{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:48:18Z","timestamp":1783698498967,"version":"3.55.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"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. Pharmacol."],"abstract":"<jats:p><jats:bold>Objective:<\/jats:bold> To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73).<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% <jats:italic>CI<\/jats:italic> = 0.91\u20131.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD.<\/jats:p>","DOI":"10.3389\/fphar.2023.1334439","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T04:11:58Z","timestamp":1704859918000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients"],"prefix":"10.3389","volume":"14","author":[{"given":"Zihan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linghua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.gheart.2018.01.001","article-title":"National prevalence of self-reported coronary heart disease and chronic stable angina pectoris: factor analysis of the underlying cardiometabolic risk factors in the SuRFNCD-2011","volume":"13","author":"Abbasi","year":"2018","journal-title":"Glob. Heart"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102289","DOI":"10.1016\/j.artmed.2022.102289","article-title":"Machine learning-based heart disease diagnosis: a systematic literature review","volume":"128","author":"Ahsan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"e21061","DOI":"10.2196\/21061","article-title":"Digital technology interventions for risk factor modification in patients with cardiovascular disease: systematic review and meta-analysis","volume":"9","author":"Akinosun","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"B4","doi-asserted-by":"publisher","first-page":"3769152","DOI":"10.1155\/2016\/3769152","article-title":"Assessing risk in patients with stable coronary disease: when should we intensify care and follow-up? Results from a meta-analysis of observational studies of the COURAGE and FAME era","volume":"2016","author":"Barbero","year":"2016","journal-title":"Sci. (Cairo)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"150","DOI":"10.19852\/j.cnki.jtcm.2021.01.017","article-title":"Traditional Chinese Medicine in the prevention and treatment of stable angina pectoris in patients with coronary heart disease based on the theory of \u201cphlegm and blood stasis\u201d under guidance of evidence-based medicine: a prospective cohort study","volume":"41","author":"Chen","year":"2021","journal-title":"J. Tradit. Chin. Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"262","DOI":"10.2174\/1570161117666190513105859","article-title":"Antiplatelet therapy in acute coronary syndromes. Lights and shadows of platelet function tests to guide the best therapeutic approach","volume":"18","author":"Cimmino","year":"2020","journal-title":"Curr. Vasc. Pharmacol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"B8","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: the Framingham Heart Study","volume":"117","author":"D\u2019Agostino","year":"2008","journal-title":"Circulation"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3856","DOI":"10.1093\/eurheartj\/ehab468","article-title":"Frailty and cardiovascular outcomes in the national health and aging trends study","volume":"42","author":"Damluji","year":"2021","journal-title":"Eur. Heart J."},{"key":"B10","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1136\/heartjnl-2013-303640","article-title":"Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score","volume":"99","author":"Dorresteijn","year":"2013","journal-title":"Heart"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e354","DOI":"10.1161\/CIR.0b013e318277d6a0","article-title":"2012 ACCF\/AHA\/ACP\/AATS\/PCNA\/SCAI\/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology foundation\/American heart association task force on practice guidelines, and the American College of physicians, American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons","volume":"126","author":"Fihn","year":"2012","journal-title":"Circulation"},{"key":"B12","doi-asserted-by":"publisher","first-page":"101099","DOI":"10.1016\/j.ijcha.2022.101099","article-title":"Outcomes after coronary angiography for unstable angina compared to stable angina, myocardial infarction and an asymptomatic general population","volume":"42","author":"Fladseth","year":"2022","journal-title":"Int. J. Cardiol. Heart Vasc."},{"key":"B13","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jcin.2019.11.001","article-title":"1-Year outcomes of angina management guided by invasive coronary function testing (CorMicA)","volume":"13","author":"Ford","year":"2020","journal-title":"JACC Cardiovasc Interv."},{"key":"B14","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1097\/CM9.0000000000001257","article-title":"Efficacy and safety of Shexiang Baoxin pill (MUSKARDIA) in patients with stable coronary artery disease: a multicenter, double-blind, placebo-controlled phase IV randomized clinical trial","volume":"134","author":"Ge","year":"2021","journal-title":"Chin. Med. J. Engl."},{"key":"B15","doi-asserted-by":"publisher","first-page":"S49","DOI":"10.1161\/01.cir.0000437741.48606.98","article-title":"2013 ACC\/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology\/American heart association task force on practice guidelines","volume":"129","author":"Goff","year":"2014","journal-title":"Circulation"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e005119","DOI":"10.1161\/CIRCOUTCOMES.118.005119","article-title":"Clinical and angiographic predictors of patient-reported angina 1 Year after coronary artery bypass graft surgery","volume":"12","author":"Hattler","year":"2019","journal-title":"Circ. Cardiovasc Qual. Outcomes"},{"key":"B17","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1136\/bmj.39261.471806.55","article-title":"Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study","volume":"335","author":"Hippisley-Cox","year":"2007","journal-title":"BMJ"},{"key":"B18","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1253\/circj.cj-09-0637","article-title":"Coronary score adds prognostic information for patients with acute coronary syndrome","volume":"74","author":"Huang","year":"2010","journal-title":"Circ. J."