Dogma without Data: A Clinical Decision-Making Tool for Postoperative Blood Cultures

Abstract

Bacteremia is a worrisome postoperative complication and blood cultures (BCx) are often nondiagnostic. We previously reported a 4 per cent overall yield of positive cultures in postoperative patients. To reduce unnecessary testing, we present a predictive model to identify patients in whom growth of pathogens is unlikely and provide a clinical decision-making guide. Retrospective analysis of nonpregnant patients ≥18 years who had BCx within 10 days postoperatively was performed. Generalized linear mixed models identified clinical predictors of high- and low-yield cultures. A clinical algorithm was created using significant predictors, and positive predictive value, negative predictive value, sensitivity, and specificity calculated. Among 1759 BCx, hypotension, maximum temperature ≥101.5 °F within 24 hours of culture, and culture collected after postoperative day (POD) two were statistically significant predictors of positive cultures. Forty nine per cent of BCx were sent ≤ POD 2, and <1 per cent of these were positive. When all three criteria were met, the probability of a positive culture increased to 17 per cent. When absent, the probability of a negative culture was 99 per cent. When applied to the initial data set, the model resulted in 85 per cent reduction of cultures with 9 per cent yield of positive cultures. Drawing BCx based on a single predictor is inadequate. Reducing the number of cultures reflexively ordered within the first two POD could significantly reduce the number of unnecessary BCx. Several clinical features identified patients most likely to have positive BCx within the first 10 POD and could reduce unnecessary BCx. This model should be validated in an independent, prospective cohort.