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Identifying distinct clinical clusters in heart failure with mildly reduced ejection fraction

Authors :
Meijs, Claartje
Brugts, Jasper J.
Lund, Lars H.
Linssen, Gerard C. M.
Brunner-La Rocca, Hans-Peter
Dahlström, Ulf
Vaartjes, Ilonca
Koudstaal, Stefan
Asselbergs, Folkert W.
Savarese, Gianluigi
Uijl, Alicia
Meijs, Claartje
Brugts, Jasper J.
Lund, Lars H.
Linssen, Gerard C. M.
Brunner-La Rocca, Hans-Peter
Dahlström, Ulf
Vaartjes, Ilonca
Koudstaal, Stefan
Asselbergs, Folkert W.
Savarese, Gianluigi
Uijl, Alicia
Publication Year :
2023

Abstract

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 registrybased 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.<br />Funding Agencies|Swedish National Board of Health and Welfare; Swedish Association of Local Authorities and Regions; Swedish Society of Cardiology; Swedish Heart-Lung Foundation; Servier, the Netherlands; EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart; Swedish Research Council; Swedish Heart Lung Foundation [116074]; Stockholm County Council [2013-23897-104604-23, 523-2014-2336]; UCL Hospitals NIHR Biomedical Research Centre [20150557, 20170841]; Dutch Heart Foundation [20140220, 20170112]

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1442998109
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1016.j.ijcard.2023.05.024