Machine Learning Approach to Identify Phenotypes in Patients with Ischaemic Heart Failure with Reduced Ejection Fraction
Year: 2024
Type: article
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.
DOI: 10.1002/ejhf.3547
Institutions: Inserm, Centre Hospitalier Régional et Universitaire de Nancy, French Clinical Research Infrastructure Network, Défaillance Cardiovasculaire Aiguë et Chronique, Université de Lorraine, Stavanger University Hospital, University of Bergen, University of Michigan, Michigan Medicine, University of Glasgow, Berlin-Brandenburger Centrum für Regenerative Therapien, German Centre for Cardiovascular Research, Charité - Universitätsmedizin Berlin, University Medical Center Groningen, National University of Singapore, University of Groningen, National University Heart Centre Singapore, Duke-NUS Medical School, National Heart Centre Singapore, Brigham and Women's Hospital, Harvard University, University of California San Diego
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,250
OpenAlex ID: W4405211316