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Inadequate Empiric Antibiotic Therapy among Canadian
Hospitalized Solid-Organ Transplant Patients:
Incidence and Impact on Hospital Mortality
by
Bassem Hamandi
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Graduate Department of Pharmaceutical Sciences
University of Toronto
© Copyright by Bassem Hamandi (2008)
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Inadequate Empiric Antibiotic Therapy among Canadian Hospitalized Solid-Organ Transplant Patients: Incidence and Impact on Hospital Mortality
Master of Science (2008)
Bassem Hamandi
Graduate Department of Pharmaceutical Sciences, University of Toronto
ABSTRACT Background: The incidence of inadequate empiric antibiotic therapy (IET) and its clinical
importance as a risk factor for hospital mortality in Canadian solid-organ transplant patients
remains unknown.
Methods: This retrospective cohort study evaluated all patients admitted to a transplant unit
from May/2002-April/2004. Therapy was considered adequate when the organism cultured was
found to be susceptible to an antibiotic administered within 24 hours of the index sample
collection time. Univariate and multivariate regression analyses were conducted to determine
associations between potential determinants, IET, and mortality.
Results: IET was administered in 169/312 (54%) transplant patients. Regression analysis
demonstrated that an increasing duration of IET (adjusted OR at 24h, 1.33; p < 0.001), ICU-
associated infections (adjusted OR, 6.27; p < 0.001), prior antibiotic use (adjusted OR, 3.56; p =
0.004), and increasing APACHE-II scores (adjusted OR, 1.26; p < 0.001), were independent
determinants of hospital mortality.
Conclusions: IET is common and appears to be an important determinant of hospital mortality in
the Canadian transplant population.
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ACKNOWLEDGEMENTS
I would like to thank my supervisor, Dr. Anne Holbrook for her support and guidance during my
graduate studies. Her constructive criticism and comments from the initial conception to the end
of this work were highly appreciated. Thank you to my committee members, Dr. James Brunton
and Dr. Manny Papadimitropoulos for their expert opinions, advice, and invaluable feedback at
our meetings. Dr. Michael Gardam’s expertise and feedback were much appreciated. I would
also like to thank Dr. Lehana Thabane for his statistical insight and advice. A special thanks to
Dr. Atul Humar for his unique expertise in transplant infectious diseases.
I am indebted to Mr. Gary Wong for his support, suggestions, and feedback during the early
planning and throughout the implementation of the study. I would also like to thank my
colleagues in the Pharmacy Department at The University Health Network for their help and
support.
Finally, I would like to thank my family and friends for their encouragement and support
throughout my studies.
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TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................II
ACKNOWLEDGEMENTS ..................................................................................................................................... III
LIST OF ABBREVIATIONS .................................................................................................................................. VI
LIST OF TABLES...................................................................................................................................................VII
LIST OF FIGURES............................................................................................................................................... VIII
LIST OF APPENDICES .......................................................................................................................................... IX
1.0 INTRODUCTION........................................................................................................................................1
1.1 STATEMENT OF THE PROBLEM.....................................................................................................................2 1.1.1 Infection after Solid-Organ Transplantation.............................................................................................2 1.1.2 Antibiotic Resistance .................................................................................................................................3 1.1.3 Inadequate Empiric Antibiotic Therapy ....................................................................................................5
1.2 PURPOSE......................................................................................................................................................7 1.2.1 Objective 1.................................................................................................................................................7 1.2.2 Objective 2.................................................................................................................................................7
1.3 STATEMENT OF RESEARCH HYPOTHESIS .....................................................................................................7 1.4 RATIONALE FOR HYPOTHESIS .....................................................................................................................8 1.5 REVIEW OF THE LITERATURE ......................................................................................................................8
2.0 METHODS .................................................................................................................................................25
2.1 STUDY LOCATION AND POPULATION.........................................................................................................25 2.1.1 Inclusion Criteria ....................................................................................................................................25 2.1.2 Exclusion Criteria ...................................................................................................................................25
2.2 STUDY DESIGN ..........................................................................................................................................25 2.3 MICROBIOLOGY.........................................................................................................................................26 2.4 DEFINITIONS..............................................................................................................................................27
2.4.1 Infection vs. Contamination vs. Colonization..........................................................................................27 2.4.2 Infectious Episodes..................................................................................................................................28 2.4.3 Healthcare vs. ICU vs. Community Associated Infections.......................................................................28 2.4.4 Primary Site of Infection..........................................................................................................................28 2.4.5 Empiric Antibiotic Therapy .....................................................................................................................29 2.4.6 Adequate vs. Inadequate Empiric Antibiotic Therapy .............................................................................29 2.4.7 Previous Antibiotic Therapy....................................................................................................................30 2.4.8 Previous Graft Rejection and Immunosuppressant Use ..........................................................................30 2.4.9 Multi-Drug Resistance.............................................................................................................................31
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2.5 OUTCOMES ................................................................................................................................................31 2.6 STATISTICAL ANALYSIS ............................................................................................................................31
3.0 RESULTS....................................................................................................................................................33
3.1 PATIENTS ..................................................................................................................................................33 3.2 INADEQUATE EMPIRIC ANTIBIOTIC THERAPY ...........................................................................................33 3.3 CHARACTERISTICS RELATED TO HOSPITAL MORTALITY...........................................................................40 3.4 LOGISTIC REGRESSION ANALYSIS .............................................................................................................43 3.5 SECONDARY OUTCOMES ...........................................................................................................................44
4.0 DISCUSSION .............................................................................................................................................45
4.1 STUDY LIMITATIONS .................................................................................................................................48 4.2 IMPLICATIONS OF INADEQUATE EMPIRIC THERAPY...................................................................................49
5.0 CONCLUSIONS.........................................................................................................................................51
6.0 REFERENCES ...........................................................................................................................................52
7.0 PUBLICATIONS AND ABSTRACTS TO DATE ..................................................................................58
8.0 APPENDICES ............................................................................................................................................59
APPENDIX I – LITERATURE REVIEW SEARCH STRATEGY .........................................................................................59 APPENDIX II – RAW DATA.......................................................................................................................................60 APPENDIX III – MULTIVARIATE LOGISTIC REGRESSION MODELLING ......................................................................66
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LIST OF ABBREVIATIONS
AET Adequate empiric antibiotic therapy
APACHE Acute Physiology and Chronic Health Evaluation
ATG Anti-thymocyte globulin
BAL Bronchoalveolar lavage
CDC Centers for Disease Control
CLSI Clinical and Laboratory Standards Institute
CMV Cytomegalovirus
CNS Coagulase-negative staphylococci
CVC Central venous catheter
HAI Healthcare-associated infections
ICU Intensive care unit
IET Inadequate empiric antibiotic therapy
IL-2 Interleukin-2
MDR Multi-drug resistant
MIC Minimum inhibitory concentration
MOT Multi-organ transplant
MRSA Methicillin-resistant Staphylococcus aureus
NNIS National Nosocomial Infections Surveillance
OR Odds ratio
RR Relative risk
SOT Solid-organ transplant
UTI Urinary tract infection
VAP Ventilator-associated pneumonia
VRE Vancomycin-resistant enterococci
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LIST OF TABLES
TABLE 1. A SUMMARY OF 22 NON-RANDOMIZED COMPARATIVE COHORT STUDIES ASSESSING THE ASSOCIATION
BETWEEN IET AND MORTALITY. .........................................................................................................................11
TABLE 2. CHARACTERISTICS OF PATIENTS RECEIVING ADEQUATE OR INADEQUATE EMPIRIC ANTIBIOTIC THERAPY .....34
TABLE 3. CHARACTERISTICS OF TRUE INFECTIOUS EPISODES TREATED WITH ADEQUATE OR INADEQUATE EMPIRIC
ANTIBIOTIC THERAPY ..........................................................................................................................................36
TABLE 4. ORGANISMS CULTURED FROM PATIENTS WITH INADEQUATELY TREATED INFECTIOUS EPISODES ..................38
TABLE 5. MULTI-DRUG RESISTANT ORGANISMS CULTURED AMONG PATIENTS RECEIVING ADEQUATE OR INADEQUATE
EMPIRIC ANTIBIOTIC THERAPY ............................................................................................................................39
TABLE 6. INTRA-ABDOMINAL ORGANISMS CULTURED AMONG 17 PATIENTS RECEIVING ADEQUATE AND 30 PATIENTS
RECEIVING INADEQUATE EMPIRIC ANTIBIOTIC THERAPY .....................................................................................39
TABLE 7. REASONS FOR ADMINISTRATION OF INADEQUATE EMPIRIC THERAPY ............................................................40
TABLE 8. PATIENT CHARACTERISTICS AMONG HOSPITAL SURVIVORS AND NONSURVIVORS .........................................41
TABLE 9. CULTURE CHARACTERISTICS AMONG HOSPITAL SURVIVORS AND NONSURVIVORS........................................42
TABLE 10. MOST COMMONLY CULTURED ORGANISMS AND THEIR ASSOCIATED MORTALITY AMONG PATIENTS
RECEIVING ADEQUATE OR INADEQUATE EMPIRIC ANTIBIOTIC THERAPY..............................................................42
TABLE 11. LOGISTIC REGRESSION ANALYSIS PREDICTING HOSPITAL MORTALITY.........................................................44
TABLE 12. OUTCOMES OF PATIENTS RECEIVING ADEQUATE VS. INADEQUATE EMPIRIC ANTIBIOTIC THERAPY..............44
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LIST OF FIGURES
FIGURE 1. INDIVIDUAL MULTIVARIABLE REGRESSION ANALYSIS OR (95% CI) OF 20 NON-RANDOMIZED COMPARATIVE
STUDIES ILLUSTRATING THE ASSOCIATION BETWEEN INADEQUATE ANTIBIOTIC THERAPY AND MORTALITY AT
END OF FOLLOW-UP. STUDIES CONDUCTED IN CRITICALLY ILL UNITS ARE SHOWN SEPARATELY NEAR THE
BOTTOM. .............................................................................................................................................................13
FIGURE 2. AN ILLUSTRATION DEPICTING THE TIMELINE FOR DETERMINING INADEQUATE EMPIRIC ANTIBIOTIC
THERAPY. THERAPY WAS CONSIDERED ADEQUATE WHEN, FOR A GIVEN INFECTIOUS EPISODE, THE ORGANISM
CULTURED WAS SUBSEQUENTLY FOUND TO BE SUSCEPTIBLE TO AN ANTIBIOTIC THAT WAS ADMINISTERED
WITHIN 24 HOURS OF THE SAMPLE COLLECTION TIME. ........................................................................................30
FIGURE 3. INCIDENCE AND RELATIVE RISK OF MORTALITY FOR INADEQUATE VERSUS ADEQUATE EMPIRIC ANTIBIOTIC
THERAPY AMONG HOSPITALIZED SOLID-ORGAN TRANSPLANT RECIPIENTS..........................................................34
FIGURE 4. DELAY IN ADMINISTRATION OF ADEQUATE EMPIRIC ANTIBIOTIC THERAPY AND ASSOCIATED MORTALITY
RATES. ................................................................................................................................................................43
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LIST OF APPENDICES
APPENDIX I – LITERATURE REVIEW SEARCH STRATEGY .........................................................................................59 APPENDIX II – RAW DATA.......................................................................................................................................60 APPENDIX III – MULTIVARIATE LOGISTIC REGRESSION MODELLING ......................................................................66
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1.0 INTRODUCTION
Healthcare–associated infections (HAIs) are infections that patients acquire in a healthcare
setting, during the course of receiving treatment for another condition (1). HAIs have imposed
significant burdens on our healthcare system, leading to increased morbidity, mortality, and
healthcare costs (2-4). As a result, the optimal management of HAIs has become an important
healthcare concern. HAIs typically affect patients who are immunocompromised, either because
of their age, underlying disease, or as a result of medical or surgical treatments (2). Along with
an aging population, the growing use of medical and surgical interventions, including invasive
devices and organ transplantation, have resulted in patients who are quite susceptible. The
pathogens involved and the body sites of infection are often related to the treatments and devices
used in intensive care units (ICUs). As a result, the highest infection rates are found among ICU
patients, who experience approximately three times higher rates than patients found elsewhere in
the hospital (2).
Upon clinical suspicion of infection, antibiotic therapy is often started early and empirically,
before pathogen identification, and before antibiotic susceptibilities are known. Deciding on the
use of an antibiotic requires a balance between the benefits of more potent broad-spectrum
antibiotics against their costs, including the potential for increased antibiotic resistance rates
caused by their overuse. Once the decision to initiate antibiotic therapy has been made, it should
ideally be directed at the most likely causative pathogens, taking into account local antibiotic
susceptibility patterns. As antibiotic resistance rates continue to increase, it appears that the
likelihood of administrating inadequate empiric antibiotic therapy (IET) also increases (5;6).
Most clinicians consider therapy to be inadequate when the antibiotic agent initiated
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demonstrates poor or no in vitro activity against the causative pathogen at the tissue site of
infection (7). Several studies concentrating on the consequences of IET have been conducted in
the ICU setting (5;6;8-11), however little information exists concerning outcomes of inadequate
empiric therapy among hospitalized solid-organ transplant (SOT) patients.
1.1 Statement of the Problem
1.1.1 Infection after Solid-Organ Transplantation
Infection in SOT patients is an important determinant of clinical outcomes (12), consequently,
the treatment of acquired bacterial infections with antibiotic therapy is recognized as being an
essential component in improving outcomes (13). Kidney, liver, heart, lung, pancreas, and small
bowel transplantation has become a therapeutic option for many end-stage organ diseases.
Advances in surgical techniques, medical management, and immunosuppressants have enhanced
both graft and patient survival rates and quality of life, however, infection continues to be a
major cause of morbidity and mortality among SOT recipients (14-19). The use of newer and
more potent immunosuppressants, particularly induction therapy with agents such as anti-
thymocyte globulin (ATG) and interleukin-2 receptor antagonists, increases the level of
immunosuppression and leads to increased susceptibility to infection in the early post-transplant
period (20). The incidence of bacterial infections in SOT recipients ranges from 21 to 68%
depending on the organ(s) transplanted, although the severity of the infection can vary among the
different SOT groups (20). In liver transplant patients, bacterial infections of the liver, peritoneal
cavity, biliary tree, bloodstream, and surgical wound are common (18). Lung and heart transplant
recipients are susceptible to pulmonary infections and bacteremias, of which 50% are of
pulmonary origin during the first post-transplant year (21). Infections among kidney transplant
patients include wound, bloodstream, and more commonly, urinary tract infections (UTIs)
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(15;17;19;22;23). Severe infections, such as bacteremia, continue to pose an increased risk of
death in transplant patients, with 14-day mortality rates ranging from 11% in kidney, 24% in
liver, 33% in heart recipients (14), and a 28-day mortality rate of 25% in lung recipients (21).
