{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:58:12Z","timestamp":1783612692334,"version":"3.55.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2,018YFC131,1505"],"award-info":[{"award-number":["2,018YFC131,1505"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>ST-elevation myocardial infarction (STEMI) poses a significant threat to global mortality and disability. Advances in percutaneous coronary intervention (PCI) have reduced in-hospital mortality, highlighting the importance of post-discharge management. Machine learning (ML) models have shown promise in predicting adverse clinical outcomes. However, a systematic approach that combines high predictive accuracy with model simplicity is still lacking.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study applied three data processing and ML algorithms to address class imbalance and support model development. ML models were trained to predict one-year mortality in STEMI patients post-PCI, with performance evaluated using accuracy, sensitivity, precision, F1-score, area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We analyzed data from 1,274 patients, incorporating 46 clinical and laboratory features. Using the Random Forest (RF) algorithm, we achieved an AUROC of 0.94 (95% confidence interval (CI): 0.90\u20130.98), an AUPRC of 0.44 (95% CI:0.15\u20130.76) in the internal validation set, identifying five key predictors: cardiogenic shock, creatinine, NT-proBNP, diastolic blood pressure, and left ventricular ejection fraction. By integrating risk stratification, the model\u2019s performance improved, achieving an AUROC of 0.97 (95% CI: 0.96\u20130.99) and an AUPRC of 0.74 (95% CI: 0.60\u20130.84).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study highlights the feasibility of constructing accurate and interpretable ML models using a minimal set of predictors, supplemented by risk stratification, to improve long-term outcome prediction in STEMI patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2025.1618492","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:38:16Z","timestamp":1755859096000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification"],"prefix":"10.3389","volume":"8","author":[{"given":"Wenqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongdong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"156696","DOI":"10.1016\/j.cyto.2024.156696","article-title":"Inflammatory biomarkers and long-term outcome in young patients three months after a first myocardial infarction","volume":"182","author":"Cederstr\u00f6m","year":"2024","journal-title":"Cytokine"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"107636","DOI":"10.1016\/j.compbiomed.2023.107636","article-title":"Diagnostic test accuracy of artificial intelligence-assisted detection of acute coronary syndrome: a systematic review and meta-analysis","volume":"167","author":"Chan","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1002\/ehf2.15062","article-title":"Explainable machine learning and online calculators to predict heart failure mortality in intensive care units","volume":"12","author":"Chen","year":"2024","journal-title":"ESC Heart Fail."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/S0140-6736(20)32519-8","article-title":"Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets","volume":"397","author":"D'Ascenzo","year":"2021","journal-title":"Lancet"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"e254894","DOI":"10.1371\/journal.pone.0254894","article-title":"Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach","volume":"16","author":"Fukumoto","year":"2021","journal-title":"PLoS One"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1016\/S0140-6736(23)00459-2","article-title":"Current concepts in coronary artery revascularisation","volume":"401","author":"Gaudino","year":"2023","journal-title":"Lancet"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1093\/eurheartj\/ehad836","article-title":"Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions","volume":"45","author":"Hamilton","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"132191","DOI":"10.1016\/j.ijcard.2024.132191","article-title":"Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients","volume":"409","author":"Hosseini","year":"2024","journal-title":"Int. J. Cardiol."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"102409","DOI":"10.1016\/j.eclinm.2023.102409","article-title":"Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study","volume":"68","author":"Hu","year":"2024","journal-title":"eClinicalMedicine"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1186\/s12889-025-21609-7","article-title":"Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS)","volume":"25","author":"Huang","year":"2025","journal-title":"BMC Public Health"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"396","DOI":"10.3390\/jcdd11120396","article-title":"Evaluating binary classifiers for cardiovascular disease prediction: enhancing early diagnostic capabilities","volume":"11","author":"Iacobescu","year":"2024","journal-title":"J. Cardiovasc. Dev. Dis."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1340022","DOI":"10.3389\/fcvm.2024.1340022","article-title":"Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm","volume":"11","author":"Jeong","year":"2024","journal-title":"Front. Cardiovasc. Med."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1001\/jamacardio.2021.0122","article-title":"Use of machine learning models to predict death after acute myocardial infarction","volume":"6","author":"Khera","year":"2021","journal-title":"JAMA Cardiol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jacc.2024.05.003","article-title":"Transforming cardiovascular care with artificial intelligence: from discovery to practice","volume":"84","author":"Khera","year":"2024","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.amjcard.2020.07.048","article-title":"Prediction of 1-year mortality from acute myocardial infarction using machine learning","volume":"133","author":"Lee","year":"2020","journal-title":"Am. J. Cardiol."