 |
|
Health and Economic Outcomes of Vancomycin-Resistant Enterococci
Yehuda Carmeli, MD, MPH;
George Eliopoulos, MD;
Essy Mozaffari, PharmD, MPH;
Matthew Samore, MD
Arch Intern Med. 2002;162:2223-2228.
ABSTRACT
| |
Background The health and economic impact of vancomycin-resistant enterococci has
not been quantified.
Methods A retrospective matched cohort study was conducted comparing the outcomes
of patients with vancomycin-resistant enterococci (cases) with those of control
subjects matched for length of hospital stay until inclusion in the cohort,
hospital location, and calendar date. The propensity to be a vancomycin-resistant
enterococci case was modeled based on patient characteristics, and included
in multivariable models to adjust for confounding. Analyses included the following:
(1) conditional logistic regression for mortality, surgery, intensive care
unit admission, and discharge to long-term care; (2) linear regression for
the logarithm of cost; and (3) accelerated failure time model for length of
stay.
Results A total of 233 cases were compared with 647 controls. Groups were similar
in age (mean, 62 years), sex (female, 47%), and length of stay before inclusion
in the cohort (mean, 8.1 days), but differed in primary diagnosis and comorbidities,
past infection or colonization with methicillin sodium–resistant Staphylococcus aureus or Clostridium
difficile, and treatment with cephalosporins or metronidazole. These
variables were included in the propensity score, which had good to excellent
prediction. Outcomes for cases vs controls and adjusted risks (relative risks
[RRs]) were as follows: (1) case fatality rate, 17% vs 6% (RR, 2.13; P = .04); (2) length of stay after inclusion in the cohort,
15.1 vs 8.5 days (RR, 1.73; P<.001); (3) hospital
costs, $52 449 vs $31 915 (RR, 1.40; P<.001);
(4) surgery after inclusion in the cohort, 18% vs 10% (RR, 2.74; P = .001); (5) intensive care unit admission after inclusion in the
cohort, 25% vs 14% (RR, 3.47; P<.001); and (6)
transfer to an institution, 51% vs 35% (RR, 2.01; P =
.001).
Conclusion Compared with a matched hospital population, a population with vancomycin-resistant
enterococci was associated with severe adverse outcomes: increased mortality,
morbidity, and costs.
INTRODUCTION
ANTIBIOTIC RESISTANCE is a recognized clinical problem and a major public
health threat. Infections caused by resistant bacteria are believed to result
in severe adverse outcomes (increased mortality, morbidity, and medical care
costs).1-4 The
reason that antibiotic resistance leads to adverse outcomes is presumably
because of an increased likelihood that ineffective or suboptimal antibiotic
therapy will be given. Moreover, in some organisms, the development of resistance
to all available antibiotics may preclude the effectiveness of any antibiotic
regimen.
Vancomycin-resistant enterococci are considered ultimate pathogens,
organisms toward which no effective treatment is available (until recently).
First isolated in 1987,5-6 vancomycin-resistant
enterococci have rapidly become established as important nosocomial pathogens
in the United States. In intensive care units (ICUs) reporting to the National
Nosocomial Infections Surveillance System in 1999, vancomycin-resistant enterococci
were responsible for approximately one quarter of all enterococcal infections,
an increase of 43% compared with 1994 to 1998 data. Similar trends were also
observed in other hospital wards.7
Studies8-11 found
that bacteremia due to vancomycin-resistant enterococci was associated with
increased mortality. Edmond et al8 estimated
that the attributable mortality of those with bacteremia due to vancomycin-resistant
enterococci is 37%. Stosor et al9 also found
that patients with bacteremia due to vancomycin-resistant enterococci had
an average additional cost of hospitalization of $27 000. Primary bacteremia
due to vancomycin-resistant enterococci composes fewer than 10% of all vancomycin-resistant
enterococci infections. This study quantifies the overall, direct, in-hospital
clinical, and economic impact of vancomycin-resistant enterococci in a cohort
of patients with vancomycin-resistant enterococci compared with a matched
hospital population.
