Development and internal-external validation of an interpretable machine learning model integrating clinical and polygenic predictors for coronary artery disease: a multicenter retrospective cohort study.

Background: Accurate primary-prevention risk stratification for coronary artery disease (CAD) remains challenging. We developed and internally validated a framework integrating clinical variables with a polygenic risk score (PRS). Methods: This three-center retrospective cohort included 2,067 adults without baseline CAD. The primary target was 5-year CAD cumulative incidence, with non-coronary death treated as a competing event. Clinical, PRS-only, and integrated cause-specific eXtreme Gradient Boosting (XGBoost) models were evaluated using leave-one-center-out internal-external validation. Performance assessment included cumulative/dynamic area under the curve (AUC), inverse-probability-of-censoring-weighted Brier score, calibration, and competing-risk decision-curve analysis. PRS associations were estimated using Fine-Gray regression, and model behavior was examined using SHapley Additive exPlanations (SHAP). Results: During a median follow-up of 6.8 years, 215 participants experienced the primary endpoint; 163 events occurred within 5 years, and the 5-year cumulative incidence was 8.4%. Each 1-SD increase in the principal component-adjusted PRS was associated with the primary endpoint (adjusted subdistribution hazard ratio, 1.37; 95% CI, 1.21-1.55; Conclusions: The PRS was independently associated with incident CAD. Integration with clinical variables yielded a modest, statistically uncertain improvement in discrimination and satisfactory calibration during internal-external validation. External validation is required before clinical implementation.