Introduction: Heart failure (HF) is a heterogeneous syndrome, and the specific sub-category HF with mildly reduced ejection fraction (EF) range (HFmrEF; 41-49% EF) is only recently recognised as a distinct entity. Cluster analysis can characterise heterogeneous patient populations and could serve as a stratification tool in clinical trials and for prognostication. The aim of this study was to identify clusters in HFmrEF and compare cluster prognosis.
Methods and results: Latent class analysis to cluster HFmrEF patients based on their characteristics was performed in the Swedish HF registry (n = 7316). Identified clusters were validated in a Dutch cross-sectional HF registry-based dataset CHECK-HF (n = 1536). In Sweden, mortality and hospitalisation across the clusters were compared using a Cox proportional hazard model, with a Fine-Gray sub-distribution for competing risks and adjustment for age and sex. Six clusters were discovered with the following prevalence and hazard ratio with 95% confidence intervals (HR [95%CI]) vs. cluster 1: 1) low-comorbidity (17%, reference), 2) ischaemic-male (13%, HR 0.9 [95% CI 0.7-1.1]), 3) atrial fibrillation (20%, HR 1.5 [95% CI 1.2-1.9]), 4) device/wide QRS (9%, HR 2.7 [95% CI 2.2-3.4]), 5) metabolic (19%, HR 3.1 [95% CI 2.5-3.7]) and 6) cardio-renal phenotype (22%, HR 2.8 [95% CI 2.2-3.6]). The cluster model was robust between both datasets.
Conclusion: We found robust clusters with potential clinical meaning and differences in mortality and hospitalisation. Our clustering model could be valuable as a clinical differentiation support and prognostic tool in clinical trial design.
Keywords: Clustering; Heart failure with mildly reduced ejection fraction; Heterogeneity; Latent class analysis.
Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.