1.1.2 Antibiotic Resistance
The spread and rapid increase in antibiotic resistance rates has become a serious worldwide
healthcare concern (13). In 1946, Sir Alexander Fleming suggested that, “It seems likely that in
the next few years a combination of antibiotics with different antibacterial spectra will furnish a
cribrum therapeuticum from which fewer and fewer infecting bacteria will escape.” Despite the
advent of these potent antibiotics, the emergence and spread of antibiotic-resistant bacteria has
become a tremendous burden on our healthcare system. In 1970, the Centers for Disease Control
(CDC) established the National Nosocomial Infections Surveillance (NNIS) system, which
receives monthly reports of nosocomial infections from a non-random sample of hospitals in the
United States (24). With nearly 300 institutions currently reporting, data from the NNIS system
shows that the nosocomial infection rate remains relatively unchanged. However, the gradual
decline in the duration of inpatient stays has increased the rate of nosocomial infections per
1,000 patient days by 36%, from 7.2 in 1975 to 9.8 in 1995 (2). In 2002, nosocomial infections
accounted for nearly 1.7 million infections, resulting in excess of 98,000 deaths in the United
States (2;25). More than 70 percent of the bacteria that cause these infections are resistant to at
least one antibiotic that is commonly used to treat them (3). Drug-resistant infections can be
significantly more expensive to treat than non-resistant infections because they tend to result in a
longer duration of hospitalization, increased rates of readmission, higher drug costs, more post-
hospital care, lost work days, and increased mortality (3). Hospital-treated infections are
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estimated to cost $260-553 million each year in Canada, however, resistant infections may add
2.8 times more than what a drug-susceptible infection adds to the direct cost of care (26).
The excessive and inappropriate use of antibiotic agents continues to be one of the most
important factors affecting antibiotic resistance patterns (13;27). In more and more cases,
bacteria are becoming resistant to multiple drugs, leaving clinicians with few effective therapies,
if any. Bacteria demonstrating multiple drug resistance were found to be responsible for 48% of
bloodstream infections in a cohort of lung transplant recipients (21). Antibiotic resistance in
hospitals may be increasing as a result of several factors, including the proliferation and
prolongation of broad-spectrum antibiotic use, grouping of patients with higher disease acuity in
segregated wards or units, and decreased staffing leading to increased person-to-person
transmission (28). Several studies have shown an association between previous antibiotic use and
the development of resistance in both gram-negative and gram-positive bacteria, especially in
specialized settings such as ICUs (29-31;31-33). Conversely, colonization and infection with
antibiotic resistant bacteria, increases the likelihood of administering IET (5), leading one to
believe that a circular and confounding relationship may exist between antibiotic use, resistance
and IET. Moreover, for some patients who receive IET, altering their antibiotic therapy later in
the course of infection, based on subsequent culture susceptibility results, may yield little benefit
with respect to in-hospital mortality, suggesting that adequate early treatment is vital (8).
Overall, it seems that infections caused by antibiotic resistant bacteria are more difficult to treat
and are associated with higher mortality rates and hospital costs (13).
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1.1.3 Inadequate Empiric Antibiotic Therapy
Pharmacological treatment with antibiotics demonstrating bacteriostatic or bactericidal activity
against the causative pathogen remains the cornerstone of managing infectious diseases. The aim
of in vitro antimicrobial susceptibility testing is to predict the in vivo success or failure of a panel
of antibiotics at standard concentrations. The results of antimicrobial susceptibility testing
combined with clinical information and experience, allows clinicians to select the most
appropriate antibiotic. The safety and efficacy of antimicrobial agents in treating infections
caused by specific pathogens must be established in well-controlled clinical trials or studies. The
degree to which in vitro susceptibility results may or may not correlate with the in vivo efficacy
of antimicrobials has been previously studied (34). Apart from the minimum inhibitory
concentration (MIC) for a particular isolate, clinicians must consider patient, antimicrobial and
pathogen-specific factors in determining how to best treat an infection. Achieving levels at or
above the MIC by itself does not provide any information on persistent effects of antibacterial
agents, such as the post-antibiotic effect. For moderate or severe infections, clinicians commonly
initiate antibiotic therapy early and empirically, before the results of cultures and their respective
antibiotic susceptibilities are known. Empiric therapy for patients with suspected or confirmed
infections should be prescribed after considering patient symptoms, laboratory findings and the
patient’s past medical history, in the context of appropriate local and wider antibiotic resistance
trends. As a consequence, there is a possibility then that situations may arise where the chosen
antimicrobial agent demonstrates poor or no in vitro activity against the identified causative
pathogen. This condition can be considered to be one of inadequate empiric antibiotic therapy.
Thus, prescribing empiric therapy demands a balance between the benefits of using agents that
have a broader spectrum of in vitro susceptibilities that may correspond to the isolated
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pathogen’s profile, against the current financial costs, potential side-effects, and future costs of
developing resistance.
Several factors have been shown to be problematic in the selection of adequate antimicrobial
therapy. First, the complexity of the drug selection process may seem confusing, presenting a
difficult challenge to many clinicians. Selecting adequate therapy involves early recognition of
infection in a patient that may present with several confounding signs and symptoms,
identification of the causative pathogen, and prescribing of an antimicrobial regimen that is
efficacious, cost-effective and poses minimal toxicity. In addition, the types of pathogens, along
with antibiotic resistance patterns, have been shown to vary among different hospitals and even
within in-hospital units, suggesting the need to develop unit-specific reporting systems (35;36).
But until the susceptibility profile of the pathogen is known, antibiotic selection occurs through
an empiric process, based on local sensitivity patterns and the patient’s clinical presentation.
Infections caused by antibiotic resistant bacteria can lead to the problem of IET (37;38). To
further this dilemma, some organisms have become resistant to a point where few or no
treatment alternatives exist (39). The escalating concern with regard to antimicrobial resistance
in the hospital setting has led several investigators to examine how this and other factors have
influenced the prescribing of inadequate treatment. However, even after controlling for other
contributing risk factors, it may still be difficult to determine whether delayed or inadequate
therapy or antibiotic resistance has led to poor outcomes. To complicate issues further, humans
who are able to produce an innate immune response, may rid themselves of the infection and
thus seem to respond to inadequate therapy or even no treatment at all. Unfortunately, few
studies to date have addressed the issue of IET use in SOT recipients and its relationship to
clinical outcomes.
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1.2 Purpose
To date, little work has been done with respect to the incidence and clinical importance of IET as
a risk factor for hospital mortality in SOT recipients. The purpose of this study was two-fold.
Firstly, to determine the scale of the problem of IET among a cohort of Canadian hospitalized
SOT recipients. Secondly, this study aimed to examine the extent to which IET contributes to in-
hospital mortality among SOT patients. Determining the incidence and impact of IET in this
population may help in the decision-making process of prescribing empiric antibiotic therapy,
and perhaps justify the use of broad-spectrum empiric antibiotics in this population.
1.2.1 Objective 1
Our first objective was to determine the incidence of inadequate empiric antibiotic therapy
among Canadian hospitalized SOT patients.
1.2.2 Objective 2
Our second objective was to determine whether IET and the duration of IET are clinically
important risk factors for in-hospital mortality in SOT patients.
1.3 Statement of Research Hypothesis
Null Hypothesis: There is no statistically significant difference (p < 0.05) in hospital mortality
between SOT recipients receiving adequate versus inadequate empiric antibiotic.
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1.4 Rationale for Hypothesis
Although the view that early adequate therapy should improve survival seems plausible, few
studies exist to support this assumption outside the ICU setting. Many of these studies
specifically evaluated bacteremic ICU patients who had been prescribed IET, a possible
confounder given that bloodstream infections have been found to be an independent predictor for
mortality (5). The ICU setting includes a variety of pressures that are not as prevalent in other
settings, influencing the emergence and spread of antibiotic resistance. One of these pressures
includes patients with prolonged hospitalization who may harbour these organisms for the
duration of their stay. The presence of invasive devices, such as urinary catheters and
endotracheal tubes, along with prolonged mechanical ventilation may also promote infections
with resistant bacteria (24;40). Severity of illness may also be an important confounder among
ICU patients, and as such, use of broad-spectrum antibiotics early in the course of infection may
have a greater impact in this population. While SOT recipients are immunocompromised and
may share some of the qualities of ICU patients, in general, they are not as acutely ill, suggesting
that IET may not contribute to an excess risk for in-hospital mortality among this cohort.
1.5 Review of the Literature
We performed a literature search of the National Library of Medicine using the OVID
MEDLINE database to find original English-language articles published from 1950 to April
2007. The search strategy is outlined in Appendix I. The aim was to find publications that
included IET as a primary independent variable of interest, and mortality as a dependent
variable. We used terms related to antibiotic use or infection in addition to transplantation,
critical illness, and hospitalization to define our population of interest. We limited the search
9
with keywords for adequate or inadequate therapy and outcomes related to morbidity or
mortality. Additional articles referenced in publications found in the MEDLINE search were also
included. IET was considered to be a primary exposure of interest if it was explicitly stated as
such in the study objectives and it was forced into a multivariate statistical analysis. We only
included publications that accounted for the administration of empiric therapy, as defined by the
receipt of the final antibiotic sensitivity profile of the organism isolated from the index culture.
No studies that met the search criteria with SOT recipients as their primary population of interest
were found. We did find two studies that described the administration of ‘discordant initial’ and
‘inactive’ antibiotic therapy among SOT patients (21;41). In a prospective cohort of 56
bacteremic lung transplant recipients, discordant therapy (defined as therapy that was inactive in
vitro for the first two days following the index blood culture) occurred in 12/56 (21%) of the
patients (21). Six of the 12 patients receiving discordant died within 28-days, compared to a
mortality rate of 8/44 (18%) among patients receiving concordant therapy (21). Another
prospective examination of 66 SOT recipients who developed septic shock, revealed that empiric
therapy was inactive in vitro in 14/66 (21%) of the cases, with a mortality rate of 64% versus
52% for those receiving active empiric therapy (41). Neither study included inadequate empiric
therapy as a primary exposure of interest nor did they include the term in the final multivariable
analysis.
Table 1 summarizes 22 non-randomized comparative cohort studies that did assess the
association between IET and mortality (5;6;10;11;38;42-58). Two of these studies did not report
the 95% OR obtained from the multivariate analysis (53;58). The reported incidence of IET
among these studies ranges from 10-80%. Many studies assessing the impact of inadequate
10
empiric antibiotic usage have focused on the ICU population and have yielded more or less
similar conclusions about the importance of adequate therapy in bloodstream infections,
including sepsis and septic shock, and ventilator-associated pneumonia (VAP). The majority of
these studies have demonstrated that hospital mortality for critically ill patients receiving
inadequate antibiotic treatment is significantly greater than those receiving adequate therapy
(5;6;10;11;42;56). However, one study did not find adequate antibiotic treatment to be associated
with a significant mortality benefit in critically ill patients (58). We extracted the reported
multivariable regression analysis odds ratio along with the associated 95% confidence interval
from each individual study. Figure 1 depicts a list of the individual studies’ multivariable
regression analysis OR (95% CI), illustrating the association between inadequate antibiotic
therapy and mortality at end of follow-up. Compared to non-critically ill settings, studies
conducted in critically ill patients had a stronger trend towards favouring the use of adequate
therapy.
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Table 1. A summary of 22 non-randomized comparative cohort studies assessing the association between IET and mortality.
Study Design1 Patient
population Infection/ pathogens
Inadequate therapy
definition
Inadequate therapy
incidence
Mortality definition
Mortality (adequate vs.
inadequate therapy)
Multivariable regression analysis
OR (95% CI) Byl et al. (38), 1999
P 417 episodes 361 patients Tertiary care hospital
Bacteremia 24 h 159/428 (37%)
Attributable in-hospital mortality
33/258 (13%) vs. 24/159 (15%)
2.22 (1.09 - 4.55)
Clec’h et al. (42), 2004
P 196 episodes 142 patients Six ICUs
VAP 24 h 109/196 (56%)
In-hospital mortality
30/63 (48%) vs. 41/79 (52%)
7.24 (1.48-35.5)
Fraser et al. (43), 2006
P 895 patients 3 tertiary care hospitals
All bacterial infections
24 h 319/895 (36%)
30-day mortality
68/576 (12%) vs. 64/319 (20%)
1.58 (0.99-2.54)
Harbarth et al. (44), 2003
P 904 patients 108 hospitals
Severe sepsis or septic shock
24 h 211/904 (23%)
28-day mortality
168/693 (24%) vs. 82/211 (39%)
1.8 (1.2-2.6)
Hyle et al. (45), 2005
R 187 patients 2 Tertiary care hospitals
ESBL E. coli & Klebsiella species
48 h 112/187 (60%)
In-hospital mortality
8/75 (11%) vs. 24/112 (21%)
0.69 (0.19-2.53)
Ibrahim et al. (6), 2000
P 492 patients ICU
Bacteremia >72 h 147/492 (30%)
In-hospital mortality
98/345 (28%) vs. 91/147 (62%)
6.86 (5.09-9.24)
Iregui et al. (11), 2002
P 107 patients ICU
VAP 24 h 33/107 (31%) Attributable in-hospital mortality
8/74 (11%) vs. 13/33 (39%)
7.68 (4.50-13.09)
Kang et al. (46), 2005
R 286 patients Tertiary care hospital
Gram-negative bacteremia
24 h 151/286 (53%)
30-day mortality
37/135 (27%) vs. 58/151 (38%)
3.64 (1.13-11.72)
Kim et al. (47), 2006
R 238 patients Tertiary care hospital
S. aureus bacteremia
48 h 117/238 (49%)
12-week attributable mortality
34/121 (28%) vs. 45/117 (39%)
1.39 (0.62-3.15)
Kollef et al. (5), 1999
P 655 patients ICU
All bacterial infections
>72 h 169/655 (26%)
Attributable in-hospital mortality
59/486 (12%) vs. 88/169 (52%)
4.26 (3.35-5.44)
Leibovici et al. (10), 1998
P 3413 patients Tertiary care hospital
Bacteremia 48 h 1255/3440 (36%)
In-hospital mortality
436/2158 (20%) vs. 432/1255 (34%)
1.6 (1.3-1.9)
Lodise et al. (48), 2003
R 167 patients Level 1 trauma centre
S. aureus bacteremia
48 h 48/167 (29%) Attributable mortality
23/119 (19%) vs. 16/48 (33%)
3.8 (1.3-11.0)
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Study Design1 Patient
population Infection/ pathogens
Inadequate therapy
definition
Inadequate therapy
incidence
Mortality definition
Mortality (adequate vs.
inadequate therapy)
Multivariable regression analysis
OR (95% CI) Lujan et al. (49), 2004
P 100 patients Tertiary care hospital
S. pneumoniae bacteremia
24 h 10/100 (10%) 28-day mortality
13/90 (14%) vs. 5/10 (50%)
5.72 (0.72-45.26)
Micek et al. (50), 2005
R 305 patients Tertiary care hospital
P. aeruginosa bacteremia
>72 h 75/305 (25%) In-hospital mortality
41/230 (18%) vs. 23/75 (31%)
2.04 (1.42-2.92)
Osih et al. (51), 2007
R 167 episodes 159 patients Tertiary care hospital
P. aeruginosa bacteremia
24 h 68/167 (41%) In-hospital mortality
35/99 (35%) vs. 26/68 (38%)
0.93 (0.45-1.92)
Paterson et al. (52), 2003
P 85 patients 12 hospitals
ESBL K. pneumoniae
>72 h 11/82 (13%) 14-day mortality
10/71 (14%) vs. 7/11 (64%)
11.11 (1.54-100)
Roghmann (53), 2000
R 132 episodes 125 patients Tertiary care hospital
S. aureus bacteremia
48 h 105/132 (80%)
30-day mortality
28/102 (27%) vs. 5/23 (22%)
Not reported
Scarsi et al. (54), 2006
R 884 patients Tertiary care hospital
Gram-negative bacteremia
24 h 125/884 (14%)
In-hospital mortality
122/759 (16%) vs. 17/125 (14%)
0.61 (0.31-1.18)
Schramm et al. (55), 2006
R 549 patients Tertiary care hospital
MRSA sterile-site infections
24 h 380/549 (69%)
In-hospital mortality
28/169 (17%) vs. 99/380 (26%)
1.92 (1.48-2.50)
Valles et al. (56), 2003
P 339 patients 30 ICUs
Community-acquired bacteremia
24 h 49/339 (14%) In-hospital mortality
107/290 (37%) vs. 34/49 (69%)
4.11 (2.03-8.32)
Vidal et al. (57), 1996
P 189 episodes 182 patients Tertiary care hospital
P. aeruginosa bacteremia
>72 h 19/189 (10%) All-cause mortality
24/170 (14%) vs. 10/19 (53%)
6.53 (1.10 - 48.80)
Zaragoza et al. (58), 2003
P 166 patients ICU
Bacteremia >72 h 39/166 (23%) Attributable mortality
29/127 (23%) vs. 12/39 (31%)
Not reported
1 P = Prospective; R = Retrospective
13
Figure 1. Individual multivariable regression analysis OR (95% CI) of 20 non-randomized comparative studies illustrating the association between inadequate antibiotic therapy and mortality at end of follow-up. Studies conducted in critically ill units are shown separately near the bottom.
