A Machine Learning–Based Risk Score for Prediction of Infective Endocarditis Among Patients With Staphylococcus aureus Bacteremia—The SABIER Score

Abstract Background Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel risk score that is independent of subjective clinical judgment and can be used early, at the time of blood culture positivity. Methods We conducted a retrospective big data analysis from territory-wide electronic data and included hospitalized patients with SAB between 2009 and 2019. We applied a random forest risk scoring model to select variables from an array of parameters, according to the statistical importance in predicting SA-IE outcome. The data were divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROCs) were determined. Results We identified 15 741 SAB patients, among them 658 (4.18%) had SA-IE. The AUCROC was 0.74 (95%CI 0.70–0.76), with a negative predictive value of 0.980 (95%CI 0.977–0.983). The four most discriminatory features were age, history of infective endocarditis, valvular heart disease, and community onset. Conclusions We developed a novel risk score with performance comparable with existing scores, which can be used at the time of SAB and prior to subjective clinical judgment. We developed a random forest risk score based on an 11-year territory-wide electronic health record to stratify the risk of infective endocarditis in patients with Staphylococcus aureus bacteremia at the time of blood culture positivity.