low numeric result)
2…………….LL (critically low result)
3…………….H (high numeric result)
4…………….HH (critically high result)
90
5…………….A (abnormal text result)
6…………….AA (critical text result)
407.09 Test Category (Circle one) v407t8l7tc
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
407.09 Another pending laboratory test to record? v407t8l8sk
1………………YES 0…………………NO
If NO, skip to item 408
407.10 Test Name __________________________ v407t9l1tn
407.10 Date Received in Lab (MM/DD/YYYY) v407t9l2rc
______/ ______/ __________
407.10 Date Result Reported (MM/DD/YYYY) v407t9l3rp
______/ ______/ __________
407.10 Result _______________________________ v407t9l4rs
91
407.10 Reporting Units _______________________ v407t9l5un
407.10 Result Flag (Circle one) v407t9l6fl
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)
407.10 Test Category (Circle one) v407t9l7tc
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
407.10 Another pending laboratory test to record? v407t9l8sk
1………………YES 0…………………NO
If NO, skip to item 408
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
If NO, skip to item 409
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’.
407.01 Date Received in Lab (MM/DD/YYYY) v407t0l2rc
______/ ______/ __________
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.
407.01 Date Result Reported (MM/DD/YYYY) v407t0l3rp
______/ ______/ __________
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.
407.01 Result Flag (Circle one) v407t0l6fl
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’.
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
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.
1……………..Hematology 8……………..Immunology
2……………..Coagulation 9……………..Molecular Diagnostic
3………………Chemistry 10…………….Reference Lab Testing (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
If NO, skip to item 408
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
106
107
108
109
110
111 Appendix E: Editorial Response to JGIM Paper
112
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)
Primary Discharge Diagnosis
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)
Hierarchical condition category score
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)
*Adjusted by including all control variables in the model
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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