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

BACKGROUNDThree-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.METHODSWe 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.RESULTSThe 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.CONCLUSIONSThe 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.

Coronary artery disease (CAD) is the leading cause of death and disability worldwide.^[1] Three-vessel disease (TVD) is the most severe form of CAD and is characterized by significant stenosis in all three major coronary arteries. The application of myocardial revascularization techniques, including coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI), has significantly improved the clinical outcomes of patients with severe CAD. CABG has traditionally been the standard therapy for complex coronary lesions, including TVD.^[2] In recent years, with the advancements in PCI technology and the accumulation of operators’ experience, the incidence of periprocedural and long-term adverse events of PCI has substantially decreased, and PCI has been gradually applied in the treatment of TVD.^[3,4] Current guidelines recommend use of the SYNergy between PCI with TAXus and cardiac surgery (SYNTAX) score and diabetes status to guide the revascularization strategy for patients with TVD.^[5,6] Current practice guidelines do not recommend PCI for patients with TVD with a SYNTAX score of ≥ 23. However, using the SYNTAX score to guide clinical decision-making between PCI and CABG remains controversial, and its reasonability has been questioned since a newly published meta-analysis showed no significant association between the SYNTAX score and the comparative effectiveness of PCI and CABG.^[7] Moreover, the SYNTAX score is a quantitative indicator of the anatomical complexity of TVD and does not include clinical variables that may have significant effects on the patient’s prognosis. Whether some patients with specific clinical characteristics can obtain a comparable or even greater survival benefit from PCI than from CABG is unclear. Moreover, risk assessment for patients with TVD after revascularization therapy remain challenging.^[8–10]Machine learning has recently emerged as an important research method and has been successfully applied in many scientific fields, including clinical medicine.^[11–13] The decision tree algorithm, a common approach in machine learning, can handle non-linearity, heterogeneous effects, and high-dimensional features and partition a trial population into subgroups characterized by multiple simultaneous characteristics.^[14] In the present study involving a large cohort of patients with TVD with a SYNTAX score of ≥ 23, we employed a decision tree algorithm to generate specific subgroups, compared the long-term prognosis between patients who underwent PCI or CABG in each subgroup, and conducted a comparative analysis of the long-term prognosis between subgroups generated by machine learning. We evaluated whether machine learning can help in selecting the optimal revascularization method and assessing risk in patients with severe TVD.