{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T13:04:03Z","timestamp":1784379843955,"version":"3.55.0"},"reference-count":35,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity\/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model\u2010developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and\n                    <jats:italic>CYP2C19<\/jats:italic>\n                    genetic information to predict 1\u2010year ischemic outcomes post\u2010PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR\u2010PCI trial (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20094572) and the Precision PCI registry (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20093745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1\u2009year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR\u2010PCI and validated in Precision PCI using cross\u2010validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (&gt;\u200975). ML models trained on large clinical trial and real\u2010world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de\u2010escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Trial Registration:<\/jats:bold>\n                    TAILOR\u2010PCI URL:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/clinicaltrials.gov\/ct2\/show\/NCT01742117\">https:\/\/clinicaltrials.gov\/ct2\/show\/NCT01742117<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1111\/cts.70655","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:50:41Z","timestamp":1783666241000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning With Genetic and Clinical Data to Predict Ischemic Outcomes After\n                    <scp>PCI<\/scp>"],"prefix":"10.1111","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9342-2138","authenticated-orcid":false,"given":"Caroline W.","family":"Grant","sequence":"first","affiliation":[{"name":"Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8087-2620","authenticated-orcid":false,"given":"Brenden S.","family":"Ingraham","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1160-2333","authenticated-orcid":false,"given":"Ryan J.","family":"Lennon","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences Mayo Clinic  Rochester Minnesota 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H.","family":"Cavallari","sequence":"additional","affiliation":[{"name":"Department of Pharmacotherapy &amp; Translational Research and Center for Pharmacogenomics and Precision Medicine University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3595-5301","authenticated-orcid":false,"given":"Craig R.","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Pharmacotherapy and Experimental Therapeutics University of North Carolina Eshelman School of Pharmacy  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0958-7197","authenticated-orcid":false,"given":"Amber","family":"Beitelshees","sequence":"additional","affiliation":[{"name":"Department of Medicine University of Maryland  Baltimore Maryland 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