A machine learning-based prediction model for 30-day mortality in patients with infective endocarditis

Infective endocarditis (IE) is a severe cardiac infection disease. This study aimed to explore 30-day mortality risk factors in patients with IE, evaluate machine learning (ML) models, and establish an interpretable ML model. A retrospective study including 391 IE patients (January 2017 and December 2024) was conducted. Feature selection was executed using the least absolute shrinkage and selection operator (LASSO). Eleven machine learning algorithms were used to construct prediction models. The area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score and calibration curve analysis were used to evaluate performance. The Shapley Additive exPlanations (SHAP) method was used to model interpretability. Sixteen independent predictors were identified. Random Forest (RF) performed best, with an AUC of 1.00 (development) and 0.819 (validation). SHAP analysis showed that the top eight important indicators were: lactate dehydrogenase to lymphocyte percentage ratio (LLPR), surge, C-reactive protein (CRP), activated partial thromboplastin time (APTT), urea nitrogen (Urea), platelets (PLT), prothrombin time-international normalized ratio (PT-INR), and triglycerides (TG). Using these top eight features, the RF model achieved its highest performance (development AUC = 1.00, validation AUC = 0.813). An RF model based on eight key features effectively predicts 30-day mortality in IE patients with good interpretability and performance.