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1186\/s13019-024-02856-y","article-title":"Development and validation of a machine learning predictive model for perioperative myocardial injury in cardiac surgery with cardiopulmonary bypass","volume":"19","author":"Li","year":"2024","journal-title":"J. Cardiothorac. Surg."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"13393","DOI":"10.1038\/s41598-024-64048-x","article-title":"Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"765","DOI":"10.4244\/EIJ-D-20-01155","article-title":"A deep learning algorithm for detecting acute myocardial infarction","volume":"17","author":"Liu","year":"2021","journal-title":"EuroIntervention"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"864312","DOI":"10.3389\/fcvm.2022.864312","article-title":"Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations","volume":"9","author":"Liu","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1097\/CRD.0000000000000294","article-title":"Machine intelligence in cardiovascular medicine","volume":"28","author":"Miller","year":"2020","journal-title":"Cardiol. Rev."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.amjcard.2023.01.048","article-title":"Unsupervised machine learning with cluster analysis in patients discharged after an acute coronary syndrome: insights from a 23,270-patient study","volume":"193","author":"Mohammadi","year":"2023","journal-title":"Am. J. Cardiol."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"6190","DOI":"10.1016\/j.cmpb.2021.106190","article-title":"A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach","volume":"207","author":"Mohd Faizal","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"144","DOI":"10.3390\/diagnostics14020144","article-title":"Machine learning-based predictive models for detection of cardiovascular diseases","volume":"14","author":"Ogunpola","year":"2024","journal-title":"Diagnostics"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s12911-023-02168-6","article-title":"Machine learning prediction of mortality in acute myocardial infarction","volume":"23","author":"Oliveira","year":"2023","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"109839","DOI":"10.1016\/j.compbiomed.2025.109839","article-title":"A machine learning based death risk analysis and prediction of ST-segment elevation myocardial infarction (STEMI) patients","volume":"188","author":"\u00d6ztekin","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"273","DOI":"10.3233\/SHTI190226","article-title":"Enhancing prediction models for one-year mortality in patients with acute myocardial infarction and post myocardial infarction syndrome","volume":"264","author":"Payrovnaziri","year":"2019","journal-title":"Stud. Health Technol. Inform."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"103470","DOI":"10.1016\/j.redox.2024.103470","article-title":"Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants","volume":"79","author":"Qi","year":"2025","journal-title":"Redox Biol."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"100322","DOI":"10.1016\/j.medntd.2024.100322","article-title":"Deep learning-based approaches for myocardial infarction detection: a comprehensive review recent advances and emerging challenges","volume":"23","author":"Radwa","year":"2024","journal-title":"Med. Novel Technol. Devices"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"109439","DOI":"10.1016\/j.compbiomed.2024.109439","article-title":"Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms","volume":"184","author":"Razavi","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"7341","DOI":"10.1007\/s00371-025-03808-w","article-title":"Toward artificial general intelligence in health care","volume":"41","author":"Ren","year":"2025","journal-title":"Vis. Comput."},{"key":"ref32","author":"Shi","year":"2023"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12933-025-02640-9","article-title":"Development and validation of prediction models for stroke and myocardial infarction in type 2 diabetes based on health insurance claims: does machine learning outperform traditional regression approaches?","volume":"24","author":"Stephan","year":"2025","journal-title":"Cardiovasc. Diabetol."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"5078","DOI":"10.1007\/s12325-021-01908-2","article-title":"Application of artificial intelligence in acute coronary syndrome: a brief literature review","volume":"38","author":"Wang","year":"2021","journal-title":"Adv. Ther."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"132925","DOI":"10.1016\/j.ijcard.2024.132925","article-title":"Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: a machine learning model vs. the PRECISE-DAPT score","volume":"421","author":"Yang","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"48487","DOI":"10.2196\/48487","article-title":"Machine learning for early prediction of major adverse cardiovascular events after first percutaneous coronary intervention in patients with acute myocardial infarction: retrospective cohort study","volume":"8","author":"Zhang","year":"2023","journal-title":"JMIR Form. Res."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"4267","DOI":"10.1002\/ehf2.15033","article-title":"Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning","volume":"11","author":"Zheng","year":"2024","journal-title":"ESC Heart Fail."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"625","DOI":"10.3967\/bes2023.089","article-title":"Exploring the feasibility of machine learning to predict risk stratification within 3 months in chest pain patients with suspected NSTE-ACS","volume":"36","author":"Zheng","year":"2023","journal-title":"Biomed. Environ. Sci."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s41512-021-00102-w","article-title":"A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve","volume":"5","author":"Zhou","year":"2021","journal-title":"Diagn. Progn. Res."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1618492\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T10:38:18Z","timestamp":1755859098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1618492\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"references-count":39,"alternative-id":["10.3389\/frai.2025.1618492"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1618492","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,22]]},"article-number":"1618492"}}