Development and validation of an interpretable machine learning model for predicting 5-year major adverse cardiovascular events in patients with coronary artery disease

BACKGROUND: Coronary artery disease (CAD) remains a major contributor to global cardiovascular mortality and accurate prognosis is critical for guiding clinical decision-making. This study aimed to develop and validate interpretable machine learning (ML) models for predicting 5-year major adverse cardiovascular events (MACE) in hospitalized CAD patients. METHODS: A prospective cohort of 705 CAD patients was included and randomly divided into training (n = 494) and validation (n = 211) sets. Key predictors were selected using least absolute shrinkage and selection operator (LASSO) regression. Four survival-based models were developed, and model performance was assessed using discrimination, calibration, and decision curve analysis. Shapley Additive Explanations (SHAP) analysis was applied to enhance model interpretability. RESULTS: A total of 705 hospitalized CAD patients were included (mean age 63.2 years; 72.5% men), of whom 221 (31.3%) developed MACEs during the 5-year follow-up. LASSO regression revealed 11 key predictors, including left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP) level, nitrate use, CAD duration, depressive symptoms, and age. Among the four models, the random survival forest (RSF) model showed favourable discrimination performance, with C-index of 0.804 (95% CI: 0.770-0.837) in the training cohort and 0.710 (95% CI: 0.650-0.768) in the validation cohort. The RSF model also showed acceptable calibration, achieving the lowest Brier score in the validation cohort. Decision curve analysis (DCA) demonstrated that the RSF model provided potential clinical benefit over the treat-all and treat-none strategies across a wide range of risk thresholds. SHAP analysis revealed that the LVEF, age, and number of diseased vessels were the most important predictors of 5-year MACEs. CONCLUSIONS: The RSF model demonstrated relatively favourable discrimination, calibration, and clinical utility for predicting 5-year MACEs in hospitalized CAD patients. These findings suggest that ML-based approaches may assist in individualized risk stratification and guide secondary prevention strategies.