14
One of the earliest studies assessing the relationship between inadequate antibiotic treatment and
hospital mortality, evaluated a prospective cohort of 2,000 patients admitted over an 8-month
period to the medical or surgical ICU of a large urban teaching hospital in St. Louis, Missouri.
Inadequate antimicrobial treatment was defined as the microbiological documentation of a
pathogen causing infection, which was not effectively treated at the time of its identification.
This included both the absence of antimicrobial agents and the administration of an agent to
which the pathogen was resistant. Comparisons were made between patients receiving
inadequate and adequate therapy and hospital survivors to non-survivors. Multiple logistic
regression analysis was used to evaluate the relationship between the dependent variable of
hospital mortality and the independent variable of inadequate treatment and to identify
independent risk factors for the administration of inadequate treatment. Of the 655 patients with
a nosocomial or community-acquired infection, 169 (25.8%) were found to have received
inadequate antibiotic treatment. The infection-related mortality rate for infected patients
receiving inadequate therapy (42.0%) was significantly greater than patients receiving adequate
antibiotic treatment (17.7%) (RR, 2.37; 95% CI, 1.83 to 3.08; p < 0.001). In addition, a logistic
regression model demonstrated that inadequate antibiotic treatment was the most important
independent determinant of hospital mortality (adjusted OR, 4.27; 95% CI, 3.35 to 5.44; p <
0.001). The incidence of inadequate antimicrobial treatment was most common among patients
with nosocomial infections, which developed after treatment of a community-acquired infection
(45.2%), followed by patients with nosocomial infections alone (34.3%) and patients with
community-acquired infections alone (17.1%) (p < 0.001). Among patients with nosocomial
infections, inadequate therapy occurred most commonly as a result of Gram-negative bacteria
that were resistant to third-generation cephalosporins. Inadequate treatment for methicillin-
resistant Staphylococcus aureus (MRSA), Candida species and vancomycin-resistant enterococci
15
(VRE) were also commonly found among nosocomial infections. Multiple logistic regression
analysis revealed that the prior administration of antibiotics (adjusted OR, 3.39; 95% CI, 2.88 to
4.23; p < 0.001), presence of a bloodstream infection (adjusted OR, 1.88; 95% CI, 1.52 to 2.32; p
= 0.003), increasing Acute Physiology and Chronic Health Evaluation (APACHE) II scores in 1-
point increments (adjusted OR, 1.04; 95% CI, 1.03 to 1.05; p = 0.002), and decreasing patient
age (adjusted OR, 1.01; 95% CI, 1.01 to 1.02; p = 0.012) were independently associated with the
administration of inadequate antibiotic treatment. This study established an association between
the prescribing of inadequate therapy and hospital mortality, and demonstrated that previous
antibiotic use may be an important risk factor for inadequate therapy among ICU patients.
Continuing the work of Kollef et al., Ibrahim et al. prospectively evaluated the relationship
between the adequacy of antimicrobial treatment for bloodstream infections and the primary
outcome of hospital mortality among a cohort of patients at the same university-affiliated urban
teaching hospital. All patients admitted to the medical or surgical ICU were eligible for
enrolment. Inadequate antimicrobial treatment was defined as the microbiological documentation
of a pathogen causing infection, both bacterial and fungal, which was not effectively treated at
the time the pathogen and its susceptibility profile were known. The primary analysis compared
hospital survivors to non-survivors. Multiple logistic regression analysis was used to evaluate the
relationship between the dependent variable of hospital mortality and the independent variable of
inadequate antimicrobial treatment and to identify independent risk factors for the administration
of inadequate treatment. Over a two-year period, 4913 critically ill patients were admitted, of
whom 492 (10.0%) were found to have a bloodstream infection. Of the 492 patients, 147
(29.9%) received inadequate treatment. Furthermore, the hospital mortality for these patients was
significantly greater than those receiving adequate therapy (61.9% vs. 28.4%; RR, 2.18; 95% CI,
16
1.77 to 2.69; p < 0.001). Multiple logistic regression analysis identified the administration of
inadequate antibiotic treatment as an independent risk factor hospital mortality (adjusted OR,
6.86; 95% CI, 5.09 to 9.24; p < 0.001). The most commonly identified bloodstream pathogens
along with their rates of inadequate antimicrobial treatment included: VRE (n = 17; 100%),
Candida species (n=41; 95.1%), MRSA (n = 46; 32.6%), coagulase-negative staphylococci
(CNS) (n = 96; 21.9%), and Pseudomonas aeruginosa (n = 22; 10.0%). A statistically significant
correlation was found between the rates of inadequate antimicrobial treatment for individual
micro-organisms and their associated rates of hospital mortality (Spearman’s correlation
coefficient 0.8287; p=0.006). However, some organisms, such as Escherichia coli and Klebsiella
species, were found to be associated with relatively low rates of inadequate antimicrobial
therapy, though their associated hospital mortality rates were greater than 30%. Multiple logistic
regression analysis also demonstrated that the following criteria were independently associated
with the administration of inadequate antimicrobial treatment: Bloodstream infection attributable
to Candida species (adjusted OR, 51.86; 95% CI, 24.57 to 109.49; p < 0.001); Prior
administration of antibiotics during the current hospital stay (adjusted OR, 2.08; 95% CI, 1.58 to
2.74; p = 0.008); Decreasing serum albumin concentrations (adjusted OR, 1.37; 95% CI, 1.21 to
1.56; p = 0.014); Increasing central catheter duration (adjusted OR, 1.03; 95% CI, 1.02 to 1.04; p
= 0.008). The study demonstrated that ICU patients with bloodstream infections receiving
inadequate antimicrobial treatment were at an increased risk of death compared to patients
receiving adequate treatment. The authors recommended initial empiric therapy with
vancomycin for MRSA and CNS, along with combination therapy for the treatment of P.
aeruginosa.
17
Most studies with bacteremia seem to support the importance of adequate empiric therapy,
however, there are a few studies that do not come to this conclusion (51;54;58). These studies
may include certain groups of organisms that may be less virulent and thus it may be more
problematic in determining their role in morbidity or mortality. One such cohort study in Spain
was conducted among 166 prospectively followed patients with bacteremia, 39 (23.5%) of which
received inadequate antibiotic treatment, while 127 (76.5%) received adequate treatment (58).
Bacteremia was determined to be nosocomial in nature in 92.3% of the inadequately treated
cohort, and 79.5% of adequately treated group. The occurrence of coagulase-negative
staphylococci isolates (OR, 2.62; 95% CI, 1.10 to 6.21; p = 0.015), and absence of a respiratory
or abdominal source of infection (OR, 0.35; 95% CI, 0.12 to 0.97; p = 0.04) was greater in the
cohort with inadequate treatment than in the group with adequate treatment. Neither crude
mortality rates (56.4% vs. 50.3%; p = 0.512) nor bacteremia-related mortality rates (30.8% vs.
22.8%; p = 0.315) were significantly different among the inadequately and adequately treated
groups, respectively. Multivariate analysis did not reveal inadequate treatment to be an
independent predictor of mortality. The authors suggested that this was likely a result of
microbiological factors and clinical features, such as the types of micro-organisms isolated and
the sources of the bacteremia. Cultures from patients in the inadequately treated arm were more
likely to have grown less virulent isolates and to have been sampled from sources of infections
with typically better prognoses. As a result, this tended to dilute the effects on mortality, and
produce a statistically non-significant difference.
In an application to non-critically ill patients, Leibovici et al. set out to determine whether
empiric antibiotic treatment matching the in vitro susceptibility of the pathogen, which they
termed as being appropriate treatment, improved survival in hospitalized patients with
18
bloodstream infections. This prospective and observational cohort study examined patients with
bloodstream infections identified between 1988 and 1994 in an urban hospital in Israel.
Empirical antibiotic treatment was defined as appropriate if it was started within two days of the
first positive blood culture, and the infecting micro-organism was subsequently found to be
susceptible to an intravenously administered drug. However, the authors decided that treatment
of a pseudomonal infection with just an aminoglycoside would be considered inappropriate. This
study analyzed the benefit presented by appropriate empiric treatment in stratified subgroups of
patients defined by a set of other mortality risk factors. Logistic regression analysis was used to
measure the independent contribution of inappropriate treatment to hospital mortality. Of the
3415 patients identified with bloodstream infections, 2158 (63.2%) were given appropriate
empiric antibiotic treatment, of which 436 (20.2%) died, compared with the death of 432
(34.4%) of 1255 patients who were given inappropriate treatment (OR, 2.1; 95% CI, 1.8 to 2.4; p
= 0.0001). The median duration of hospital stay for survivors was 9 days when given appropriate
treatment and 11 days when given inappropriate treatment (p = 0.0001). The greatest relative
reduction in the mortality rate associated with appropriate treatment versus inappropriate
treatment in patients was seen most commonly in: Pediatric patients (4% vs. 17%; OR, 5.1; 95%
CI, 2.4 to 10.7); Intra-abdominal infections (12% vs. 34%; OR, 3.8; 95% CI, 2.0 to 7.1); Skin
and soft tissue infections (23% vs. 49%; OR, 3.1; 95% CI, 1.8 to 5.6); Infections caused by
Klebsiella pneumoniae (17% vs. 39%; OR, 3.0; 95% CI, 1.7 to 5.1), and Streptococcus
pneumoniae (22% vs. 42%; OR, 2.6; 95% CI, 1.1 to 5.9). Multivariable logistic regression
analysis revealed that inappropriate empiric treatment was associated with a significant risk for
in-hospital mortality (adjusted OR, 1.6; 95% CI, 1.3 to 1.9) independent of other risk factors.
The investigators concluded that in this relatively large cohort of hospitalized patients with
19
bloodstream infections, inappropriate empiric treatment was associated with an increased risk of
death, regardless of concomitant risk factors for mortality.
Infections resulting from certain groups of organisms, such as antibiotic-resistant gram-negative
bacilli, have become more of a concern in recent years, as patients infected by these relatively
virulent isolates may be at a higher risk of receiving inadequate therapy (45;46;52;55). To
evaluate the effect of inappropriate initial antimicrobial therapy on mortality, Kang et al.
retrospectively reviewed 286 hospitalized patients in Seoul, South Korea. They identified and
included patients with nosocomial antibiotic-resistant gram-negative bacteremia: 61 patients with
E. coli, 65 with K. pneumoniae, 74 with P. aeruginosa, and 86 with Enterobacter species. Initial
antibiotic therapy was considered to have been appropriate if a patient received at least one agent
within 24 hours of blood culture collection to which the causative pathogens were susceptible.
Only the first bacteremic episode per patient was included. Antibiotic resistance was defined as
in vitro resistance to either cefotaxime or ceftazidime, except for P. aeruginosa, which was
required to be resistant to either piperacillin, ciprofloxacin, ceftazidime, or imipenem. High-risk
sources of bacteremia were defined as the lung, peritoneum, or an unknown source. The main
outcome measure was 30-day mortality. Of the 286 patients, 135 (47.2%) received appropriate
initial empirical antimicrobial therapy, with the remaining 151 (52.8%) patients receiving
inappropriate therapy. The inadequately treated group had a significantly greater mortality rate
compared to the adequately treated cohort (38.4% vs. 27.4%; p=0.049). Multivariate analysis
demonstrated that septic shock, a high-risk source of bacteremia, P. aeruginosa infection, and an
increasing APACHE II score, were independent risk factors for mortality. In a subgroup analysis
of patients with a high-risk source of bacteremia (n = 132), inappropriate initial antimicrobial
therapy was independently associated with decreased survival (adjusted OR, 3.64; 95% CI, 1.13
20
to 11.72; p = 0.030). The results of this study suggest that inappropriate initial antimicrobial
therapy is associated with adverse outcomes in patients diagnosed with antibiotic-resistant gram-
negative bacteremia, specifically in those that are severely ill, have a high-risk source of
bacteremia, or have isolates that are especially virulent.
Some evidence suggests that when adequate empiric antibiotic therapy (AET) is initiated early in
the course of the infection and prior to the availability of susceptibility results, the associated
mortality rates are significantly lower (8;11;48;55;59). Iregui et al. fixed their efforts on the
clinical importance of delayed initial appropriate antibiotic treatment in critically ill patients with
clinically diagnosed VAP. Their goals were to identify the occurrence of initially delayed
appropriate antibiotic treatment for VAP, and to determine its effects on patient outcomes. They
prospectively observed a cohort of patients requiring mechanical ventilation and admitted to the
medical ICU of a large urban teaching hospital in St. Louis, Missouri. The primary outcome
compared hospital mortality among those receiving initially delayed appropriate antibiotic
treatment to all other patients in the cohort. Adequate initial treatment was defined as an
antibiotic with in vitro susceptibility to the pathogen isolated in respiratory samples. Delayed
therapy was defined as a time period of >24 hours between the time of VAP diagnosis, until the
time that appropriate antibiotic therapy was administered. Thirty-three of 107 (30.8%) patients
received appropriate antibiotic therapy that was delayed >24 hours after the clinical diagnosis of
VAP. The most common cause of inadequate initial treatment was a delay in writing the medical
orders (n = 25; 75.8%). The presence of a resistant micro-organism (n = 6) accounted for 18.2%
of cases. Among these patients, the mean time delay between VAP diagnosis and the
administration of an appropriate antibiotic was 28.6 ± 5.8 hours, compared to 12.5 ± 4.2 hours
for all other patients (p < 0.001). Patients with initially delayed treatment had a significantly
21
greater hospital mortality compared to the other patients in the cohort (69.7% vs. 28.4%; p <
0.01). It is important to note that diagnostic delays were not counted, and that the authors utilized
a clinical VAP diagnosis as opposed to using bronchoscopically obtained cultures. It was
suggested that clinicians avoid delaying the administration of appropriate antibiotic therapy to
patients with VAP to minimize their mortality risk.
