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oai:figshare.com:article/33979246 2026-09-24T04:29:01Z category_140 portal_316 item_type_3 month_year_09_2026

Supplementary file 2_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 Fangmeng Lei (25101646) Xiangwen Xi (8906696) Zihe Dang (25101649) Shaoqing Cao (25101652) Yanqing Shen (2606146) Wenjing Hu (503593) Qiang Huang (43791) Weiwei Zhang (222964) Yanwei Yin (25101655) Wenlin Lu (25101658) Cardiology coronary artery disease explainable artificial intelligence machine learning polygenic risk score risk stratification Background<p>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).</p>Methods<p>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).</p>Results<p>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; 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> 2026-09-24T04:29:01Z Dataset Dataset 10.3389/fcvm.2026.1936899.s003 https://figshare.com/articles/dataset/Supplementary_file_2_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/33979246 CC BY 4.0