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Evaluating Clinical Prediction Rules for Bacteremia Detection in the Emergency Department: A Retrospective Review
Lincoln McNab1, Rachelle Lee1, Angela L Chiew2
1Prince of Wales Clinical School, University of New South Wales (UNSW) Medicine, Sydney, NSW, Australia.
Background:
Bacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia.
Objective:
To compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department.
Methods:
Retrospective analysis of medical records of patients who had a blood culture performed in a tertiary hospital emergency department in 2020 (12 months). Positive blood cultures were compared with randomly selected negative blood cultures (1:4 ratio). Blood cultures were analyzed per patient presentation. Clinical data from patient presentations were extracted and appraised against the modified-Shapiro (mShapiro) rule and systemic inflammatory response syndrome (SIRS) criteria to calculate diagnostic accuracy to detect bacteremia.
Results:
During the study period, 3870 blood cultures were taken from 2921 patients: 476 (12.3%) cultures were positive for bacterial growth, from 421 individual patient presentations (10 excluded as incomplete data). Of included patients, 338 were true positives and 73 contaminates, these were compared with 1446 patients with negative blood culture presentations. Evaluating mShapiro's rule and SIRS criteria to detect bacteremia vs. no bacteremia (negative + contaminated cultures) had a sensitivity of 94.4% (95% confidence interval [CI] 91.4-96.4%) and 84.9% (95% CI 80.7-88.3%), respectively, and a specificity of 37.9% (95% CI 35.5-40.1%) and 33.8% (95% CI 31.5-36.3%), respectively. Both had a high negative predictive value for bacteremia of 96.8% (95% CI 95.1-98.0) and 91.0% (95% CI 88.3-93.1) for mShapiro's rule and SIRS criteria, respectively.
Conclusions:
In this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.
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