The extent to which the timely use of adequate therapy impacts clinical outcomes in hospitalized
patients may depend on the causative pathogens and populations studied, but may also depend on
the actual time delay itself. In a study of episodes of nosocomial S. aureus bacteremia, Lodise et
al. attempted to determine the effect of delayed therapy on morbidity and mortality in a
retrospective cohort of 167 hospitalized patients at a trauma centre in Detroit, Michigan. If a
patient had more than one episode of S. aureus bacteremia during a hospitalization, only the first
episode was included. Classification and regression tree analysis was utilized to select the time
interval (from the time the culture result was obtained until administration of adequate therapy)
that classified patients as having either a low-risk or high-risk of infection-related mortality. The
time breakpoint between delayed and early treatment was determined to be 44.75 hours.
Accordingly, 48 (28.7%) patients did not receive appropriate treatment prior to the breakpoint
time and were deemed to have received delayed treatment. The remaining 119 (71.3%) patients
did receive appropriate treatment within 44.75 hours, and were classified into the early treatment
group. A comparison of the infection-related mortality rates between the two groups revealed a
1.7-fold increase among patients receiving delayed therapy versus early treatment (33.3% vs.
19.3%; p = 0.05). A multivariate analysis revealed that delayed treatment was an independent
predictor of infection-related mortality (adjusted OR, 3.8; 95% CI, 1.3 to 11.0; p = 0.01) and was
associated with a longer hospital stay than early treatment (20.2 vs. 14.3 days; p = 0.05). For
22
patients with an APACHE II score >15.5 and a ‘high-risk’ source of infection (non-IV catheter
related), mortality was 86.7% in the delayed treatment group compared with 44.7% in the early
treatment group (p = 0.006). However, among patients with an APACHE II score <15.5, the
mortality rate was not significantly different, indicating that delayed treatment had more of an
adverse effect on those that were more severely ill. The results of this study point towards a
delay in adequate therapy in the realm of 24-48 hours as being an important factor in clinical
outcomes of patients hospitalized with S. aureus nosocomial bacteremias, particularly those who
are severely ill or have a ‘high-risk’ source of infection.
To date, studies determining the impact of initially delayed adequate therapy on the clinical
outcomes of infections, have utilized observational cohorts in their design. Randomized
controlled trials would provide a methodological advantage in reducing bias and the effect of
confounders, however, this design is neither practical nor ethical. The propensity score is an
analysis that utilizes the probability of exposure to a specific treatment conditional on observed
variables and is increasingly being used in observational studies. This analysis attempts to
compensate for selection bias by creating strata in which subjects are matched on the propensity
score, balancing the covariables between patients receiving adequate or inadequate therapy. Kim
et al. studied 238 hospitalized patients with S. aureus bacteremia who received either
inappropriate or appropriate empirical therapy, and compared them by using two risk
stratification models. The first model used a cohort study with a propensity score to adjust for
confounding by treatment allocation, and the second, used a propensity-matched case-control
study. Inappropriate therapy was modeled on the basis of patient characteristics, and included in
the multivariate model to adjust for confounding. For the case-matching analysis, patients with
inadequate empiric treatment (cases) were matched to those with adequate empiric treatment
23
(controls) on the basis of the propensity score. The cohort study revealed that the bacteremia-
related mortality rate was 38.4% (45/117) among those inappropriately treated versus 28.1%
(34/121) for those appropriately treated (adjusted OR 1.60; 95% CI, 0.93-2.76; p = 0.09).
Conducting a multivariate analysis to adjust for independent predictors for mortality and the
propensity score, demonstrated that inappropriate empiric therapy was not associated with
mortality (adjusted OR, 1.39; 95% CI, 0.62-3.15). The matched case-control study analyzed 50
pairs, with mortality rates of 32% (16/50) for the case group and 28% (14/50) for the control
group (OR, 1.15; 95% CI, 0.51-2.64; p = 0.85). Once more, these authors indicated that this may
have been a result of microbiological factors, specifically, the fact that most gram-positive
pathogens tend not to be as virulent as gram-negative ones. In addition, they point to the
methodology of the study and the propensity score, including the possibility of it being
underpowered and not being able to control for detection bias.
In summary, these results suggest an association between the time to administration of IET and
mortality, promoting the principle of utilizing broad-spectrum antibiotics early in the course of
infection. However, the patients were not homogeneous, with considerable variation with respect
to their sites of infection, organism virulence, and severity of illness. With a few exceptions, the
evidence favours the early use of adequate empiric therapy in relatively ill patients who are at
risk of an infection with a virulent organism. The Infectious Diseases Society of America and the
Society for Healthcare Epidemiology of America has developed guidelines and a set of
recommendations for enhancing antimicrobial stewardship. With respect to combination therapy,
they state that there are insufficient data to recommend the routine use of combination therapy to
prevent the emergence of resistance (Grade C-II) (60). However, combination therapy does have
a role in certain clinical contexts, including increasing the breadth of empiric coverage and the
24
likelihood of adequate initial therapy in critically ill patients at risk of infection with multidrug-
resistant pathogens (Grade A-II) (60). It is also important to note that they also stress a more
targeted approach to empirical antimicrobial therapy on the basis of culture results and
elimination of redundant combination therapy. This strategy can more effectively target the
causative pathogen, resulting in decreased antimicrobial exposure and substantial cost savings
(Grade A-II) (60). It remains to be seen whether SOT patients are at increased risk of mortality
secondary to inadequate empiric therapy.
25
2.0 METHODS
2.1 Study Location and Population
2.1.1 Inclusion Criteria
We included solid-organ transplant recipients admitted to the Multi-Organ Transplant (MOT)
unit for the 2-year period of May 2002 to April 2004 at The Toronto General Hospital,
University Health Network, Toronto, Canada. Our institution accounted for approximately 40%
of all new transplant recipients in the province of Ontario, and 15% of all new transplant patients
in Canada (61). All transplant patient groups were eligible, including kidney, kidney/pancreas,
liver, lung, heart, heart/lung, and small bowel transplant recipients. Patients must have had a
microbiologically documented infection (i.e. positive culture) in the context of systemic signs of
infection and defined according to criteria established by the CDC (62). We identified eligible
patients through the records of the clinical microbiology laboratory (Department of
Microbiology at Mount Sinai Hospital and Toronto Medical Laboratories, Toronto, Canada),
which processes and performs cultures and sensitivities of all clinical specimens obtained at our
institution.
2.1.2 Exclusion Criteria
Non-transplant patients, including living donors, transferred to the unit were excluded since they
may not be representative of our population of interest.
2.2 Study Design
A retrospective cohort study design was used to characterize the sampled patients. Patient data
were analyzed to determine if an association existed between the presence or absence of
26
inadequate antibiotic therapy and the main outcome of all-cause in-hospital mortality. Data
variables collected included patient demographics, severity of illness during the first 24 hours of
admission (as measured by the Acute Physiology And Chronic Health Evaluation score,
APACHE II), central venous catheter use, presence of acute renal failure, chronic dialysis
requirement, length of ventilation, transplant data, concomitant immunosuppression,
neutropenia, diabetes mellitus requiring insulin, infection-related data, previous use of
antibiotics, mortality, length of hospitalization, previous admissions to the ICU, length of ICU
stay, empiric antibiotic use and adequacy of treatment. Data sources included electronic medical
records, microbiology records, the Outpatient Transplant Tracking Record (OTTR) clinic
database, and transplant pharmacist patient profiles. This study was submitted to the institution’s
Research Ethics Board and received expedited review approval (REB number 04-0306-AE).
2.3 Microbiology
The clinical microbiology laboratory used the VITEK-1 system (Bio Mérieux, Charbonnières les
Bains, France), an automated, short-incubation broth micro-dilution system capable of
performing susceptibility testing of most rapidly growing gram-positive and gram-negative
aerobic bacteria. It yields results in a period of 4 to 10 hours. Results were automatically
transferred from the VITEK-1 system to the Laboratory Information System via a computer
interface. Susceptibility testing was performed and interpreted according to guidelines and
breakpoints established by the Clinical and Laboratory Standards Institute (CLSI) (63). Results
were reported qualitatively as either Susceptible (S), Intermediate (I), or Resistant (R). Quality
control procedures were performed on all new lots of identification and susceptibility cards, once
when received and once weekly when the lot was in use. Alternatively, the Kirby-Bauer disk
diffusion method was also available for use. Daily quality controls were performed when any
27
out-of-control results were observed. Subjects were included if documentation existed for both
the antibiotic therapy administered and the patient’s in-hospital survival outcome. Patient culture
data could be included more than once if they had multiple positive cultures during the study
period. For a given patient, the first infection episode was included, and subsequent infections
were included only if a different organism was cultured.
2.4 Definitions
2.4.1 Infection vs. Contamination vs. Colonization
Clinical pharmacists followed the CDC definitions of nosocomial infections to critically assess
each positive culture (62). To avoid reviewer bias, the patient being evaluated must not have
been under the direct clinical care of the pharmacist. Based on clinical documentation from the
patients’ medical records, all isolates were categorized as being a true infection, contaminant, or
of unknown clinical significance. If the medical records explicitly stated that the isolate was a
contaminant, then it was deemed as such. For laboratory-confirmed bloodstream infections with
common skin contaminants (eg. Coagulase-negative staphylococci), patients must have had at
least one of the following signs or symptoms: fever (>38°C), chills, or hypotension; and a
positive culture from two or more blood cultures drawn on separate occasions, or from at least
one blood culture from a patient with an intravascular line, with the physician instituting
appropriate antimicrobial therapy (62). Otherwise, infection was defined as the entry and
multiplication of micro-organisms in the tissues of the host leading to local or systemic signs and
symptoms of infection. We defined colonization as the presence of micro-organisms in or on a
host with growth and multiplication, but without tissue invasion or damage. Only true infections
were included among the infectious episodes.
28
2.4.2 Infectious Episodes
An infectious episode was defined by the first true positive bacterial (index) culture collected per
isolate per patient, or when identical isolates are cultured within 72 hours of each other, the
culture collected from the primary body site of infection. The CLSI recommendation to exclude
duplicates and include only the first isolate of a given species per patient, irrespective of body
site or antibiotic pattern, was implemented (64).
2.4.3 Healthcare vs. ICU vs. Community Associated Infections
Each infectious episode was classified as being healthcare (nosocomial), ICU, or community-
associated, according to criteria established by the CDC (62). The NNIS system defines an HAI
as a localized or systemic condition that results from an adverse reaction to the presence of an
infectious agent or its toxin; and that was not present or incubating at the time of admission to a
healthcare setting (1). For the purposes of this study, the infection must become evident (i.e.
result in a positive culture) 48 hours or more post-admission to a non-ICU ward, unless the
patient had been hospitalized within 30 days before admission, or had been transferred from
another hospital or long-term care facility. Otherwise, positive cultures obtained within 48 hours
of admission were categorized as being community-associated. An ICU-associated infection was
defined as a positive culture drawn 48 hours after admission to the ICU or within 48 hours after
transfer from the ICU (24).
2.4.4 Primary Site of Infection
The primary site of infection was confirmed by culture, clinical evidence, or not confirmed. If
confirmed, the site of infection was categorized as one of the following, according to established
CDC criteria (62): Pneumonia or lower respiratory tract infection; Urinary tract infection;
29
Bloodstream or IV catheter infection (e.g. central venous catheter); Central nervous system
infection; Skin and soft tissue infection; Intra-abdominal infection; and Cardiovascular system
infection. If the site was unconfirmed or determined solely by clinical evidence, then it was
classified as ‘Other’.
2.4.5 Empiric Antibiotic Therapy
A multidisciplinary team of physicians and pharmacists determined requirements for antibiotic
treatment and selection of specific antibiotics during the patients’ hospital course. Antibiotic
therapy was defined as empirical when administered in response to an infectious episode, but
before organism identification and susceptibility results were available.
2.4.6 Adequate vs. Inadequate Empiric Antibiotic Therapy
Empiric antibiotic therapy was critically assessed starting from the date of an infectious episode
(index culture), up to the date of patient discharge from the hospital (Figure 2). Therapy was
considered adequate when, for a given infectious episode, the organism cultured was
subsequently found to have in vitro susceptibility to an antibiotic that was administered within 24
hours of the index culture collection time. A 24 hour period after cultures are taken was used as
the cut-off point given that some evidence points to this period as being important for reducing
mortality rates when adequate therapy is prescribed (8;11;48;55). Additionally, the patient must
receive at least 3 days of therapy. Patients were classified into the inadequate therapy group if
they had experienced at least one episode of inadequate empiric therapy during their hospital
stay.
30
Time
Patient admitted
Symptoms/clinical suspicion
Culture results
Initiate empiric therapy +
samples taken
24 hours
Continue/change initial empiric
therapy
End of symptoms
Discharge or death
Adequate vs. Inadequate
Time
Patient admitted
Symptoms/clinical suspicion
Culture results
Initiate empiric therapy +
samples taken
24 hours24 hours
Continue/change initial empiric
therapy
End of symptoms
Discharge or death
Adequate vs. Inadequate
Figure 2. An illustration depicting the timeline for determining inadequate empiric antibiotic therapy. Therapy was considered adequate when, for a given infectious episode, the organism cultured was subsequently found to be susceptible to an antibiotic that was administered within 24 hours of the sample collection time.
2.4.7 Previous Antibiotic Therapy
Oral and intravenous antibiotics prescribed for patients for at least 3 days and up to 30 days prior
to their hospital admission were recorded.
2.4.8 Previous Graft Rejection and Immunosuppressant Use
Previous episodes of graft rejection and anti-thymocyte globulin or basiliximab use were
recorded for a period of up to six months prior to the patients’ admission date. Since all patients
would be receiving maintenance immunotherapy, we limited data collection to these two potent
immunosuppressants as they may have a greater effect on infection-related outcomes (15).
31
2.4.9 Multi-Drug Resistance
Some bacteria are inherently resistant to specific antibiotics. Organisms found to have in vitro
resistance to two or more antibiotics to which they would normally be susceptible, were
classified as being multi-drug resistant. The Sanford Guide to Antimicrobial Therapy was used
as a reference in determining normal susceptibility patterns (65).
2.5 Outcomes
We compared the primary outcome of in-hospital all-cause mortality between those receiving
adequate versus those receiving inadequate empiric antibiotic therapy. Secondary outcomes
included duration of hospital stay (defined as the number of days from admission to discharge or
death), need for ICU transfer, and duration of stay in the ICU.
2.6 Statistical Analysis
All data were collected and entered into a computerized database using Microsoft Access®
(Microsoft Corporation, Redmond, WA) and analyzed using SPSS 14.0® (SPSS Incorporated,
Chicago, Ill). Categorical data were compared using Fisher’s exact test, and normally distributed
continuous variables were compared using the Student’s t-test. Alternatively, depending on the
validity of the normality assumption, the Wilcoxon rank sum test was also utilized. All
comparisons were unpaired, all tests of significance two-tailed, and equal variances were not
assumed. Values are expressed as the mean ± standard deviation for continuous variables or as a
proportion for categorical variables. Relative risks are reported along with their 95% confidence
intervals. P-values of ≤ 0.05 were considered to be statistically significant. The primary data
analysis compared infected patients who received inadequate antibiotic treatment to infected
32
patients receiving adequate antibiotic treatment. Binary logistic regression analysis was used to
determine independent associations for the dependent outcome variable of in-hospital all cause
mortality. Building of the model began with forced inclusion of IET as the exposure of interest.
