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

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.
Competing Interests: Potential conflicts of interest . All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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