Identification of clinical phenotypes and heterogeneous treatment effects of surgical revascularization in ischaemic cardiomyopathy: a machine learning consensus clustering analysis

To identify ischaemic cardiomyopathy (ICM) patients with different phenotypes for evaluating their outcomes and heterogeneous treatment effects (HTEs) of coronary artery bypass grafting (CABG).

We applied a machine learning-based consensus, K-Medoids clustering analysis to the Surgical Treatment for Ischemic Heart Failure trial. We compared the risk of all-cause mortality and cardiovascular mortality among different phenotypes. The survival benefits of CABG compared with medical therapy alone were assessed in the identified phenotypes for evaluating HTEs. The consensus clustering analysis identified three distinct clinical phenotypes among 1212 ICM patients based on 19 variables. Specifically, phenotype 1 (n = 371) was characterized by younger ages, higher left ventricular ejection fraction (LVEF), and lower left ventricular end-systolic volume index (n = 371). Phenotype 2 had higher angina grades and more left main/left anterior descending artery stenosis (n = 520). Phenotype 3 had lower LVEF, higher New York Heart Association (NYHA) grades, more diabetes, and less hypertension (n = 321). After a median of 9.8 follow-up years, phenotype 3 had the highest risk of all-cause mortality [hazard ratio (HR), 1.96; 95% confidence intervals (CI), 1.62–2.37] and cardiovascular mortality (HR, 2.46; 95% CI, 1.95–3.10) compared to phenotype 1. Among phenotype 3, CABG provided significant survival benefits in all-cause mortality (HR, 0.75; 95% CI, 0.58–0.96) and cardiovascular mortality (HR, 0.67; 95% CI, 0.50–0.90) compared with medical therapy alone.

We identified three phenotypes with distinct outcomes and HTEs among ICM patients. Patients with lower LVEF, higher NYHA grades, and diabetes had the poorest clinical outcomes but were more likely to derive greater survival benefits from CABG.