METHODS
SETTING, DATA COLLECTION, AND MICROBIOLOGICAL STUDIES
Beth Israel Deaconess Medical Center, West Campus, is a 320-bed urban
tertiary care teaching hospital. It has 24 ICU beds, and there are approximately
12 000 patient admissions per year.
Data were extracted from patients' medical records and administrative,
accounting, and laboratory computerized databases and compiled into a single
data set using a relational database management system (Access; Microsoft
Corp, Redmond, Wash). This informatics system has been described elsewhere.12
Enterococci had been identified from clinical specimens submitted to
the microbiology laboratory using a gram-positive identification panel (Dade
International Inc, West Sacramento, Calif). Enterococci were screened for
vancomycin resistance by plating on brain-heart infusion agar with vancomycin,
6 µg/mL. Vancomycin resistance was confirmed by formal minimum inhibitory
concentration testing using a microdilution broth system (MicroScan; Dade
International Inc). Enterococcus faecium and Enterococcus faecalis isolates with vancomycin minimum
inhibitory concentrations of 8 µg/mL or greater were classified as vancomycin-resistant
enterococci according to the National Committee for Clinical Laboratory Standards
guidelines.
DEFINITIONS AND STUDY DESIGN
The study was designed as a matched cohort study. All inpatients from
whom vancomycin-resistant enterococci were first isolated from a clinical
culture in our hospital between October 1, 1993, and December 31, 1997, were
enrolled as vancomycin-resistant enterococci cases. Matching of control subjects
was done based on 3 variables: hospital ward, calendar date (within 7 days),
and duration of hospital stay at the time of matching (up to 3 days' difference
if no exact match was available). Up to 3 appropriately matched control patients
who were not positive for vancomycin-resistant enterococci (ie, specimens
from the patient were cultured and no vancomycin-resistant enterococci were
isolated or specimens from the patients were never cultured) were randomly
selected for each case. A list of all possible controls was created, each
was assigned a random number, and then the 3 highest random numbers were chosen
(without replacement).
Five outcomes were examined: mortality, length of hospital stay (LOS),
total hospital costs, admission to an ICU, and need for surgery (all assessed
after inclusion in the cohort) or discharge to an institution (rehabilitation,
nursing home, or long-term care facility).
To further control for confounding, we used a propensity score for being
a vancomycin-resistant enterococci case (described later), and later we included
the propensity score along with other confounding variables in the multivariate
analysis examining each of the outcomes. We explored for confounding of the
following variables: demographic characteristics, admitting diagnosis, comorbidities
(grouped into 8 major groups and by the Charlson chronic comorbidity score13), being transferred from another institution, being
admitted to an ICU (before inclusion in the cohort) and number of days in
the ICU, undergoing a major surgical procedure, being infected with resistant
organisms (Clostridium difficile or methicillin-resistant Staphylococcus aureus), and antecedent treatment with different
antibiotic agents.
STATISTICAL ANALYSES
Statistics were performed on Stata software (Stata Corp, College Station,
Tex). A matched (conditional) logistic regression model was used to construct
an explanatory model for the probability of being a vancomycin-resistant enterococci
case.14 By using the prediction probabilities
of this model, a propensity score was constructed.15-17 All
the variables were candidates for the model, and were selected in a stepwise
manner, with an enrollment criterion of P<.20
and a criterion to stay in the model of P<.05.
Variables that were not retained in the model by this procedure were then
tested for confounding by adding them one at a time to the model and examining
their effects on the  coefficients. Variables that caused substantial
confounding (change in the coefficient of >10%) were included in the
final model. In addition to examining statistical significance and confounding,
effect modification between variables was evaluated by testing appropriate
interaction terms for statistical significance. The ability of the propensity
score to adjust for important covariates of treatment was evaluated by testing
for differences in the covariates within quintiles of propensity.
