A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia - The SABIER score.

ABSTRACT

BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among Staphylococcus aureus bacteraemia (SAB) patients to guide clinical management. The objective of this study is to develop a novel risk score 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 of each feature in predicting SA-IE outcome. The data was divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROC) were determined. RESULTS: We identified 15,741 SAB patients, among them 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 being community-onset. CONCLUSION: We developed a novel risk score with good performance as compared to existing scores and can be used at the time of SAB and prior to subjective clinical judgment.