ISSN 1671-5411 CN 11-5329/R
Volume 19 Issue 5
May  2022
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Please cite this article as: LIU L, FENG XX, DUAN YF, LIU JH, ZHANG C, JIANG L, XU LJ, TIAN J, ZHAO XY, ZHANG Y, SUN K, XU B, ZHAO W, HUI RT, GAO RL, WANG JZ, YUAN JQ, HUANG X, SONG L. Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease. J Geriatr Cardiol 2022; 19(5): 367−376. DOI: 10.11909/j.issn.1671-5411.2022.05.005
Citation: Please cite this article as: LIU L, FENG XX, DUAN YF, LIU JH, ZHANG C, JIANG L, XU LJ, TIAN J, ZHAO XY, ZHANG Y, SUN K, XU B, ZHAO W, HUI RT, GAO RL, WANG JZ, YUAN JQ, HUANG X, SONG L. Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease. J Geriatr Cardiol 2022; 19(5): 367−376. DOI: 10.11909/j.issn.1671-5411.2022.05.005

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

doi: 10.11909/j.issn.1671-5411.2022.05.005
*The authors contributed equally to this manuscript
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  •  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 decision-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 revascularization, 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 subgroup 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 differences 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.
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