Each outcome was later examined independently, using multivariate analysis.
All the variables were candidates for these models with the procedure described
previously, while forcing the vancomycin-resistant enterococci status and
the propensity score into the model. Mortality, admission to an ICU, and need
for surgery (all after inclusion in the cohort) or discharge to a rehabilitation
or long-term care facility were examined using a matched (conditional) logistic
regression model. We estimated the adjusted population-attributable fraction
from the logistic regression model,18 and used
these estimates to calculate the adjusted attributable risk for the exposed.
Survivorship curves of hospital LOS were examined and were appropriate for
the accelerated failure time model (Weibull). Thus, an accelerated failure
time model (Weibull, stratified to account for matching) was used to examine
LOS. Time 0 was considered to be the date of enrollment into the cohort (the
day of vancomycin-resistant enterococci isolation for cases and the matching
day for controls). Patients were censored at death. The Weibull model was
parameterized in the form of logarithm time, so that the coefficients that
underwent exponentiation could be interpreted as multiplicative effects (MEs)
on LOS. This model was also used to estimate excess number of days in the
hospital, based on the value of the ME. Hospital costs were examined using
logarithm-transformed hospital charges (to achieve a normal distribution)
and analyzed with linear regression models, with an absorbed variable to account
for matching. Coefficients from the model underwent exponentiation because
of the logarithm transformation of the dependent variable and, thus, were
also interpreted as MEs. All statistical tests were 2-tailed. P .05 was considered significant.
RESULTS
During the 51 months studied, 251 vancomycin-resistant enterococci cases
were identified. No appropriate control patient could be matched for 18 cases.
Thus, the study cohort included 880 patients: 233 cases and 647 matched controls.
The primary site of isolation was a wound in 42%, the urinary tract in 31%,
an intra-abdominal infection in 17%, and a primary bloodstream infection in
9% (percentages do not total 100 because of rounding). The average age of
the patients was 62 years, and 46% were female. Patients were hospitalized
for an average of 8.1 days before enrollment into the study. Many of the cohort
patients had chronic underlying illnesses and were severely ill, as expressed
by a high mean Charlson score of 2.9; 37% were transferred from another institution,
32% underwent a major surgical procedure, and 27% were admitted to an ICU.
Many patients (33%) had diabetes mellitus, a characteristic of the overall
hospital population. Characteristics of cases and controls are summarized
in Table 1.
|
|
|
|
Table 1. Characteristics of the Case and Control Patients*
|
|
|
A multivariate logistic regression model was developed to calculate
the propensity score,14-16 a
score that predicts the patient probability of being a vancomycin-resistant
enterococci case. After matching for hospital location, calendar date, and
duration of hospitalization, the following variables predicted being a vancomycin-resistant
enterococci case (propensity score): (1) main admitting diagnosis of cardiovascular
disease (0.44) or infectious condition (2.9); (2) comorbid conditions of diabetes
mellitus (2.1), organ transplantation (2.6), and hepatobiliary disease (2.9);
(3) infection or colonization with methicillin-resistant S aureus (3.5) or C difficile (2.0) within
the past year; or (4) undergoing treatment with a third-generation cephalosporin
(2.8) or metronidazole (2.1). The propensity score had an area under the receiver
operating characteristic curve of 80%, indicating excellent discrimination
between vancomycin-resistant enterococci cases and controls.
MORTALITY
Of the 880 cohort patients, 81 died in the hospital: 39 of the 233 vancomycin-resistant
enterococci case patients and 42 of the 647 control patients (case fatality
rate, 17% vs 6%; relative risk [RR], 3.49; P<.001;
attributable mortality, 10%). In a matched logistic regression multivariable
model constructed to control for confounding, being a vancomycin-resistant
enterococci case was significantly associated with mortality (adjusted RR,
2.13; P = .04; adjusted attributable mortality, 6%).
