ArticleEuropean journal of heart failure2025
Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction.
Article in European journal of heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Within a Heartbeat: A Machine Learning Approach Using VCG for Heart Failure Assessment in Sinus Rhythm.Journal of personalized medicine · 2026Article
- State-of-the-art management of ischemic cardiomyopathy: integrating diagnostic pathways, revascularization strategies, and guideline-directed therapy.Heart failure reviews · 2026Review
- Unsupervised phenotypic clustering of cardiac MRI data reveals distinct subgroups associated with outcomes in ischemic cardiomyopathy.The international journal of cardiovascular imaging · 2026Article
- Heart Failure in the Modern Era: A Narrative Overview of Recent Research from 2022-2025.Journal of cardiovascular development and disease · 2025Review
- An individual treatment effect approach to predict response to mineralocorticoid receptor antagonists in patients with heart failure and reduced ejection fraction.European journal of heart failure · 2025Article
- Unsupervised machine learning for cardiovascular disease: A framework for future studies.European journal of heart failure · 2025Review
- A Present Where AI is Enhancing Laboratory Medicine, A Future Where It Redefines Healthcare.EJIFCC · 2025Article
- Comparative Mortality in Heart Failure on Guideline-Directed Medical Therapy Versus Malignant Cancer: A Report From a Global Federated Research Network.Journal of the American Heart Association · 2025Article
- Identifying congestion phenotypes using unsupervised machine learning in acute heart failure.European heart journal. Digital health · 2025Article
- Apixaban in patients with nonvalvular atrial fibrillation, heart failure and low body weight: A report from a global federated research dataset.European journal of clinical investigation · 2025Article
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Authors and funding
12 authors.
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Abstract
aimsPatients 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
resultsA 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).
conclusionsClustering 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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