All clinically plausible variables with p<0.2 on univariable analysis were also considered for
inclusion in the model. A sequential nested approach was used to enter new terms into the
models with 0.05 as the limit for acceptance or removal of new terms. Results of the logistic
regression analysis are reported as adjusted Odds Ratios (OR) with 95% confidence intervals.
Goodness of fit was assessed by comparing the predicted and observed outcomes as
demonstrated by the contingency table for the Hosmer and Lemeshow Test (66).
33
3.0 RESULTS
3.1 Patients
During the two-year study period, 1675 out of approximately 3300 actively followed transplant
patients were admitted to the multi-organ transplant unit and eligible for evaluation. Of these
patients, 355 were found to have microbiologically documented positive cultures, and 43 were
deemed to be a result of either contamination or colonization. None of these 43 patients received
antibiotic therapy, and all survived their hospital stay. The remaining 312 patients were included
in the evaluation, and were found to have 574 evaluable cultures (Figure 3). Tables 2 and 3
outline various characteristics among patients and infectious episodes receiving adequate versus
inadequate empiric antibiotic therapy. The mean age (49.7 vs. 52.1; p = 0.088), mean graft age
(1.8 vs. 1.4; p = 0.231), proportion of males (60.1% vs. 65.7%; p = 0.347), and severity of illness
score (19.0 vs. 19.8; p = 0.205) did not differ significantly between those receiving adequate
versus inadequate therapy.
3.2 Inadequate Empiric Antibiotic Therapy
Inadequate empiric antibiotic therapy was prescribed in 169/312 (54.2%) patients and among
248/574 (43.2%) cultures. Univariate analysis revealed that the presence of a central IV catheter
(43.2% vs. 25.2%; p<0.001), increasing duration of mechanical ventilation (9.5 vs. 4.0 days;
p=0.016), liver transplantation (40.2% vs. 27.3%; p = 0.017), previous episodes of rejection
(29.6% vs. 18.9%; p = 0.035), anti-thymocyte globulin use (39.1% vs. 24.5%; p = 0.007), and
previous fluoroquinolone use (23.7% vs. 13.3%; p = 0.021), were statistically more likely to be
associated with patients receiving inadequate therapy. Multi-drug resistant organisms (50.8% vs.
34
39.6%; p = 0.009) and intra-abdominal infections (16.9% vs. 7.7%; p < 0.001) were more likely
to occur among patients with cultures that were treated inadequately.
Admitted
SOT Patients N= 1675
Patients with positive cultures
N = 355
Patients with true infections
N = 312
Patients with contam’n/colon’n
N = 43
Patients with no/ negative cultures
N = 1320
Adequate N = 143 (46%)
Inadequate N = 169 (54%)
Survivors N = 133 (93%)
Survivors N = 127 (75%)
Nonsurvivors N = 42 (25%)
Nonsurvivors N = 10 (7%)
Exclude
Exclude
24.9% vs. 7.0% RR = 3.55
95% CI: 1.85 to 6.83 P < 0.001
Figure 3. Incidence and relative risk of mortality for inadequate versus adequate empiric antibiotic therapy among hospitalized solid-organ transplant recipients.
35
Table 2. Characteristics of patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)
AET IET p
No. of Patients 143 (%) 169 (%) Age (yrs) 49.7 ± 13.6 52.1 ± 11.6 0.088 Gender Male 86 (60.1) 111 (65.7) 0.347 Female 57 (39.9) 58 (34.3) Diabetes mellitus requiring insulin
7 (4.9) 11 (6.5) 0.630
Dialysis 17 (11.9) 14 (8.3) 0.344 Acute renal failure 6 (4.2) 10 (5.9) 0.610 Neutropenia 5 (3.5) 5 (3.0) >0.999 Central IV catheter 36 (25.2) 73 (43.2) <0.001 Length of ventilation (days) 4.0 ± 10.7 9.5 ± 26.8 0.016 APACHE II score 19.0 ± 6.0 19.8 ± 5.3 0.205 Graft age (yrs) 1.8 ± 3.9 1.4 ± 3.1 0.231 Organ Heart 6 (4.2) 10 (5.9) 0.610 Heart-Lung 1 (0.7) 1 (0.6) >0.999 Kidney 39 (27.3) 31 (18.3) 0.076 Kidney-Pancreas 7 (4.9) 15 (8.9) 0.190 Liver 39 (27.3) 68 (40.2) 0.017 Lung 50 (35.0) 44 (26.0) 0.107 Small Bowel 1 (0.7) 0 (0.0) 0.458 Previous graft rejection 27 (18.9) 50 (29.6) 0.035 CMV serology D+ R- 22 (15.4) 21 (12.4) 0.511 D+ R+ 32 (22.4) 43 (25.4) 0.595 D- R+ 43 (30.1) 54 (32.0) 0.806 D- R- 21 (14.7) 32 (18.9) 0.365 Unknown 25 (17.5) 19 (11.2) 0.142 Immunosuppression Anti-thymocyte globulin 35 (24.5) 66 (39.1) 0.007 IL-2 receptor antagonists 44 (30.8) 47 (27.8) 0.618 Muromonab-CD3 0 (0.0) 0 (0.0) Previous antibiotic use 67 (46.9) 84 (49.7) 0.650 Carbapenems 5 (3.5) 8 (4.7) 0.778 Aminoglycosides 12 (8.4) 10 (5.9) 0.507 Macrolides 12 (8.4) 8 (4.7) 0.247 Fluoroquinolones 19 (13.3) 40 (23.7) 0.021 Piperacillin-Tazobactam 2 (1.4) 3 (1.8) >0.999 Penicillins 3 (2.1) 6 (3.6) 0.515 3rd/4th Generation Cephalosporins 4 (2.8) 12 (7.1) 0.121 1st/2nd Generation Cephalosporins 0 (0.0) 5 (3.0) 0.065 Cotrimoxazole 30 (21.0) 35 (20.7) >0.999
36
Table 3. Characteristics of true infectious episodes treated with adequate (AET) or inadequate empiric antibiotic therapy (IET)
AET IET p
No. of Positive Cultures 326 (%) 248 (%)
Primary source Pulmonary 100 (30.7) 47 (19.0) 0.002 Urinary 72 (22.1) 69 (27.8) 0.142 Bloodstream/IV Catheter 39 (12.0) 16 (6.5) 0.031 CNS 0 (0.0) 2 (0.8) 0.187 Cardiovascular 2 (0.6) 0 (0.0) 0.508 Intra-Abdominal 25 (7.7) 42 (16.9) <0.001 Skin/Soft Tissue 20 (6.1) 26 (10.5) 0.064 Other 68 (20.9) 46 (18.5) 0.527
Bacteremia 111 (34.0) 63 (25.4) 0.028
Gram-Negative organisms 169 (51.8) 133 (53.6) 0.736
Multi-drug resistant organisms 129 (39.6) 126 (50.8) 0.009
Location acquired Nosocomial 182 (55.8) 153 (61.7) 0.172 ICU 87 (26.7) 63 (25.4) 0.774 Community 57 (17.5) 32 (12.9) 0.132
Among the 574 infectious episodes, 58.2% were classified as being an HAI (excluding ICU),
26.1% were ICU-associated, followed by community-associated infections, which contributed to
15.5% of all episodes. Gram-negative organisms were cultured from 302 (52.6%) episodes. The
most common primary sources of infection among all episodes were found to be pulmonary (147
cultures) and urinary (141 cultures) sources. Table 4 shows the distribution of organisms
associated with the various body sites of infection among the 248 infectious episodes deemed to
have received IET. Coagulase-negative staphylococci and Enterococcus species were the most
commonly cultured gram-positive organisms, whereas Pseudomonas species and Escherichia
coli were the most commonly cultured gram-negative pathogens.
37
Coagulase-negative staphylococci, Escherichia coli, Pseudomonas aeruginosa, and
Enterococcus species were the most common multi-drug resistant organisms found among the
inadequately treated cultures (Table 5). Enterococcus species, coagulase-negative staphylococci,
Escherichia coli, and Citrobacter species were the most common organisms causing intra-
abdominal infections among the inadequately treated cultures (Table 6). The reasons for initial
administration of inadequate empiric antibiotic therapy are listed in Table 7. The most common
reason among both gram-negative and gram-positive organisms, was a delay of more than 24
hours in initiating any type of therapy. Other reasons included resistance of gram-negative
species to 3rd-Generation Cephalosporins, Ciprofloxacin, and other antibiotics initiated within 24
hours of the sample collection time. Among gram-positive isolates, resistance to penicillins and
failure to initiate gram-positive specific therapy also contributed to inadequate empiric therapy.
38
Table 4. Organisms cultured from patients with inadequately treated infectious episodes
Organism Urinary Pulmonary Other Intra- Abdominal
Skin/ Soft Tissue
Line Central Nervous
Total
CNS 1 1 25 12 7 6 2 54 Pseudomonas sp. 7 20 2 5 34 Enterococcus sp. 22 0 1 6 2 31 Escherichia coli 18 1 4 5 2 30 Citrobacter sp. 6 4 4 4 1 19 Klebsiella sp. 6 2 1 3 1 2 15 Enterobacter cloacae 2 3 3 1 1 2 12 Enterococcus faecium 4 4 2 10 Enterococcus faecalis 2 1 2 3 1 9 Acinetobacter sp. 1 1 2 1 5 Serratia marcescens 4 1 5 MSSA 1 2 1 1 5 MRSA 2 1 3 Hafnia alvei 1 1 1 3 Enterobacter aerogenes
1 2 3
Moraxella catarrhalis 2 2 Proteus mirabilis 2 2 Haemophilus influenzae
1 1 2
Streptococcus sp. 1 1 2 Enterococcus gallinarum
1 1
Burkholderia cepacia 1 1
Total 69 47 46 42 26 16 2 248
39
Table 5. Multi-drug resistant organisms cultured among patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)
Multi-drug Resistant Organisms AET
(N=326) (%) IET
(N=248) (%)
Coagulase-negative staphylococci 49 (38.0) 47 (37.3) Escherichia coli 25 (19.4) 20 (15.9) Pseudomonas aeruginosa 12 (9.3) 19 (15.1) Enterococcus sp. 10 (7.8) 11 (8.7) MRSA 7 (5.4) 3 (2.4) Enterobacter sp. 7 (5.4) 4 (3.2) Citrobacter sp. 6 (4.7) 9 (7.1) Klebsiella sp. 5 (3.9) 9 (7.1) Acinetobacter sp. 2 (1.6) 2 (1.6) Burkholderia cepacia 4 (3.1) 1 (0.8) Others 2 (1.6) 1 (0.8)
TOTAL 129 (100) 126 (100)
Table 6. Intra-abdominal organisms cultured among 17 patients receiving adequate (AET) and 30 patients receiving inadequate empiric antibiotic therapy (IET)
Intra-abdominal Organisms AET
(n=17) (%) IET
(n=30) (%)
Enterococcus sp. 7 (28) 13 (31.0) Coagulase-negative Staphylococci 6 (24) 12 (28.6) Escherichia coli 1 (4) 5 (11.9) Citrobacter sp. 2 (8) 4 (9.5) Klebsiella sp. 3 (12) 3 (7.1) Acinetobacter sp. 0 (0) 2 (4.8) Pseudomonas aeruginosa 1 (4) 2 (4.8) Enterobacter cloacae 4 (16) 1 (2.4) Serratia sp. 1 (4) 0 (0.0)
TOTAL 25 (100) 42 (100)
40
Table 7. Reasons for administration of inadequate empiric therapy
Reason No. (%)
GNB therapy initiated >24h after sample 85 (34.2) GPB therapy initiated >24h after sample (Other) 53 (21.4) GPB therapy initiated >24h after sample (CNS) 39 (15.7) GNB resistant to other empiric therapy 24 (9.7) GNB resistant to 3rd-Generation Cephalosporins 14 (5.6) GNB resistant to Ciprofloxacin 13 (5.2) GPB resistant to penicillins 11 (4.4) No GPB-specific antibiotic initiated 9 (3.6)
TOTAL 248
• GNB = Gram-negative bacteria • GPB = Gram-positive bacteria • CNS = Coagulase-negative staphylococci
3.3 Characteristics Related to Hospital Mortality
Of the 312 patients evaluated, 52 did not survive their in-hospital stay. Univariate analyses
revealed that lung transplant recipients (50.0% vs. 26.2%; p < 0.001), prior antibiotic use (69.2%
vs. 44.2%; p < 0.001), and ICU-associated infections (59.1% vs. 16.3%; p < 0.001) were more
likely to be associated among the 52 nonsurvivors (Tables 8 and 9). The in-hospital mortality
rate for patients receiving at least one episode of inadequate empiric therapy was significantly
greater than those receiving adequate therapy (24.9% vs. 7.0%; RR, 3.55; 95% CI, 1.85 to 6.83;
p < 0.001; Figure 3). Furthermore, there was a significant association between increasing time to
administration of adequate empiric therapy (measured in 24 hour increments) and increased
hospital mortality rates (Figure 4). During the same two-year period, the mean mortality rate for
all patients admitted to the transplant unit was 4.7%. Organisms cultured from patients receiving
IET and their associated mortality rates are shown in Table 10. The most commonly cultured
pathogens among non-survivors included Pseudomonas sp., Enterococcus sp., Escherichia coli,
Citrobacter sp., Klebsiella sp., and Enterobacter cloacae.