Results of adjusted analyses, and corresponding adjusted attributable risks
(attributable fraction in the exposed), are displayed in Table 2, and subgroup analyses for mortality according to initial
site of vancomycin-resistant enterococci isolation are presented in Table 3.
|
|
|
|
Table 2. Outcomes and Adjusted Analyses for Case Patients vs Matched
Control Patients*
|
|
|
|
|
|
|
Table 3. Subgroup Adjusted Analyses of Outcomes for Case Patients With
Vancomycin-Resistant Enterococci vs Matched Control Patients According to
the Initial Site of Vancomycin-Resistant Enterococci*
|
|
|
HOSPITAL LOS
The median number of days between inclusion in the cohort and discharge
from the hospital was 9 for vancomycin-resistant enterococci cases (mean,
15.1 days; range, 1-107 days) and 5 for controls (mean, 8.5 days; range, 1-116
days).
The ME on the duration of stay following inclusion in the cohort (vancomycin-resistant
enterococci isolation day for cases and matching day for controls) was calculated
using an accelerated failure time model. Results of the analysis are displayed
in Table 2. A longer duration
of stay was observed in the vancomycin-resistant enterococci cases (ME, 2.06; P<.001). Similar results were seen in the multivariable
model developed to control for confounding (ME, 1.73; P<.001). We estimated that being a vancomycin-resistant enterococci
case was associated with an average adjusted increase of 6.2 days in hospital
LOS. Subgroup analyses for LOS according to initial site of vancomycin-resistant
enterococci isolation are presented in Table 3.
SURGERY
Forty-one of the 233 vancomycin-resistant enterococci case patients
and 66 of the 647 control patients underwent a major surgical procedure after
being included in the cohort (18% vs 10%; RR, 1.96; P =
.004; attributable need for surgery, 7%). After adjusting for confounding
using the propensity score and multivariate modeling, being a vancomycin-resistant
enterococci case was associated with a significantly higher likelihood of
undergoing major surgery after being included in the cohort (RR, 2.74; P = .001; adjusted attributable risk, 10%). The subgroup
analyses for surgery according to initial site of vancomycin-resistant enterococci
isolation are presented in Table 3.
ADMISSION TO THE ICU
Fifty-nine of the 233 vancomycin-resistant enterococci case patients
and 92 of the 647 control patients required ICU care for at least 24 hours
after being included in the cohort (25% vs 14%; RR, 3.1; P<.001; attributable risk for ICU admission, 11%). After adjusting
for confounding using multivariate modeling (the propensity score and admission
to the ICU before inclusion in the cohort), being a vancomycin-resistant enterococci
case was associated with a significantly higher likelihood for ICU admission
at some time after being included in the cohort (adjusted RR, 3.47; P<.001; adjusted attributable risk, 11%). Subgroup analyses
for admission to the ICU according to initial site of vancomycin-resistant
enterococci isolation are summarized in Table 3.
DISCHARGE TO LONG-TERM CARE
Of the 799 surviving cohort patients, 312 (39%) were discharged to long-term
care. Among the vancomycin-resistant enterococci cases, 99 were discharged
to long-term care compared with 213 controls (51% vs 35%; RR, 1.98; P<.001). After adjusting for confounding (propensity
and being transferred from an institution), being a vancomycin-resistant enterococci
case was associated with a higher rate of being discharged to long-term care
(RR, 2.01; P = .001; attributable risk, 16%). Subgroup
analyses for discharge to long-term care according to initial site of vancomycin-resistant
enterococci isolation are presented in Table 3.
COMMENT
Antimicrobial resistance is a growing public health threat. Infections
due to antibiotic-resistant pathogens are believed to be associated with greater
mortality, morbidity, and costs than infections due to susceptible organisms.
