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Validation of a Bacteremia Prediction Model

Published online by Cambridge University Press:  02 January 2015

Joseph M. Mylotte ,
Mario A. Pisano ,
Sanjay Ram ,
Sharlene Nakasato  and
Denise Rotella
Joseph M. Mylotte*
Affiliation:
Departments of Medicine, Buffalo, New York Microbiology, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York
Mario A. Pisano
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York
Sanjay Ram
Affiliation:
Departments of Medicine, Buffalo, New York
Sharlene Nakasato
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York
Denise Rotella
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York Department of Pharmacy, School of Pharmacy, State University of New York at Buffalo, Buffalo, New York
*
Infectious Diseases, Erie County Medical Center, 462 Grider St., Buffalo, NY 14215
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Abstract

Objective:

To validate a previously published model for predicting bacteremia in hospitalized patients.

Design:

Application of a published bacteremia prediction model to a prospective validation cohort of patients and comparison of its predictability to that found in the derivation cohort.

Setting:

Urban, university-affiliated, 550-bed public hospital.

Patients:

The validation cohort consisted of 342 patients with 559 blood culture episodes between October 14, 1992, and December 5, 1992. Each blood culture episode was scored based on the presence or absence of seven predictors of bacteremia and the findings compared with published results (derivation cohort).

Interventions:

None.

Results:

Application of the bacteremia prediction model to the validation cohort identified episodes with a low risk (3%) and a high risk (17%) for true bacteremia, similar to the findings in the derivation cohort (1% and 16%, respectively). Comparison of the predictions of the model in the two cohorts by receiver operator characteristic curve analysis revealed that the overall predictability of the model in the validation cohort was not as good as in the derivation cohort.

Conclusions:

Although the bacteremia prediction model did not perform as well overall in the validation cohort, the model still was able to clearly define two extreme groups: those with a low risk and those with a high risk for true bacteremia. This predictive capability may aid physicians in prescribing empiric antimicrobial therapy and also may be useful to hospital epidemiologists in assessing quality of care

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 16 , Issue 4 , April 1995 , pp. 203 - 209
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1995

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