Objectives
We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared with medical therapy alone.
Methods
Machine learning causal forest modeling was performed to identify the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy from the Surgical Treatment for Ischemic Heart Failure trial. The risks of death from any cause and death from cardiovascular causes between coronary artery bypass grafting and medical therapy alone were assessed in the identified subgroups.
Results
Among 1212 patients enrolled in the Surgical Treatment for Ischemic Heart Failure trial, left ventricular end-systolic volume index, serum creatinine, and age were identified by the machine learning algorithm to distinguish patients with heterogeneous treatment effects. Among patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age 60.27 years or less, coronary artery bypass grafting was associated with a significantly lower risk of death from any cause (adjusted hazard ratio, 0.61; 95% CI, 0.45-0.84) and death from cardiovascular causes (adjusted hazard ratio, 0.63; 95% CI, 0.45-0.89). By contrast, the survival benefits of coronary artery bypass grafting no longer exist in patients with left ventricular end-systolic volume index 84 mL/m2 or less and serum creatinine 1.04 mg/dL or less, or patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age more than 60.27 years.
Conclusions
The current post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial identified heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy. Younger patients with severe left ventricular enlargement were more likely to derive greater survival benefits from coronary artery bypass grafting.