41
Table 8. Patient characteristics among hospital survivors and nonsurvivors
Nonsurvivors Survivors p
No. of Patients
52 (%) 260 (%)
Age (yrs) 51.0 ± 13.3 51.0 ± 12.5 0.995
Gender Male 31 (59.6) 166 (63.8) 0.637 Female 21 (40.4) 94 (36.2) Diabetes requiring insulin 2 (3.8) 16 (6.2) 0.747
Dialysis 4 (7.7) 27 (10.4) 0.799
Acute renal failure 3 (5.8) 13 (5.0) 0.736
Neutropenia 2 (3.8) 8 (3.1) 0.675
Central IV catheter 37 (71.2) 72 (27.7) <0.001
Previous ICU admission 17 (32.7) 37 (14.2) 0.002
Length of ventilation (days) 3.1 ± 9.3 26.4 ± 42.9 <0.001
APACHE II score 25.6 ± 7.6 18.2 ± 4.2 <0.001 Graft age (yrs) 1.8 ± 3.3 1.5 ± 3.5 0.629
Organ
Heart 2 (3.8) 14 (5.4) >0.999
Heart-Lung 0 (0.0) 2 (0.8) >0.999
Kidney 4 (7.7) 66 (25.4) 0.004
Kidney-Pancreas 1 (1.9) 21 (8.1) 0.143
Liver 19 (36.5) 88 (33.8) 0.750
Lung 26 (50.0) 68 (26.2) <0.001
Small Bowel 0 (0.0) 1 (0.4) >0.999
Previous graft rejection 10 (19.2) 67 (25.8) 0.381
CMV serology
D+ R- 4 (7.7) 39 (15.0) 0.192
D+ R+ 15 (28.8) 60 (23.1) 0.378
D- R+ 12 (23.1) 85 (32.7) 0.192
D- R- 11 (21.2) 42 (16.2) 0.418
Unknown 10 (19.2) 34 (13.1) 0.274
Immunosuppression
Anti-thymocyte globulin 17 (32.7) 84 (32.3) >0.999
IL-2 receptor antagonist 12 (23.1) 79 (30.4) 0.321
Muromonab-CD3 0 (0.0) 0 (0.0)
Previous antibiotic use 36 (69.2) 115 (44.2) <0.001
Carbapenems 5 (9.6) 8 (3.1) 0.047
Aminoglycosides 6 (11.5) 16 (6.2) 0.229
Macrolides 5 (9.6) 15 (5.8) 0.348
Fluoroquinolones 18 (34.6) 41 (15.8) 0.003
Piperacillin-Tazobactam 1 (1.9) 4 (1.5) >0.999
Penicillins 1 (1.9) 8 (3.1) >0.999
3rd/4th Gen. Cephalosporins 4 (7.7) 13 (5.0) 0.499
1st/2nd Gen. Cephalosporins 3 (5.8) 3 (1.2) 0.060
Cotrimoxazole 15 (28.8) 50 (19.2) 0.135
42
Table 9. Culture characteristics among hospital survivors and nonsurvivors
Nonsurvivors Survivors p No. of Positive Cultures
132 (%) 442 (%)
Primary source Pulmonary 42 (31.8) 106 (24.0) 0.089 Urinary 28 (21.2) 113 (25.6) 0.357 Line 16 (12.1) 39 (8.8) 0.311 CNS 0 (0.0) 2 (0.5) >0.999 Endocardium 1 (0.8) 1 (0.2) 0.407 Intra-Abdominal 17 (12.9) 50 (11.3) 0.643 Skin/Soft Tissue 7 (5.3) 39 (8.8) 0.272 Other 21 (15.9) 93 (21.0) 0.216 Bacteremia 46 (34.8) 153 (34.6) >0.999 Gram Negative Cultures 75 (56.8) 226 (51.1) 0.276 Multi-drug resistant organisms
68 (51.5) 187 (42.3) 0.072
Location acquired Nosocomial 48 (36.4) 287 (64.9) <0.001 ICU 78 (59.1) 72 (16.3) <0.001 Community 6 (4.5) 83 (18.8) <0.001
Table 10. Most commonly cultured organisms and their associated mortality among patients receiving adequate (AET) or inadequate empiric antibiotic therapy (IET)
Organism AET
Nonsurvivors/ Patients
Mortality (%)
IET Nonsurvivors/
Patients
Mortality (%)
p
Acinetobacter species 1/5 (20.0) 2/5 (40.0) >0.999
Citrobacter freundii 1/15 (6.7) 6/18 (33.3) 0.095
CNS 0/58 (0.0) 0/54 (0.0) -
Enterobacter cloacae 7/14 (50.0) 6/12 (50.0) >0.999
Enterococcus sp. 8/61 (13.1) 15/52 (28.8) 0.043
Escherichia coli 6/39 (15.4) 7/30 (23.3) 0.537
Klebsiella sp. 7/23 (30.4) 3/15 (20.0) 0.709
MSSA 7/31 (22.6) 1/5 (20.0) >0.999
Pseudomonas aeruginosa 5/45 (11.1) 14/32 (43.8) 0.003
Serratia sp. 1/7 (14.3) 0/5 (0.0) >0.999
43
7.5
55.6
24.128.2
0
10
20
30
40
50
60
70
80
≤24 24-48 48-72 >72
Time to administration of adequate empiric therapy (h)
Mort
alit
y (
%)
Figure 4. Delay in administration of adequate empiric antibiotic therapy and associated mortality rates.
3.4 Logistic Regression Analysis
Binary logistic regression analysis was conducted to determine independent risk factors for
hospital mortality (Table 11). Increasing time to administration of adequate empiric antibiotic
therapy (per one hour increment) was found to be an independent determinant of in-hospital
mortality (adjusted OR, 1.01; 95% CI, 1.006 to 1.018; p < 0.001). ICU-associated infections
(adjusted OR, 6.27; 95% CI, 2.79 to 14.09; p < 0.001), antibiotic use 30 days prior to admission
(adjusted OR, 3.56; 95% CI, 1.51 to 8.41; p = 0.004), and an increasing APACHE II score
(adjusted OR, 1.26; 95% CI, 1.16 to 1.34; p < 0.001), were also identified as independent
predictors of hospital mortality. Raw data and detailed results of the binary logistic model are
found in Appendices II and III respectively.
p<0.001
P=0.049
p=0.001
N=143 N=42 N=36 N=91
44
Table 11. Logistic regression analysis predicting hospital mortality
Predictor B Wald χ2 P-value Adjusted
Odds Ratio 95% CI
Lower Upper
IET (1 hour increment) 0.012 14.311 <0.001 1.012 1.006 1.018
APACHE II score (1 point increment)
0.227 39.467 <0.001 1.255 1.163 1.336
Previous antibiotic use 1.270 8.408 0.004 3.562 1.509 8.406
ICU infection 1.836 19.781 <0.001 6.273 2.793 14.090
Constant -8.698 70.696
3.5 Secondary Outcomes
Secondary outcomes are reported in Table 12. In addition to increased mortality rates,
inadequately treated patients were more likely to require transfer to the ICU (26.0% vs. 7.0%; p
< 0.001), with a total of 621 ICU patient days. The average in-hospital length of stay was also
statistically greater among those receiving IET, although the average ICU length of stay did not
differ significantly. The total number of inpatient days among patients receiving IET was more
than twofold compared to those receiving adequate therapy (8 697 vs. 4 305 days).
Table 12. Outcomes of patients receiving adequate (AET) vs. inadequate empiric antibiotic therapy (IET)
AET IET p
No. of Patients 143 (%) 169 (%)
Nonsurvivors 10 (7.0) 42 (24.9) <0.001 Length of stay (mean days) 30.1 ± 47.8 51.5 ± 56.8 <0.001 Total inpatient days 4 305 8 697 Transfers to ICU 10 (7.0) 44 (26.0) <0.001 Length of ICU stay (mean days) 10.6 ± 16.4 14.2 ± 17.7 0.549 Total ICU days 109 621
45
4.0 DISCUSSION
This single-centre retrospective study demonstrated that inadequate empiric antibiotic therapy
occurs in approximately half (169/312) of our hospitalized solid-organ transplant recipients
being treated for infection. Approximately one-quarter (42/169) of patients receiving IET did not
survive their hospital stay, which is in relative agreement with mortality rates ranging from 31-
69% among critically ill patients receiving IET (Table 1). Controlling for several confounders,
solid-organ transplant patients with infections were at greater risk for in-hospital mortality the
longer they received IET (adjusted OR, 1.012 per one-hour increment). It may be more
significant to look at increments other than one-hour units. For example, an increase in IET by
24 hours increases the adjusted OR to 1.33, assuming that the logit function is linear for this
continuous covariate. Despite the importance of antibiotic use in this immunosuppressed
population, the benefits of adequate empiric therapy have been unclear. Given that our institution
accounts for approximately 40% of all new transplant recipients in the province of Ontario, and
15% of all new transplant patients in Canada, these findings cannot be taken lightly (61). Results
from this study indicate that in addition to critically ill patients, solid-organ transplant recipients
also experience inadequate empiric therapy at a rate that has a significant clinical effect on
hospital mortality. Moreover, inadequately treated patients required a more resource intensive
hospital stay, with increased ICU transfers and longer durations of stay.
Among patients and cultures treated with inadequate empiric therapy, we also identified potential
risk factors for its administration. Patients with prior antibiotic use, specifically
fluoroquinolones, and isolates that were multi-drug resistant, were more likely to have received
IET. In addition to being a potential risk factor for IET, prior antibiotic use was also found to be
an independent determinant of hospital mortality (adjusted OR, 3.56), though multi-drug
46
resistance was not retained in the final regression model. Late initiation of empiric therapy was
the most common reason for the administration of IET, however, resistance to therapy still
accounted for approximately one-quarter of all IET cases. Resistance is especially problematic in
the context of relatively pathogenic isolates such as P. aeruginosa (50), which was the most
common multi-drug resistant gram-negative isolated from 14 of the 52 non-survivors. A number
of studies have examined the relationship between antibiotic use and the development of
antibiotic resistance (29;67;68). Vanderkooi et al. (68) aimed to identify risk factors, including
previous antibiotic therapy, which were predictive of antibiotic resistance in invasive S.
pneumoniae infections. The authors found that patients who had previously received courses of
trimethoprim-sulfamethoxazole, macrolides, and fluoroquinolones, were at least four times as
likely to have an infection with an isolate that was resistant to the same class of antibiotics. They
also observed that previous use of agents from any antibiotic class (except fluoroquinolones) was
associated with infections due to isolates that were also resistant to agents from other classes,
including penicillin. Moreover, healthcare-associated infections were more likely to have a
fluoroquinolone-resistant isolate cultured (adjusted OR, 12.9; 95% CI, 3.95 to 43.8; p < 0.001
and 9.94; 95% CI, 2.22 to 44.6; p = 0.003, respectively for infections acquired in a nursing home
or hospital). The emergence of resistant bacteria, specifically gram-negatives, to 3rd-Generation
Cephalosporins, Ciprofloxacin, and other antibiotics, continues to constitute a major barrier to
adequate therapy (13). Furthermore, colonization with resistant pathogens may predispose
certain patients to re-infection with these resistant organisms (9).
In addition to prior antibiotic usage, we observed that treatment of episodes of previous graft
rejection, liver transplant recipients, patients with intra-abdominal infections, central IV
catheterization, and mechanical ventilation were also more likely to be associated with IET. The
47
increased use of novel immunosuppressive agents along with the use of antimicrobial agents,
continues to alter the epidemiology of infections in solid-organ transplant recipients (15;69). The
use of potent anti-rejection agents, including anti-thymocyte globulin use, may increase the net
state of immunosuppression in transplant recipients, depressing cell-mediated immunity and
increasing their susceptibility to infectious complications (70). The use of anti-thymocyte
globulin has been associated with a greater overall risk of infection, especially for opportunistic
viral infections, such as CMV (69). However, a recent observational study of renal transplant
recipients revealed that anti-thymocyte globulin was also associated with an increased risk for
bacterial infections (adjusted OR, 3.3; 95% CI, 1.3 to 7.9; p = 0.009) (15). Previous studies have
reported that 7-11% of bacteremias among liver transplant recipients were polymicrobial in
nature (14;71). Liver transplant recipients and those with intra-abdominal infections tend to
acquire polymicrobial infections that may be more difficult to treat empirically, resulting in
inadequate therapy (18). Increased mortality rates have been associated with Enterococcus sp.
isolated from polymicrobial intra-abdominal infections (72), and in addition to drainage of any
focal collections, providing specific therapy directed at this pathogen does seem to be justified
(73). We observed that all 13 intra-abdominal isolates of Enterococcus sp. were treated
inadequately and that 6/13 were in fact resistant to penicillins. Thus, local resistance levels
among gram-positive organisms, such as Enterococcus sp., in addition to gram-negatives should
be considered carefully before initiation of therapy for intra-abdominal infections. Several
studies have demonstrated associations between central IV catheterization or increasing duration
of mechanical ventilation and the occurrence of bloodstream infections or VAP, respectively.
These studies have found that the longer the duration of central vein catheterization or
mechanical ventilation, the more likely it is for patients to develop bloodstream or pulmonary
infections with resistant bacteria (40;74;75).
48
4.1 Study Limitations
Several limitations for this study were identified, including selection and recall biases. These
limitations were minimized through the use of inclusion criteria, and the use of different data
sources to validate the data collected. Since this study utilized retrospectively collected data,
there is a possibility of a recall bias when data was collected from the respective data sources.
The impact of this bias was lessened by the availability of redundant data sources including
electronic and paper medical records, in addition to clinical pharmacists’ patient profiles. In
addition, the inclusion of some less virulent contaminants as pathogens may have decreased the
overall attributable mortality among all pathogens.
Selection bias may occur if the patients from whom microbiological samples are obtained are not
typical of the entire population with infections. For example, physicians may be more likely to
take samples from patients who have recurrent symptoms or significant co-morbidities. This may
skew the data towards more frequent reporting of certain infections over others. To reduce this
possibility, we excluded duplicate isolates of the same organism, and only included the first
isolate cultured.
Given that this study was conducted in a single centre, the results reported are reflective of
institutional-specific practices and standards of care, which may differ from other centres. Since
randomization would not have been ethical and blinding difficult to achieve, this could result in
exposures being linked to hidden confounders. Eligibility criteria and outcome assessments can
be standardised, although measurement bias is a possibility if, for example, the outcome was
detected more accurately in the prospective cohort.
49
The definition of adequate therapy utilized in vitro susceptibility as the main requirement.
Determining the impact of inadequate empiric therapy poses a problem of characterization, in
that there seems to be varying definitions of what constitutes inadequate therapy.
Pharmacokinetic and pharmacodynamic considerations, such as dosage, duration of treatment,
concentration breakpoints, and tissue penetration, could also be important in defining adequate
treatment. Adequate therapy may much more than merely a laboratory result indicating in vitro
susceptibility. Additionally, the timing of antibiotic treatment may also be an important
consideration. We examined a single time point of 24 hours to define adequate therapy, although
it has been well accepted that antibiotics should be given as soon as possible. It seems that the
effective timeframe for administrating antibiotics in some infections is relatively narrow (11),
and other time points less than this cut-off may also be significant. We identified our primary
outcome as in-hospital mortality, an outcome that can be measured explicitly. However, we were
not able to directly associate the outcome to the infection in question, although implying
infectious causation can be subjective (76).
4.2 Implications of Inadequate Empiric Therapy
The increased mortality rates associated with inadequate therapy suggest that starting empiric
therapy at the earliest signs of infection may be beneficial. Unfortunately, if the empiric therapy
chosen does not demonstrate in vitro susceptibility, a change of antibiotic therapy after
susceptibility results are reported does not seem to improve clinical outcomes (9;77;78). As
previously described, the benefits of starting adequate empiric therapy early in the course of the
infectious episode (< 24 hours) seem to be significant, and the results of this study appear to
support these previous findings. This will require a higher degree of clinical suspicion, both in
50
terms of timely therapy and antibiotic coverage for organisms, such as some gram-positive
bacteria, which may not be initially suspected. We observed that Enterococcus and Pseudomonas
species, mostly from intra-abdominal and pulmonary sources respectively, were the most
common isolates from non-survivors. They were also observed to be some of the most common
multi-drug resistant isolates as well. For patients at risk for these types of infections, and having
received prior antibiotic therapy, initiation of empiric therapy with either a different class of
antibiotics, or the use of combination therapy may be more prudent (79). However, in order to
thwart the development of resistance, therapy should be continuously reassessed in order to
prescribe the narrowest spectrum of coverage possible, and to discontinue therapy as soon as it is
clinically appropriate.
