Heart failure & cardiomyopathies
Machine Learning Approach to Identify Phenotypes in Patients with Ischaemic Heart Failure with Reduced Ejection Fraction
Authors' conclusion
Clustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients.
Study context
- Population
- Ischaemic heart failure with reduced ejection fraction (HFrEF) patients pooled from the EPHESUS and CAPRICORN trials (64 ± 12 years; 28% women)validation n = 5022
- Participants
- 8,591 patients
- Comparison
- Machine learning-derived phenotype clusters
- Cluster 1n = 2,161
- Cluster 2n = 1,376
- Cluster 3n = 1,157
- Cluster 4n = 2,073
- Cluster 5n = 1,824
- Primary endpoint
- HF hospitalization or all-cause death; CV hospitalization or all-cause death; MACE
Methodological detailsmachine learning clustering, pooled trials, external validation in COMMANDER-HF (n = 5022)
- Machine learning clustering
- Pooled trials
- External validation in COMMANDER-HF (n = 5022)
Primary result
As reported in the abstract
Association of clusters with outcomes
- Five clinically distinct clusters identified; membership efficiently predicted by a random forest algorithm
- Clusters significantly associated with outcomes in derivation and validation datasets; Cluster 1 highest risk, Cluster 4 lowest
Further findings
MRA benefit on cardiovascular (CV) hospitalization or all-cause death, by cluster
- MRA benefit magnified in lowest-risk clusters (Clusters 2 and 4)
Original publication
Machine Learning Approach to Identify Phenotypes in Patients with Ischaemic Heart Failure with Reduced Ejection Fraction. Eur J Heart Fail. Published 10 December 2024. doi:10.
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