Objective: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record (EHR).Design: Retrospective analysis of a large, multicenter inpatient data.Setting: Two academic tertiary medical centers between the years 2007 and 2018.
Subjects:All hospitalized patients who received a blood culture during hospitalization.
Intervention(s):The dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine (GBM) models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%.
Measurements and MainResults: There were 252,569 blood culture days -defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The GBM model for bacteremia had significantly higher AUC (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p<0.001). The model identified a high-risk group with over 30 times the incidence of bacteremia in the low-risk group (27.4 vs 0.9%, p<0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The GBM model for fungemia had high discrimination (AUC 0.88 [95% CI 0.86-0.90]). The highrisk fungemia group had 252 fungemic cultures compared to one fungemic culture in the low-risk
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