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Validation of a bacteremia prediction model
J M Mylotte1, M A Pisano, S Ram
1Department of Medicine, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, USA.
Infection Control and Hospital Epidemiology
|April 1, 1995
Summary
A bacteremia prediction model effectively identified high-risk patients in a validation study, though overall accuracy decreased compared to its initial development. This tool aids physicians in antimicrobial therapy decisions and hospital quality assessment.
Area of Science:
- Clinical Medicine
- Infectious Diseases
- Epidemiology
Background:
- Accurate prediction of bacteremia (bacteria in the bloodstream) is crucial for effective patient management and antimicrobial stewardship.
- A previously developed model aimed to predict bacteremia in hospitalized patients, requiring validation in a new cohort.
Purpose of the Study:
- To validate a previously published model for predicting bacteremia in hospitalized patients.
- To compare the model's predictability in a new validation cohort against its original derivation cohort.
Main Methods:
- A prospective validation cohort of 342 patients (559 blood culture episodes) was established at an urban, university-affiliated hospital.
- A published bacteremia prediction model, based on seven predictors, was applied to the validation cohort.
- Predictability was assessed by comparing results to the derivation cohort using receiver operator characteristic (ROC) curve analysis.
Main Results:
- The model identified low-risk (3%) and high-risk (17%) bacteremia episodes in the validation cohort, comparable to the derivation cohort (1% and 16%).
- However, ROC analysis indicated that the overall predictability of the model was lower in the validation cohort than in the derivation cohort.
Conclusions:
- Despite a decrease in overall performance, the bacteremia prediction model remains valuable for distinguishing patients at very low or very high risk for bacteremia.
- This capability can assist clinicians in making informed decisions regarding empiric antimicrobial therapy.
- The model may also serve as a tool for hospital epidemiologists to monitor and assess the quality of care.