In an effort to control the increase in antibiotic resistance, and possibly improve adequate
treatment, multiple strategies have emerged (80). To this end, development of a system for
reporting antibiotic susceptibility patterns for specific units on a regular basis in order to capture
local temporal pattern changes could be used as a guide to initiate adequate therapy. Antibiotic
susceptibility data are often aggregated into antibiograms, which are helpful tools that
summarize commonly cultured organisms and their susceptibility to routinely used antibiotics.
Unfortunately, they may not necessarily predict the susceptibility patterns from a particular
patient since reported data are rarely stratified by other patient factors. One such factor includes
the location of the hospitalized patient. The types of pathogens associated with nosocomial
infections in ICUs from different institutions, along with their antibiotic susceptibility profiles,
have been shown to vary (35). Furthermore, others have discovered variability in the
susceptibility profiles of micro-organisms among surgical, trauma, and medical ICUs within a
single large teaching hospital (36). This suggests that hospitals may not only need to develop
51
their own systems for reporting patterns of antibiotic susceptibility, but may also need to take
into account unit-specific patterns of antibiotic resistance. Such information may help clinicians
develop more rational prescribing practices that will avoid inadequate antibiotic treatment of
hospitalized patients. A joint committee of the Society for Healthcare Epidemiology of America
and the Infectious Diseases Society of America has developed a set of recommendations for the
prevention and reduction of antimicrobial resistance in hospitals (80). These recommendations
include the monitoring of antimicrobial use on a regular basis, in addition to monitoring the
relationship between antimicrobial use and resistance. These responsibilities should be assigned
through practice guidelines or other institutional policies.
5.0 CONCLUSIONS
We found that inadequate empiric therapy is common and appears to be an important
determinant of hospital mortality among Canadian solid-organ transplant patients. No studies to
date have tackled the subject of inadequate empiric antibiotic use in transplant recipients and its
relationship to hospital mortality. Moreover, little advancement has been made in increasing the
proportion of hospitalized patients receiving adequate empiric antibiotic treatment. Development
of an antibiogram reporting system may help clinicians develop more rational prescribing
practices that will reduce the unnecessary administration of broad-spectrum drugs and avoid
inadequate antibiotic treatment in transplant patients. Future studies may concentrate on
stratification of antibiogram data by additional patient characteristics that might be useful in
improving empiric treatment selections. Efforts aimed at identifying patients at risk and reducing
the occurrence of inadequate empiric therapy may improve outcomes.
52
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7.0 PUBLICATIONS AND ABSTRACTS TO DATE Abstract/oral presentation: American Transplant Congress, June 2006, Boston, MA Inadequate Empiric Antibiotic Therapy among Solid-Organ Transplant Patients: Incidence and Impact on Hospital Mortality. Hamandi B, Holbrook A, Humar A, Brunton J, Papadimitropoulos M, Wong G. Transplantation 2006 Jul 15;82(1)S3:442-3.
59
8.0 APPENDICES
Appendix I – Literature Review Search Strategy
Database: Ovid MEDLINE(R) 1950 - Search Strategy:
# Search History 1 organ transplantation/ or heart transplantation/ or kidney transplantation/ or liver transplantation/ or exp
lung transplantation/ or pancreas transplantation/ 2 exp Hospitals/ 3 exp Intensive Care Units/ 4 Critical Illness/ 5 exp Critical Care/ 6 Inpatients/ 7 intensive care.tw. 8 icu.tw. 9 or/1-8 10 exp Bacterial Infections/ 11 exp Anti-Bacterial Agents/ 12 exp Infection/ 13 exp Anti-Infective Agents/ 14 exp Pneumonia/ 15 Septicemia.tw. 16 (bacter: adj2 infect:).tw. 17 or/10-16 18 9 and 17 19 exp Morbidity/ 20 exp Mortality/ 21 exp Hospitalization/ 22 exp epidemiologic studies/ 23 exp Prognosis/ 24 exp risk/ 25 or/19-24 26 18 and 25 27 exp Drug Administration Schedule/ 28 ((adequa: or inadequa: or appropriate: or inappropriate: or condordant or discordant) adj2 (antibiotic: or
antibacterial or antimicrobial or therapy)).mp. 29 or/27-28 30 26 and 29 31 limit 30 to english language
60
Appendix II – Raw Data
pt Patient identification number death 1 = died in-hospital; 0 = did not die in-hospital adeq Number of hours of IET lungtx 1 = lung transplant recipient; 0 = not lung transplant recipient abxuse 1 = previous antibiotic use; 0 = no previous antibiotic use icu 1 = icu-related infection; 0 = no ICU-related infection pulmon 1 = pulmonary infection; 0 = no pulmonary infection mdr 1 = MDR infection; 0 = no MDR infection apache APACHE-II score within 24 hours of admission vent Number of days on mechanical ventilation cvc 1 = presence of CVC; 0 = absence of CVC icutx 1 = required ICU transfer; 0 = did not require ICU transfer iculos ICU length of stay in days
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
1 0 56 0 1 0 0 0 21 0 0 0 0
2 0 2 0 0 0 0 1 19 0 0 0 0
3 0 2 0 0 1 0 0 20 1 1 0 0
4 1 5 0 1 0 0 0 34 1 0 0 0
5 0 96 0 0 0 0 1 21 0 1 0 0
6 0 49 0 0 1 0 1 17 1 1 0 0
7 0 2 0 0 0 0 0 20 0 0 0 0
8 0 185 0 0 1 0 1 13 13 1 1 13
9 0 3 1 0 0 1 0 16 5 0 0 0
10 0 33 0 1 0 0 1 19 0 0 0 0
11 0 179 0 0 0 0 0 19 0 0 0 0
12 0 3 1 1 0 1 1 19 0 0 0 0
13 0 3 0 0 0 0 1 20 0 0 0 0
14 1 168 0 1 1 1 1 20 115 1 0 0
15 0 2 0 1 0 0 0 19 0 0 0 0
16 0 2 0 1 0 0 0 21 0 1 0 0
17 0 1 0 0 0 0 0 18 0 0 0 0
18 0 13 0 0 0 0 1 20 0 1 0 0
19 0 85 0 0 0 0 1 21 0 1 0 0
20 0 187 0 0 0 0 1 19 0 1 1 21
21 1 185 1 1 1 1 1 27 16 0 1 21
22 0 9 0 0 0 0 1 19 0 0 0 0
23 0 2 1 1 0 1 1 19 0 0 0 0
24 0 29 0 1 0 0 1 12 0 1 1 1
25 0 5 0 0 0 0 1 21 0 0 0 0
26 0 174 0 0 0 0 0 19 0 0 0 0
27 0 52 0 0 0 0 0 20 0 1 1 1
28 0 10 0 1 0 0 1 18 0 0 0 0
29 0 1 1 0 0 1 0 17 3 0 0 0
30 0 3 1 1 1 1 1 10 42 1 0 0
31 0 4 0 0 0 0 0 10 3 1 0 0
32 1 6 1 1 1 0 1 26 34 1 0 0
33 0 186 0 0 0 0 0 20 0 0 0 0
34 0 23 0 1 0 0 1 19 0 0 0 0
35 0 92 0 0 0 1 1 18 0 0 0 0
36 0 3 1 1 1 1 1 10 2 0 0 0
37 0 2 0 1 0 0 1 21 0 0 0 0
38 0 16 0 0 0 0 0 19 0 0 0 0
39 0 60 0 0 0 0 1 18 0 0 0 0
40 0 179 0 0 0 0 0 14 1 0 0 0
41 0 3 0 0 0 0 0 21 0 0 0 0
61
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
42 1 182 0 1 1 1 1 18 1 0 0 0
43 0 27 0 1 0 0 1 20 0 0 0 0
44 0 103 0 1 0 1 1 19 0 0 0 0
45 0 93 0 0 1 0 1 23 2 0 1 1
46 0 10 0 0 0 0 0 10 2 0 1 3
47 1 191 1 1 0 0 1 20 0 1 0 0
48 0 177 0 0 0 1 0 20 0 0 0 0
49 0 1 1 1 0 1 1 17 0 0 0 0
50 0 3 1 1 0 1 0 14 6 0 0 0
51 0 3 0 0 0 0 0 15 1 0 0 0
52 0 2 1 0 0 1 0 19 0 0 0 0
53 0 2 0 0 0 0 0 18 0 0 0 0
54 0 27 0 1 0 0 1 21 0 0 0 0
55 0 173 0 0 0 0 1 18 0 1 0 0
56 1 8 0 1 1 0 1 33 1 1 1 4
57 0 91 0 0 0 0 1 21 0 0 0 0
58 0 2 0 0 0 1 1 22 0 0 0 0
59 0 170 0 1 0 0 1 20 0 1 1 1
60 0 12 0 0 0 1 0 20 0 0 0 0
61 0 6 0 1 0 0 0 19 0 0 0 0
62 0 85 1 1 0 1 1 27 6 0 0 0
63 0 21 0 1 0 0 0 19 0 0 0 0
64 0 180 0 1 0 0 0 12 3 1 0 0
65 1 14 0 1 1 1 1 32 1 1 1 20
66 0 24 0 1 0 0 1 18 0 1 0 0
67 0 3 0 0 0 0 1 20 0 0 0 0
68 0 1 0 0 0 0 0 20 0 0 0 0
69 0 3 0 0 0 0 0 10 1 0 0 0
70 1 65 1 1 1 1 1 23 40 1 1 9
71 1 170 1 1 1 1 1 20 209 1 1 24
72 0 16 1 0 0 0 0 19 0 0 0 0
73 1 10 0 0 0 0 1 39 1 1 0 0
74 0 23 1 0 1 1 0 12 2 1 1 2
75 0 21 0 0 0 0 1 7 1 0 0 0
76 0 5 1 1 0 0 1 20 0 0 0 0
77 0 14 0 1 0 0 0 18 0 0 0 0
78 0 191 0 1 0 0 0 16 1 0 1 10
79 0 1 0 1 0 0 0 20 0 0 0 0
80 0 31 0 0 0 1 1 19 0 0 0 0
81 0 11 0 1 0 0 0 20 0 0 0 0
82 0 1 0 1 0 0 1 23 0 0 1 1
83 0 34 0 1 0 1 0 27 1 1 0 0
84 0 1 1 1 0 1 0 18 0 0 0 0
85 0 68 0 1 0 0 1 20 0 0 0 0
86 0 54 0 1 0 0 0 19 0 0 0 0
87 0 26 0 0 0 0 1 18 0 1 0 0
88 0 3 0 0 0 0 0 21 0 0 0 0
89 0 180 0 1 0 0 1 20 0 0 1 1
90 1 30 0 0 0 1 0 37 5 0 1 6
91 0 50 0 0 0 0 1 18 4 0 1 7
92 0 10 0 1 0 0 1 21 0 0 0 0
93 0 31 0 0 0 0 0 18 0 0 0 0
94 0 5 0 1 0 0 0 19 0 0 0 0
95 0 1 0 0 0 1 0 19 0 0 0 0
96 0 17 0 0 0 0 1 20 0 1 0 0
97 0 75 1 0 0 1 1 16 3 0 0 0
98 0 54 0 0 0 0 0 20 0 0 0 0
99 0 16 0 1 0 0 1 18 0 0 0 0
100 0 102 0 1 1 0 1 13 2 0 0 0
101 0 1 0 0 1 1 0 23 51 0 1 54
102 0 1 0 1 0 0 1 18 0 0 0 0
103 0 3 1 1 0 1 1 17 0 0 0 0
62
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
104 0 191 0 1 0 0 1 19 0 1 0 0
105 0 30 0 0 0 0 1 18 0 0 0 0
106 0 192 0 0 1 0 1 17 5 1 0 0
107 0 27 0 1 0 0 0 19 0 0 0 0
108 1 31 0 0 1 0 0 20 0 1 0 0
109 1 120 0 0 0 1 1 39 2 1 1 2
110 0 59 0 1 0 0 0 17 0 0 0 0
111 0 60 0 1 0 0 0 18 0 0 0 0
112 0 2 1 1 0 1 0 20 0 0 0 0
113 1 32 0 0 0 0 0 19 0 0 0 0
114 1 188 0 0 1 1 1 28 40 1 1 28
115 0 78 0 1 0 0 1 20 0 0 0 0
116 0 1 0 1 0 0 0 17 0 0 0 0
117 1 181 0 1 0 0 1 18 0 1 1 7
118 0 3 0 0 0 0 1 35 9 1 0 0
119 0 2 0 1 0 0 0 18 0 0 0 0
120 0 50 0 1 0 0 1 20 0 1 0 0
121 0 50 0 0 0 0 0 18 0 0 0 0
122 0 51 1 1 1 1 1 31 24 1 0 0
123 0 15 0 0 0 0 0 19 0 0 0 0
124 0 36 0 0 0 0 1 20 0 1 0 0
125 1 66 1 1 1 0 1 15 69 1 0 0
126 0 2 0 0 0 0 0 20 0 0 0 0
127 0 86 0 0 0 0 1 20 0 0 0 0
128 1 68 0 1 1 0 1 25 52 1 1 1
129 0 16 0 1 0 0 1 20 0 1 0 0
130 0 22 0 0 0 0 1 21 0 1 0 0
131 1 184 0 1 0 0 1 20 0 1 0 0
132 0 58 1 1 0 1 1 20 0 0 0 0
133 0 3 0 0 0 0 0 21 0 0 0 0
134 0 56 0 0 0 0 1 18 0 1 0 0
135 0 2 0 1 0 1 0 20 0 0 0 0
136 0 69 0 1 0 0 1 11 1 0 1 1
137 1 32 1 1 0 1 1 25 44 0 1 44
138 0 8 0 1 0 0 1 18 0 0 0 0
139 0 1 0 0 1 0 0 16 1 0 0 0
140 0 173 1 1 0 0 1 11 4 0 0 0
141 0 192 0 0 0 0 0 19 0 0 0 0
142 1 33 1 1 1 0 1 20 1 1 0 0