},{"key":"B19","doi-asserted-by":"publisher","first-page":"112","DOI":"10.4103\/wjtcm.wjtcm_8_20","article-title":"Investigation on the mode of action of the traditional Chinese medical prescription-yiqihuoxue formula, an effective extravasation treatment for cerebral vascular microemboli in ApoE-\/- mice","volume":"6","author":"Jiang","year":"2020","journal-title":"World J. Traditional Chin. Med."},{"key":"B20","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1093\/ehjqcco\/qcaa065","article-title":"Poor quality of life in patients with and without frailty: co-prevalence and prognostic implications in patients undergoing percutaneous coronary interventions and cardiac catheterization","volume":"7","author":"Kanwar","year":"2021","journal-title":"Eur. Heart J. Qual. Care Clin. Outcomes"},{"key":"B21","doi-asserted-by":"publisher","first-page":"933803","DOI":"10.3389\/fcvm.2022.933803","article-title":"Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease","volume":"9","author":"Kim","year":"2022","journal-title":"Front. Cardiovasc Med."},{"key":"B22","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"B23","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.jacc.2023.05.025","article-title":"Complete revascularization and angina-related health status in the ISCHEMIA trial","volume":"82","author":"Mavromatis","year":"2023","journal-title":"J. Am. Coll. Cardiol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1161\/CIRCULATIONAHA.121.054567","article-title":"International observational analysis of evolution and outcomes of chronic stable angina: the multinational CLARIFY study","volume":"144","author":"Mesnier","year":"2021","journal-title":"Circulation"},{"key":"B25","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1161\/01.cir.59.3.607","article-title":"Nomenclature and criteria for diagnosis of ischemic heart disease. Report of the Joint International Society and Federation of Cardiology\/World Health Organization task force on standardization of clinical nomenclature","volume":"59","author":"National Institutes of Health","year":"1979","journal-title":"Circulation"},{"key":"B26","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.jchf.2018.09.006","article-title":"Physical activity, fitness, and obesity in heart failure with preserved ejection fraction","volume":"6","author":"Pandey","year":"2018","journal-title":"JACC Heart Fail"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e029736","DOI":"10.1161\/JAHA.122.029736","article-title":"Developing clinical risk prediction models for worsening heart failure events and death by left ventricular ejection fraction","volume":"12","author":"Parikh","year":"2023","journal-title":"J. Am. Heart Assoc."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1136\/heartjnl-2013-305399","article-title":"Antiplatelet therapy for secondary prevention of coronary artery disease","volume":"100","author":"Pilgrim","year":"2014","journal-title":"Heart"},{"key":"B29","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.atherosclerosis.2019.05.012","article-title":"A guide for Gensini Score calculation","volume":"287","author":"Rampidis","year":"2019","journal-title":"Atherosclerosis"},{"key":"B30","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/0735-1097(94)00397-9","article-title":"Development and evaluation of the Seattle Angina Questionnaire: a new functional status measure for coronary artery disease","volume":"25","author":"Spertus","year":"1995","journal-title":"J. Am. Coll. Cardiol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1001\/jamainternmed.2013.12855","article-title":"Percutaneous coronary intervention outcomes in patients with stable obstructive coronary artery disease and myocardial ischemia: a collaborative meta-analysis of contemporary randomized clinical trials","volume":"174","author":"Stergiopoulos","year":"2014","journal-title":"JAMA Intern Med."},{"key":"B32","doi-asserted-by":"publisher","first-page":"105204","DOI":"10.1016\/j.compbiomed.2021.105204","article-title":"Understanding the bias in machine learning systems for cardiovascular disease risk assessment: the first of its kind review","volume":"142","author":"Suri","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1001\/jamacardio.2020.7478","article-title":"Interpretation of the Seattle angina questionnaire as an outcome measure in clinical trials and clinical care: a review","volume":"6","author":"Thomas","year":"2021","journal-title":"JAMA Cardiol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.csbj.2016.11.001","article-title":"Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques","volume":"15","author":"Tripoliti","year":"2016","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B35","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1177\/0962280218784726","article-title":"Sample size for binary logistic prediction models: beyond events per variable criteria","volume":"28","author":"van Smeden","year":"2019","journal-title":"Stat. Methods Med. Res."},{"key":"B36","doi-asserted-by":"publisher","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart disease and stroke statistics-2021 update: a report from the American heart association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"B37","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.amjmed.2012.01.014","article-title":"An international model to predict recurrent cardiovascular disease","volume":"125","author":"Wilson","year":"2012","journal-title":"Am. J. Med."},{"key":"B38","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1136\/hrt.2006.108167","article-title":"Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC)","volume":"93","author":"Woodward","year":"2007","journal-title":"Heart"},{"key":"B39","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1161\/CIRCULATIONAHA.116.022367","article-title":"Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-par Project (prediction for ASCVD risk in China)","volume":"134","author":"Yang","year":"2016","journal-title":"Circulation"}],"container-title":["Frontiers in Pharmacology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2023.1334439\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T04:12:03Z","timestamp":1704859923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphar.2023.1334439\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":39,"alternative-id":["10.3389\/fphar.2023.1334439"],"URL":"https:\/\/doi.org\/10.3389\/fphar.2023.1334439","relation":{},"ISSN":["1663-9812"],"issn-type":[{"value":"1663-9812","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]},"article-number":"1334439"}}