Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction.

ABSTRACT:

Aims

Patients experiencing ischaemic heart failure with reduced ejection fraction (HFrEF) represent a diverse group. We hypothesize that machine learning clustering can help separate distinctive patient phenotypes, paving the way for personalized management.

Methods and results

A total of 8591 ischaemic HFrEF patients pooled from the EPHESUS and CAPRICORN trials (64 ± 12 years; 28% women) were included in this analysis. Clusters were identified using both clinical and biological variables. Association between clusters and the composite of (i) heart failure hospitalization or all-cause death, (ii) cardiovascular (CV) hospitalization or all-cause death, and (iii) major adverse CV events was assessed. The derived algorithm was applied in the COMMANDER-HF trial (n = 5022) for ex

SUBMITTER: Monzo L 

PROVIDER: S-EPMC12803694 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature