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 learni...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 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 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.展开更多
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).
文摘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 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 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.