143 1 10 0 0 1 0 1 39 1 0 0 0
144 1 35 1 1 0 1 0 19 0 0 0 0
145 0 17 1 1 0 1 1 23 16 0 0 0
146 0 2 1 1 0 0 0 18 0 0 0 0
147 0 58 0 0 0 1 0 15 1 0 0 0
148 0 2 0 1 1 1 1 29 11 1 0 0
149 0 30 0 0 0 1 1 20 0 0 0 0
150 0 106 0 0 0 0 1 25 1 1 0 0
151 0 62 0 1 0 0 0 19 0 0 0 0
152 0 16 0 1 0 0 1 18 0 0 0 0
153 0 171 0 1 0 0 1 19 0 1 1 4
154 0 2 0 0 1 0 0 7 1 0 0 0
155 1 70 1 1 1 1 1 19 112 1 0 0
156 0 11 0 1 0 0 0 19 0 1 0 0
157 0 3 1 0 1 1 0 30 17 1 0 0
158 0 35 0 1 0 0 1 19 0 0 0 0
159 1 173 0 0 0 0 1 24 1 1 1 5
160 0 70 0 1 0 0 1 18 0 0 0 0
161 0 2 0 0 0 0 0 9 1 0 1 5
162 0 181 0 1 0 0 0 20 0 0 0 0
163 0 3 0 1 0 0 1 20 0 0 0 0
164 1 36 1 0 1 1 1 29 24 1 0 0
165 0 1 0 1 1 0 1 21 9 1 0 0
63
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
166 0 4 0 1 0 0 1 19 0 0 0 0
167 0 86 0 0 0 0 1 17 3 0 1 3
168 0 57 0 1 1 0 1 15 2 0 1 2
169 0 188 0 0 0 0 1 9 1 0 0 0
170 0 176 0 0 1 1 1 10 2 1 0 0
171 0 93 0 1 0 0 1 13 1 0 1 37
172 0 6 0 1 0 0 1 15 1 1 1 10
173 0 1 1 0 0 0 1 20 0 0 0 0
174 1 71 1 0 0 0 0 19 0 0 1 1
175 0 5 0 0 0 0 0 18 0 0 0 0
176 0 2 0 1 0 0 1 19 0 0 0 0
177 0 25 1 0 0 1 1 12 1 0 0 0
178 0 185 0 0 0 1 0 20 1 0 0 0
179 0 190 0 1 0 0 1 28 2 1 1 5
180 0 1 0 0 0 0 0 18 0 0 0 0
181 0 3 1 1 1 0 1 29 44 1 0 0
182 0 54 0 0 0 0 1 17 0 0 0 0
183 0 50 1 1 0 1 0 20 1 0 0 0
184 0 89 0 0 0 0 0 18 0 0 0 0
185 0 3 1 0 0 1 0 19 0 0 0 0
186 1 177 1 1 0 0 1 17 0 0 0 0
187 0 49 1 1 0 1 1 12 1 0 0 0
188 0 189 0 0 0 0 1 19 0 0 0 0
189 0 2 1 0 0 1 0 20 13 0 0 0
190 1 127 1 1 1 1 1 17 1 0 0 0
191 0 13 0 0 0 1 0 19 0 0 0 0
192 0 31 1 0 1 1 1 19 41 1 0 0
193 0 92 0 0 0 0 1 18 0 0 1 1
194 0 3 1 1 1 1 0 9 1 0 0 0
195 0 78 0 1 0 0 0 19 0 0 0 0
196 0 183 0 0 0 0 0 18 0 0 0 0
197 0 60 0 0 0 0 1 18 0 1 0 0
198 0 175 0 0 0 0 0 21 0 0 0 0
199 0 6 0 0 0 0 1 17 0 1 0 0
200 0 3 1 1 0 1 1 19 0 0 0 0
201 1 39 0 1 1 1 0 19 0 0 0 0
202 0 85 0 0 0 0 0 16 1 1 0 0
203 1 148 0 1 1 1 1 28 13 1 1 13
204 0 2 0 0 0 0 0 18 0 0 0 0
205 0 4 0 0 0 0 0 17 0 0 0 0
206 0 3 1 1 1 1 0 11 1 0 0 0
207 1 71 1 0 1 1 0 39 29 1 1 3
208 0 57 0 0 1 0 1 17 2 1 0 0
209 1 192 1 1 0 1 1 37 1 0 0 0
210 0 188 0 1 0 1 1 23 26 1 1 43
211 0 169 1 1 1 1 1 18 30 0 0 0
212 1 155 1 0 1 1 0 34 28 1 0 0
213 0 181 0 0 0 0 0 21 0 0 0 0
214 0 23 0 0 0 1 0 13 1 0 0 0
215 0 34 0 1 0 0 0 13 13 0 0 0
216 0 1 1 0 0 1 0 19 0 0 1 1
217 1 161 1 1 1 1 1 20 0 1 0 0
218 1 39 0 1 1 0 1 30 11 1 1 11
219 0 3 1 0 0 0 0 15 1 0 0 0
220 1 17 1 1 0 0 1 12 23 1 0 0
221 0 94 0 1 1 1 1 22 0 1 1 75
222 0 14 0 0 0 0 0 20 0 0 0 0
223 0 25 1 0 1 1 1 19 59 1 1 56
224 0 19 0 0 1 0 1 21 5 0 0 0
225 0 2 1 0 1 1 1 12 9 0 0 0
226 0 24 0 0 0 0 0 18 0 0 0 0
227 0 1 1 1 0 1 0 23 1 0 0 0
64
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
228 0 50 0 1 0 0 0 19 0 0 0 0
229 0 105 1 1 0 0 0 21 0 0 0 0
230 0 6 0 0 0 0 0 18 0 1 0 0
231 0 63 0 1 1 0 1 17 4 1 1 7
232 0 1 1 0 1 1 0 10 1 1 0 0
233 0 24 1 1 0 1 0 13 1 0 0 0
234 1 39 0 1 0 0 0 30 3 1 0 0
235 0 9 1 0 1 1 1 22 69 1 0 0
236 0 3 1 0 1 1 0 20 6 0 0 0
237 0 191 0 0 0 0 0 21 0 0 0 0
238 0 3 1 1 0 0 1 23 9 0 1 10
239 0 59 1 1 0 1 1 17 4 1 1 6
240 0 186 0 0 0 1 0 19 0 0 0 0
241 0 2 0 0 0 0 0 20 0 0 0 0
242 0 53 1 0 0 1 1 17 17 0 0 0
243 0 13 0 1 0 0 1 11 2 1 0 0
244 0 33 1 1 0 0 0 20 0 0 0 0
245 0 2 1 1 1 1 0 16 1 0 0 0
246 1 41 0 1 0 0 0 18 0 0 1 60
247 0 2 0 0 0 0 0 18 0 0 0 0
248 0 1 1 1 0 1 0 8 1 0 1 1
249 0 175 1 0 0 1 1 19 3 0 1 4
250 1 184 1 1 1 1 1 25 51 1 0 0
251 0 32 0 1 0 0 1 15 10 1 1 14
252 0 1 1 1 0 1 0 16 0 0 0 0
253 0 171 0 0 0 0 1 16 1 1 0 0
254 0 3 1 0 0 1 1 17 0 0 0 0
255 0 98 0 1 1 1 1 20 0 0 1 30
256 0 2 0 0 1 0 1 29 14 1 0 0
257 0 56 0 0 0 0 1 18 0 0 0 0
258 1 178 0 1 1 0 1 15 48 1 0 0
259 0 185 1 1 0 1 1 16 22 0 0 0
260 1 133 1 1 1 0 0 24 126 1 0 0
261 0 100 0 1 0 0 0 19 0 0 0 0
262 0 30 0 0 0 0 1 18 5 1 1 5
263 0 49 0 0 0 0 1 18 0 1 0 0
264 0 72 1 1 1 1 1 21 1 0 0 0
265 1 135 1 1 1 0 1 20 27 1 0 0
266 0 31 0 0 0 0 0 16 0 1 0 0
267 0 20 0 1 1 0 1 9 13 1 0 0
268 0 5 0 0 0 0 1 19 0 0 0 0
269 1 43 1 0 1 1 1 31 15 1 0 0
270 0 3 1 0 1 1 1 16 53 1 0 0
271 0 15 1 1 0 1 1 17 0 0 0 0
272 0 53 0 0 0 0 0 12 1 0 0 0
273 0 28 0 0 0 0 1 18 0 0 0 0
274 1 72 0 1 1 1 1 32 142 1 0 0
275 0 51 0 0 0 0 0 18 0 0 0 0
276 0 1 1 0 0 1 0 20 0 0 0 0
277 0 2 1 0 0 1 0 20 0 0 0 0
278 1 44 0 0 0 0 1 23 3 1 0 0
279 0 5 0 0 0 0 0 17 0 1 0 0
280 0 57 0 1 1 0 1 13 2 1 1 2
281 0 84 0 0 0 0 0 23 2 0 0 0
282 0 178 1 0 1 1 0 17 3 0 1 14
283 0 1 1 0 1 0 1 20 0 1 0 0
284 1 44 1 0 1 1 1 37 18 1 0 0
285 0 2 1 0 0 1 0 18 0 0 0 0
286 0 6 0 0 1 0 0 13 4 1 0 0
287 0 171 0 0 0 0 1 18 0 0 0 0
288 1 12 0 1 1 1 0 37 23 1 0 0
289 0 25 1 0 0 1 0 10 1 0 0 0
65
pt death adeq lungtx abxuse icu pulmon mdr apache vent cvc icutx iculos
290 0 2 1 1 0 1 1 7 1 0 0 0
291 0 24 0 1 0 0 0 18 0 0 0 0
292 0 2 0 0 0 0 0 16 0 0 0 0
293 0 23 1 1 0 1 0 39 19 1 0 0
294 1 13 1 1 0 1 1 19 7 0 0 0
295 0 23 0 0 1 1 0 14 3 0 0 0
296 0 52 0 1 1 0 0 18 4 1 0 0
297 0 79 0 1 0 0 1 19 0 0 0 0
298 0 55 1 0 0 1 1 17 0 0 0 0
299 0 28 0 0 0 0 1 20 0 0 0 0
300 0 10 0 1 0 0 1 20 0 0 0 0
301 0 72 1 0 1 1 1 20 1 1 1 17
302 0 3 0 0 0 0 0 13 1 0 0 0
303 0 30 0 1 0 0 1 21 1 1 1 7
304 0 77 0 0 0 0 1 21 0 0 0 0
305 1 24 1 1 1 0 1 33 19 1 0 0
306 0 1 0 0 0 1 0 15 5 0 0 0
307 0 34 0 1 1 1 1 18 0 1 0 0
308 0 25 0 0 0 0 1 20 0 0 0 0
309 0 185 0 0 0 0 1 15 1 1 0 0
310 1 47 0 0 1 1 1 27 12 1 0 0
311 0 24 0 1 0 0 0 17 0 0 0 0
312 0 52 0 1 1 0 1 15 1 1 0 0
66
Appendix III – Multivariate Logistic Regression Modelling
Block 0: Beginning Block
Classification Table(a,b)
Observed Predicted
death Percentage Correct
survivor nonsurvivor Step 0
death survivor 260 0 100.0
nonsurvivor 52 0 .0 Overall Percentage 83.3
a Constant is included in the model. b The cut value is .500 Variables in the Equation B S.E. Wald df Sig. Exp(B)
Step 0
Constant -1.609 .152 112.246 1 .000 .200
Variables not in the Equation
Score df Sig. adeqhour 15.676 1 .000 apache 75.453 1 .000 icu(1) 46.813 1 .000
Variables
abxuse(1) 10.844 1 .001
Step 0
Overall Statistics 121.275 4 .000 Block 1: Method = Forward Stepwise (Wald)
Omnibus Tests of Model Coefficients
Chi-
square df Sig. Step 68.554 1 .000 Block 68.554 1 .000
Step 1
Model 68.554 1 .000 Step 24.602 1 .000 Block 93.156 2 .000
Step 2
Model 93.156 2 .000 Step 16.469 1 .000 Block 109.625 3 .000
Step 3
Model 109.625 3 .000 Step 9.207 1 .002 Block 118.833 4 .000
Step 4
Model 118.833 4 .000
67
Model Summary
Step -2 Log
likelihood
Cox & Snell R Square
Nagelkerke R Square
1 212.596(a) .197 .332 2 187.994(b) .258 .435 3 171.525(b) .296 .499 4 162.318(b) .317 .533
a Estimation terminated at iteration number 5 because parameter estimates changed by less than .001. b Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. Hosmer and Lemeshow Test
Step Chi-
square df Sig. 1 6.527 6 .367 2 8.595 7 .283 3 5.142 8 .742 4 5.537 8 .699
Contingency Table for Hosmer and Lemeshow Test
death = survivor death = nonsurvivor Total
Observed Expected Observed Expected Step 1
1 27 27.538 1 .462 28
2 33 33.419 2 1.581 35 3 21 21.347 2 1.653 23 4 44 42.813 3 4.187 47 5 44 44.506 6 5.494 50 6 47 47.584 8 7.416 55 7 33 28.399 2 6.601 35 8 11 14.396 28 24.604 39 Step 2
1 31 31.453 1 .547 32
2 26 25.952 1 1.048 27 3 41 40.841 2 2.159 43 4 44 45.099 4 2.901 48 5 46 44.515 2 3.485 48 6 33 30.612 1 3.388 34 7 20 21.794 8 6.206 28 8 13 16.549 19 15.451 32 9
6 3.185 14 16.815 20
68
death = survivor death = nonsurvivor Total
Observed Expected Observed Expected Step 3
1 31 31.711 1 .289 32
2 32 31.327 0 .673 32 3 30 30.152 1 .848 31 4 30 29.991 1 1.009 31 5 29 29.784 2 1.216 31 6 30 29.264 1 1.736 31 7 26 27.550 5 3.450 31 8 27 24.863 4 6.137 31 9 18 18.722 13 12.278 31 10 7 6.636 24 24.364 31 Step 4
1 32 31.805 0 .195 32
2 31 31.567 1 .433 32 3 30 30.451 1 .549 31 4 31 31.093 1 .907 32 5 29 29.612 2 1.388 31 6 29 28.993 2 2.007 31 7 31 27.931 0 3.069 31 8 25 25.443 6 5.557 31 9 16 17.714 15 13.286 31 10 6 5.392 24 24.608 30
Classification Table(a)
Observed Predicted
death Percentage Correct
survivor nonsurvivor Step 1
death survivor 252 8 96.9
nonsurvivor 32 20 38.5 Overall Percentage 87.2 Step 2
death survivor 253 7 97.3
nonsurvivor 27 25 48.1 Overall Percentage 89.1 Step 3
death survivor 249 11 95.8
nonsurvivor 24 28 53.8 Overall Percentage 88.8 Step 4
death survivor 251 9 96.5
nonsurvivor 22 30 57.7 Overall Percentage 90.1
a The cut value is .500
69
Variables in the Equation
B S.E. Wald df Sig. Exp(B) 95.0% C.I.for
EXP(B) Lower Upper Step 1(a)
apache .233 .035 45.291 1 .000 1.262 1.180 1.351
Constant -6.520 .770 71.732 1 .000 .001 Step 2(b)
apache .201 .033 38.050 1 .000 1.223 1.147 1.304
icu(1) 1.893 .383 24.417 1 .000 6.639 3.134 14.068 Constant
-6.563 .753 76.035 1 .000 .001
Step 3(c)
adeqhour .012 .003 15.729 1 .000 1.012 1.006 1.018
apache .220 .035 38.831 1 .000 1.246 1.163 1.336 icu(1) 1.922 .402 22.829 1 .000 6.833 3.106 15.029 Constant -7.837 .927 71.398 1 .000 .000 Step 4(d)
adeqhour .012 .003 14.311 1 .000 1.012 1.006 1.018
apache .227 .036 39.467 1 .000 1.255 1.169 1.348 icu(1) 1.836 .413 19.781 1 .000 6.273 2.793 14.090 abxuse(1) 1.270 .438 8.408 1 .004 3.562 1.509 8.406 Constant -8.698 1.034 70.696 1 .000 .000
a Variable(s) entered on step 1: apache. b Variable(s) entered on step 2: icu. c Variable(s) entered on step 3: adeqhour. d Variable(s) entered on step 4: abxuse. Variables not in the Equation
Score df Sig. adeqhour 19.131 1 .000 icu(1) 28.561 1 .000
Variables
abxuse(1) 11.841 1 .001
Step 1
Overall Statistics 53.232 3 .000 adeqhour 17.651 1 .000 Variables abxuse(1) 10.480 1 .001
Step 2
Overall Statistics 25.217 2 .000 Variables abxuse(1) 9.042 1 .003 Step
3 Overall Statistics 9.042 1 .003