Phenotyping of acute heart failure with preserved ejection fraction: real‐world outcomes in a cohort of older patients

TL;DR

This study identifies three distinct subgroups of older patients with heart failure and preserved ejection fraction, showing different clinical features and risks of in-hospital death.

Contribution

The study introduces a novel phenotyping approach using latent class analysis to identify subgroups of HFpEF patients with distinct clinical profiles and outcomes.

Findings

Three phenogroups were identified with differing comorbidities, age, and biomarker levels.

Phenogroups 2 and 3 had significantly higher in-hospital mortality risks compared to phenogroup 1.

Phenogroup 2 showed the worst prognosis with more than double the risk of in-hospital death.

Abstract

Previous studies have identified distinct subgroups (phenogroups) of patients with heart failure with preserved ejection fraction (HFpEF).

This study aims to identify distinct phenotypes in older patients with HFpEF hospitalised for acute heart failure (AHF) and investigate the relationship between subgroups and outcomes.

Retrospective, single‐center study, including patients ≥65 years hospitalised for AHF over a 4‐year period. We used electronic medical records to collect clinical data, including hospital outcomes. Latent class analysis (LCA) was performed to identify clusters of clinical phenogroups. The primary outcome was all‐cause in‐hospital mortality.

Overall, 770 patients were included. Based on LCA, three phenogroups were identified. Phenogroup 1 (n = 323) had both the lowest burden of comorbidities and N‐terminal pro‐brain natriuretic peptides (NT proBNP) values. Phenogroup 2 (n = 224) had the oldest patients (median age 82 years), the highest prevalence of women (62%) and atrial fibrillation and the worst right ventricular function. Phenogroup 3 (n = 223) consisted mainly of men (57%) and had a higher prevalence of diabetes, obesity and established cardiovascular disease and the worst renal function. Phenogroups 2 and 3 showed a significantly higher risk of primary outcome than phenogroup 1. In addition, survival analysis showed that phenogroup 2 had the worst prognosis, with more than double the risk of in‐hospital death.

In this real‐world cohort of older patients with HFpEF hospitalised for AHF, we identified three subgroups with significantly different features and prognoses. Phenomapping may be an effective tool to identify individuals most likely to experience adverse outcomes, providing a basis for phenotype‐specific treatment strategies.

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