Abstract
Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.
Highlights
Bacteremia is a life-threatening disease that can progress to sepsis and septic shock, resulting in death [1,2]
A total of 38,752 bacteremia episodes were identified from 622,771 blood culture episodes
Two types of ensemble model—XGboost (Gbtree) and XGboost (DART)—were used for the additional analysis; the area under the receiver operating characteristic curve (AUROC) was non-inferior compared with other machine learning methods
Summary
Bacteremia is a life-threatening disease that can progress to sepsis and septic shock, resulting in death [1,2]. It is essential to identify bacteremia early and be able to predict its progression. Intervention with appropriate antibiotics is important for patients with bacteremia [3,4,5], and differentiation of bacterial and viral infections is essential to prevent antibiotic abuse. By reducing unnecessary exposure to antibiotics in viral infections, the occurrence of antibiotic-related adverse reactions and the emergence of multidrug-resistant organisms can be reduced [6,7]. The prediction model for acute infectious diseases is still limited, the ripple effect on public health, such as reduction of infectious-disease-related deaths and appropriate use of antibiotics, will be very inspiring
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