Table 1_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.docx

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; p < 0.001). Based on pooled out-of-center predictions, the integrated model had a 5-year AUC of 0.779 (95% CI, 0.746–0.812), compared with 0.762 (95% CI, 0.728–0.796) for the clinical model; the difference was 0.017 (95% CI, −0.003–0.038). Its Brier score was 0.071, observed-to-expected ratio was 1.01, and calibration slope was 0.97.

The integrated model showed a small net-benefit advantage over the clinical model at thresholds of approximately 8%–15%. SHAP ranked PRS, age, and low-density lipoprotein cholesterol highest and showed a concordant interaction pattern between PRS and low-density lipoprotein cholesterol. Five-year cumulative incidences were 2.7%, 7.2%, and 19.8% in the prespecified low-, intermediate-, and high-risk categories (Gray's test p < 0.001).</p>Conclusions<p>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.</p>