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

ABSTRACT:

Aims

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

Methods and results

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

SUBMITTER: Chu T 

PROVIDER: S-EPMC12450507 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature