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.