{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:00:55Z","timestamp":1790226055296,"version":"4.1.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats: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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats: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\u2013external 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\u2013Gray regression, and model behavior was examined using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u20131.55;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Based on pooled out-of-center predictions, the integrated model had a 5-year AUC of 0.779 (95% CI, 0.746\u20130.812), compared with 0.762 (95% CI, 0.728\u20130.796) for the clinical model; the difference was 0.017 (95% CI, \u22120.003\u20130.038). 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%\u201315%. 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\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats: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\u2013external validation. External validation is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1936899","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T04:31:48Z","timestamp":1790224308000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and internal\u2013external validation of an interpretable machine learning model integrating clinical and polygenic predictors for coronary artery disease: a multicenter retrospective cohort study"],"prefix":"10.3389","volume":"13","author":[{"given":"Fangmeng","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Cardiology, Wuxi No.2 People's Hospital","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangwen","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Hainan 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