The national costs of antimicrobial resistance for the United States have
been estimated between $100 million and $30 billion annually. The Office of
Technology Assessment of Congress has estimated the minimal hospital cost
associated with nosocomial infections caused by antibiotic-resistant bacteria
to be $1.3 billion per year (in 1992).3 However,
despite the existence of these global estimates and the growing alarm about
the problem of resistance, few studies that quantitatively examine the health
and cost impact of resistant organisms have been performed.
Studies of the impact of antibiotic resistance must rely on observational
data rather than randomized trials. Thus, to estimate the effect of resistance,
patients with resistant organisms are compared with patients without resistant
organisms for outcome measures such as cost and LOS. The problem with this
simple comparison is that sicker patients with long LOSs are more likely to
acquire resistant organisms, leading to substantial confounding bias. We addressed
this problem by applying study design and analytic methods to control as much
as possible the other factors besides antibiotic resistance that contributed
to adverse outcomes. Patients without vancomycin-resistant enterococci (controls)
were matched to patients with vancomycin-resistant enterococci (cases) so
that all controls were still hospitalized on the day when the vancomycin-resistant
enterococci were detected in cases. Primary diagnoses and comorbidities that
distinguished vancomycin-resistant enterococci cases from their matched controls
were accounted for by the propensity score method. Additional confounding
variables were identified and included in the final multivariable models.
We believe that the careful attention to the problem of confounding in our
analysis bolsters the validity of the conclusions. Another strength of this
study is that multiple outcome end points were examined, providing a more
complete picture of the impact of vancomycin-resistant enterococci.
Our major findings were that vancomycin-resistant enterococci culture
positivity was associated with the following: (1) 2-fold increased odds of
mortality, (2) 2.7-fold increased odds of a major surgical procedure, (3)
3.5-fold increased odds of admission to the ICU, (4) a 1.7-fold increase in
hospital LOS, (5) a 1.4-fold increase in cost of hospitalization, and (6)
2-fold increased odds of discharge to a long-term care facility. The latter
finding suggests that the impact of vancomycin-resistant enterococci on costs
likely extends beyond the period of hospitalization.
The corresponding estimates of vancomycin-resistant enterococci–attributable
effects, within the vancomycin-resistant enterococci case population, were
as follows: mortality, 6%; major surgical procedure, 10%; ICU admission, 11%;
and discharge to long-term care, 16%. Cases had an average extra cost of $12 766,
attributable to vancomycin-resistant enterococci. This translates to 15 cases
of in-hospital death, 22 major operations, 26 ICU admissions, 1445 additional
hospitalization days, and excess costs of $2 974 478, during the
study period. These figures represent estimates of the impact of vancomycin-resistant
enterococci after adjustment for confounding; however, the limitations of
drawing causal inferences from a single observational study need to be emphasized.
As with all studies of this nature, there may have been residual confounding
bias from unmeasured variables. Another source of bias may come from classification
bias, because some of the control patients may be undetected vancomycin-resistant
enterococci carriers. This type of misclassification would have led to an
underestimate of the true impact of vancomycin-resistant enterococci.
Our estimates of the attributable mortality and cost related to bacteremia
due to vancomycin-resistant enterococci (25% and $13 537, respectively)
are somewhat lower than those estimated by Edmond et al8 and
Stosor et al9 (37% and $27 000, respectively).
This may indicate that our methods of adjusting for confounding yielded more
conservative effect sizes. Differences between study results likely also relate
to variation between institutions and patient populations. In the study described
herein, patients with vancomycin-resistant enterococci were compared with
patients without vancomycin-resistant enterococci, whereas in the study by
Stosor et al, patients with vancomycin-resistant enterococci were compared
with patients with bacteremia due to vancomycin-susceptible enterococci. However,
differences in choice of control group probably do not account fully for differences
in attributable costs. A study of outcomes associated with wound infections
due to vancomycin-resistant enterococci compared with wound infections due
to vancomycin-susceptible enterococci has been conducted (Y.C., E.M., and
M.S., unpublished data, 2002). The results of that analysis indicated effects
that were similar in magnitude to those reported herein.
