ArticleEuropean heart journal. Digital health2025
Identification of clinical phenotypes and heterogeneous treatment effects of surgical revascularization in ischaemic cardiomyopathy: a machine learning consensus clustering analysis.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( Conclusion: 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.
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