December 10, 2024European Journal of Heart Failure15 citationsOpen Access

Machine Learning Approach to Identify Phenotypes in Patients with Ischaemic Heart Failure with Reduced Ejection Fraction

LMLuca MonzoHeart Failure & Transplant EBEmmanuel BressoCentre d’Investigation Clinique Innovation Technologique de Nancy KDKenneth DicksteinHeart Failure & Transplant

Structured PICO

Does machine learning clustering identify distinct patient phenotypes and predict outcomes in patients with ischaemic HFrEF?

Population

8,591 patients with ischaemic heart failure with reduced ejection fraction (HFrEF) pooled from the EPHESUS and CAPRICORN trials (mean age 64 ± 12 years; 28% women), with external validation in 5,022 patients from the COMMANDER-HF trial.

Intervention

Machine learning clustering using clinical and biological variables to identify patient phenotypes.

Outcome

Composite of (i) heart failure hospitalization or all-cause death, (ii) cardiovascular (CV) hospitalization or all-cause death, and (iii) major adverse CV events.composite

Machine learning clustering identifies five distinct clinical phenotypes in ischaemic HFrEF patients with varying risk profiles and differential responses to mineralocorticoid receptor antagonists.

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 external validation. Five clinical distinctive clusters were identified: Cluster 1 (n = 2161) with the older patients, higher prevalence of atrial fibrillation and previous CV events; Cluster 2 (n = 1376) with the higher prevalence of older hypertensive women and smoking habit; Cluster 3 (n = 1157) with the higher prevalence of diabetes and peripheral artery disease; Cluster 4 (n = 2073) with relatively younger patients, mostly men and with the higher left ventricular ejection fraction; Cluster 5 (n = 1824) with the younger patients and lower CV events burden. Cluster membership was efficiently predicted by a random forest algorithm. Clusters were significantly associated with outcomes in derivation and validation datasets, with Cluster 1 having the highest risk, and Cluster 4 the lowest. Mineralocorticoid receptor antagonist benefit on CV hospitalization or all-cause death was magnified in clusters with the lowest risk of events (Clusters 2 and 4). CONCLUSIONS: Clustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients. This classification, accessible online, could enhance future outcome predictions for ischaemic HFrEF cases.

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