The many vancomycin-resistant enterococci cases examined made it possible
to perform subgroup analyses and identify variations in effect, according
to the initial source of vancomycin-resistant enterococci. A relationship
with mortality was seen when the blood, urinary tract, or abdomen was the
initial source of vancomycin-resistant enterococci but not when the source
was a wound. Most of the wound infections were limb infections. Thus, the
nidus of infection could be removed by amputation or extensive debridement.
Indeed, when the wound, but not the urinary tract or blood, was the source
of vancomycin-resistant enterococci, patients had an increased rate of surgery.
Intra-abdominal infections were associated with increased mortality and an
increased rate of surgery. It is reasonable to assume that the mortality rate
would have been higher had operations not been performed. This is in accordance
with previous observations19 on multidrug-resistant Pseudomonas aeruginosa. The increased cost of care is related
primarily to a longer hospital stay, but the increased rate of ICU admissions
and major surgical procedures performed undoubtedly further contributed to
the increased cost. Indeed, bloodstream, wound, and intra-abdominal sites,
all associated with an increased ICU admission rate or access in surgical
procedures, were associated with a higher vancomycin-resistant enterococci–attributable
cost than the urinary tract, which was associated only with an increased LOS.
Our cost estimates represent most closely the direct hospital perspective.
Effects on third-party payers, and other societal perspectives, extend beyond
hospitalization and are underestimated by this study. In addition, we believe
that the hospital perspective is also underestimated because we did not account
for extra costs to the institution at large caring for patients with vancomycin-resistant
enterococci, such as surveillance cultures, isolation supplies, and loss of
beds due to the need to isolate a patient.
The results of this study provide a strong rationale for the development
of effective interventions to minimize the impact of vancomycin-resistant
enterococci. These efforts should be directed toward limiting the spread of
vancomycin-resistant enterococci and toward better treatment of infections
due to vancomycin-resistant enterococci.
AUTHOR INFORMATION
Accepted for publication March 20, 2002.
Corresponding author and reprints: Yehuda Carmeli, MD, MPH, Division
of Infectious Diseases, Tel Aviv Sourasky Medical Center, 6 Weizman St, Tel
Aviv 62749, Israel (e-mail: ycarmeli{at}caregroup.harvard.edu).
From the Divisions of Infectious Diseases, Beth Israel Deaconess Medical
Center and Harvard Medical School, Boston, Mass (Drs Carmeli and Eliopoulos),
and Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (Dr Carmeli); Pharmacia
Corp, Kalamazoo, Mich (Dr Mozaffari); and Division of Epidemiology, University
Hospital, Salt Lake City, Utah (Dr Samore). Dr Carmeli, his laboratory, and
studies that he has conducted during the past 4 years received grants, honoraria,
travel support, and other forms of financial support from the following companies:
Astra-Zeneca, London, England; Bayer Corp, West Haven, Conn; Biomedicum Ltd,
Jerusalem, Israel; Bristol-Myers Squibb, Wallingford, Conn; Eli Lilly, Indianapolis,
Ind; Merck & Co, Inc, Whitehouse Station, NJ; Neopharm Ltd, Petach Tikva,
Israel; Pharmacia Corp, Peapack, NJ; Roche, Basel, Switzerland; SmithKline
Beecham Pharmaceuticals, Philadelphia, Pa; and XTL Pharmaceuticals Ltd, Rehovot,
Israel.
REFERENCES
| |
1. Acar JF. Consequences of bacterial resistance to antibiotics in medical practice. Clin Infect Dis. 1997;24(suppl):S17-S18.
2. Cohen ML. Epidemiology of drug resistance: implications for a post-antimicrobial
era. Science. 1992;257:1050-1055.
FREE FULL TEXT
3. Office of Technology Assessment, US Congress. Impacts of Antibiotic-Resistant Bacteria, OTA-H-629. Washington, DC: US Government Printing Office; 1995.
4. Holemberg SD, Solomon SL, Blake PA. Health and economic impacts of antimicrobial resistance. Rev Infect Dis. 1987;9:1065-1078.
ISI
| PUBMED
5. Leclercq R, Derlot E, Duval J, Courvalin P. Plasmid-mediated resistance to vancomycin and teicoplanin in Enterococcus faecium. N Engl J Med. 1988;319:157-161.
ISI
| PUBMED
6. Uttley AHC, Collins CH, Naidoo J, et al. Vancomycin-resistant enterococci. Lancet. 1988;1:57-58.
FULL TEXT
| PUBMED
7. Public Health Service, US Department of Health and Human Services. NNIS Semiannual Report. Available at: http://www.cdc.gov/ncidod/hip/NNIS/ar_surv99.pdf. Accessed July 5, 2002.
8. Edmond MB, Ober JF, Dawson JD, Weinbaum DL, Wenzel RP. Vancomycin-resistant enterococcal bacteremia: natural history and attributable
mortality. Clin Infect Dis. 1996;23:1234-1239.
ISI
| PUBMED
9. Stosor V, Peterson LR, Postelnick M, Noskin GA. Enterococcus faecium bacteremia: does vancomycin
resistance make a difference? Arch Intern Med. 1998;158:522-527.
FREE FULL TEXT
10. Linden PK, Pasculle AW, Manez R, et al. Differences in outcomes for patients with bacteremia due to vancomycin-resistant Enterococcus faecium or vancomycin-susceptible E faecium. Clin Infect Dis. 1996;22:663-670.
ISI
| PUBMED
11. Bhavnani SM, Drake JA, Forrest A, et al. A nationwide, multicenter, case-control study comparing risk factors,
treatment, and outcome for vancomycin-resistant and -susceptible enterococcal
bacteremia. Diagn Microbiol Infect Dis. 2000;36:145-158.
FULL TEXT
|
ISI
| PUBMED
12. Samore M, Lichtenberg D, Sauberman L, Kawachi C, Carmeli Y. A clinical data repository enhances hospital infection control. Proc AMIA Annu Fall Symp. 1997;:56-60.
13. Charlson ME, Pompei P, Ales KL, McKenzie CR. A new method of classifying prognostic comorbidity in longitudinal
studies: development and validation. J Chronic Dis. 1987;40:373-383.
FULL TEXT
|
ISI
| PUBMED
14. Carmeli Y, Eliopoulos GM, Samore MH. Antecedent treatment with various antibiotic agents as a risk factor
for vancomycin-resistant enterococcus. Emerg Infect Dis. 2002;8:802-807.
ISI
| PUBMED
15. Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for
causal effects. Biometrika. 1983;70:41-55.
FREE FULL TEXT
16. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127:757-763.
FREE FULL TEXT
17. Connors AF Jr, Speroff T, Dawson NV, et al for the SUPPORT Investigators. The effectiveness of right heart catheterization in the initial care
of critically ill patients. JAMA. 1996;276:889-897.
ABSTRACT
18. Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic
models. Biometrics. 1993;49:865-872.
FULL TEXT
|
ISI
| PUBMED
19. Harris A, Torres-Vierra C, Venkataraman L, DeGirolami P, Samore M, Carmeli Y. Epidemiology and clinical outcomes of patients with multiresistant Pseudomonas aeruginosa. Clin Infect Dis. 1999;28:1128-1133.
FULL TEXT
|
ISI
| PUBMED
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES
Patterns of Antimicrobial Use Among Nursing Home Residents With Advanced Dementia
D'Agata and Mitchell
Arch Intern Med 2008;168:357-362.
ABSTRACT
| FULL TEXT
|
