{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T03:18:40Z","timestamp":1793762320109,"version":"4.6.1"},"reference-count":31,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Up to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Hypothesis<\/jats:title><jats:p>Machine learning (ML) methods can accurately identify risk factors and predict adverse events.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1\u00a0year. Support vector machine, extreme gradient boosting, adaptive boosting, K\u2010nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10\u2010fold cross\u2010validation to predict acute kidney injury (AKI), MI\u00a0during hospitalization, and all\u2010cause mortality within 1\u00a0year. Features with mean Shapley Additive exPlanations\u00a0score &gt;0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision\/recall\u00a0curve were used to evaluate the performance of the models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Overall, 2.6% of patients died within 1\u00a0year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other seven\u00a0ML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all\u2010cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The LDA model, using easily accessible variables from in\u2010hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1\u00a0year in ACS patients after PCI.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.24127","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T06:09:14Z","timestamp":1693548554000},"page":"1594-1602","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study"],"prefix":"10.1002","volume":"46","author":[{"given":"Long","family":"Song","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Surgery The Second Xiangya Hospital Central South 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