Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease
Jie LIU
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
1.State Key Laboratory of Cardiovascular Disease,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China2.Endocrinology and Cardiovascular Disease Centre,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China;Department of Endocrinology,Fuwai Hospital,Chinese Academy of Medical Sciences,Shenzhen,China3.Nanjing TooBoo Technology Co.,Ltd.Nanjing,China4.Department of Cardiology,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China5.Cardiomyopathy Ward,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China6.Medical Research Center,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing,China7.Information Center,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China8.Department of Cardiology,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China;National Clinical Research Center for Cardi-ovascular Diseases,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sci-ences and Peking Union Medical College,Beijing,China9.Department of Endocrinology,Fuwai Hospital,Chinese Academy of Medical Sciences,Shenzhen,China;Solar activity Prediction Center,National Astronomical Ob-servatories,Chinese Academy of Sciences,Beijing,China10.State Key Laboratory of Cardiovascular Disease,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China;Cardiomyopathy Ward,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China;National Clinical Research Center for Cardi-ovascular Diseases,Fuwai Hospital,National Center for Cardiovascular Diseases,Chinese Academy of Medical Sci-ences and Peking Union Medical College,Beijing,China
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 disease. We aimed to take advantage of machine learning to help in de-cision-making and prognostic evaluation in such patients.METHODS We analyzed 3786 patients who had TVD with a SYNTAX score of ≥ 23, had no history of previous revasculariza-tion, and underwent either coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) after enrollment. The patients were randomly assigned to a training group and testing group. The 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 label. RESULTS 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 of > 53 mm), and subgroup 3 (age of > 67 years). PCI conferred a patient survival benefit over CABG in sub-group 2. There 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 data. Among the total study population, the multivariable analysis revealed significant dif-ferences in the risk of all-cause death among patients in three subgroups. CONCLUSIONS The combination of age and LVEDD identified by machine learning can contribute to decision-making and risk assessment of death in patients with severe TVD. The present results suggest that PCI is a better choice for young patients with severe TVD characterized by left ventricular dilation.
机标关键词：learningdecisionriskarteryassessmentcoronarydiseasemachine
论文发表日期：2022-05-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 367-376 )