Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease

作     者：Liu JIE Xin-Xing FENG Yan-Feng DUAN Jun-Hao LIU Ce ZHANG Lin JIANG Lian-Jun XU Jian TIAN Xue-Yan ZHAO Yin ZHANG Kai SUN Bo XU Wei ZHAO Ru-Tai HUI Run-Lin GAO Ji-Zheng WANG Jin-Qing YUAN Xin HUANG Lei SONG 

作者机构：State Key Laboratory of Cardiovascular DiseaseFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Endocrinology and Cardiovascular Disease CentreFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Department of EndocrinologyFuwai HospitalChinese Academy of Medical SciencesShenzhenChina Nanjing TooBoo Technology Co.Ltd.NanjingChina Department of CardiologyFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Cardiomyopathy WardFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Medical Research CenterPeking Union Medical College HospitalChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Information CenterFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina National Clinical Research Center for Cardiovascular DiseasesFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Solar activity Prediction CenterNational Astronomical ObservatoriesChinese Academy of SciencesBeijingChina 

出 版 物：《Journal of Geriatric Cardiology》 (老年心脏病学杂志（英文版）)

年 卷 期：2022年第19卷第5期

页      面：367-376页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：This work was supported by the CAMS Innovation Fund for Medical Sciences(grant number 2016-I2M-1-002) the Beijing Municipal Natural Science Foundation(grant number 7181008) Capital’s Funds for Health Improvement and Research(grant number 2018-2-4033) 

主　　题：coronary testing treatment 

摘      要：BACKGROUND Three-vessel disease(TVD)with a SYNergy between PCI with TAXus and cardiac surgery(SYNTAX)score of≥23 is one of the most severe types of coronary artery *** aimed to take advantage of machine learning to help in de-cision-making and prognostic evaluation in such *** We analyzed 3786 patients who had TVD with a SYNTAX score of≥23,had no history of previous revascularization,and underwent either coronary artery bypass grafting(CABG)or percutaneous coronary intervention(PCI)after *** patients were randomly assigned to a training group and testing *** C4.5 decision tree algorithm was applied in the training group,and all-cause death after a median follow-up of 6.6 years was regarded as the class *** The decision tree algorithm selected age and left ventricular end-diastolic diameter(LVEDD)as splitting features and divided the patients into three subgroups:subgroup 1(age of≤67 years and LVEDD of≤53 mm),subgroup 2(age of≤67 years and LVEDD of53 mm),and subgroup 3(age of67 years).PCI conferred a patient survival benefit over CABG in sub-group *** was no significant difference in the risk of all-cause death between PCI and CABG in subgroup 1 and subgroup 3 in both the training data and testing *** the total study population,the multivariable analysis revealed significant dif-ferences in the risk of all-cause death among patients in three *** The combination of age and LVEDD identified by machine learning can contribute to decision-making and risk assessment of death in patients with severe *** present results suggest that PCI is a better choice for young patients with severe TVD characterized by left ventricular dilation.