ArticleEuropean journal of heart failure2021
Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction.
Article in European journal of heart failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
59 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.Current heart failure reports · 2023Pooled it
- Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.Lancet (London, England) · 2021Pooled it
- Trial
- Bidirectional metabolic-vascular feedback loops in heart failure: From myocardial energetics to multisystem cardiometabolic crosstalk.iScience · 2026Review
- The Future of Cardiac Care: A Review on the Role for Non-Invasive, Home-Based Monitoring and Rehabilitation after Hospitalisation for Acute Decompensated Heart Failure.Current heart failure reports · 2026Review
- Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.European heart journal. Digital health · 2026Review
- Longitudinal phenotyping of heart failure with preserved ejection fraction identifies early- and end-stage disease states.Open heart · 2026Article
- Trials and tribulations of spironolactone in heart failure with preserved ejection fraction: Insights from the SPIRIT-HF trial.Heart failure reviews · 2026Review
- Comorbidity Clusters in Acute Heart Failure: Insights From the AMERICCAASS Registry.Journal of the American Heart Association · 2026Observational
- Influence of Phenotypes on Short-Term Outcomes in Hospitalized Heart Failure with Preserved Ejection Fraction-Insights from a North-Eastern Romanian Cohort.Medical sciences (Basel, Switzerland) · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- What Exactly Is Cardiometabolic HFpEF: A Phenotype or an Endotype?Circulation. Heart failure · 2026Article
- Artificial intelligence-enabled deep clinical phenotyping in heart failure with preserved ejection fraction: from patient subtypes to precision care.Frontiers in cardiovascular medicine · 2026Review
- Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes.Communications medicine · 2025Article
- Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction.EClinicalMedicine · 2025Article
- Heart Failure Syndromes: Different Definitions of Different Diseases-Do We Need Separate Guidelines? A Narrative Review.Journal of clinical medicine · 2025Review
- Sex-specific cardiometabolic multimorbidity, metabolic syndrome and left ventricular function in heart failure with preserved ejection fraction in the UK Biobank.Cardiovascular diabetology · 2025Article
- State of precision medicine for heart failure with preserved ejection fraction in a new therapeutic age.ESC heart failure · 2025Review
- Current and future landscape of heart failure management: understanding the present, unraveling the future.Future cardiology · 2025Article
- Identification of Anticoagulation Benefit Subgroups of Patients With Left Ventricular Thrombus.Reviews in cardiovascular medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
aimsWe aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction ≥50%). METHODS AND
resultsWe derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF) and externally validated this in 2153 patients from the Chronic Heart Failure ESC-guideline based Cardiology practice Quality project (CHECK-HF) registry. In SwedeHF, the median age was 80 [interquartile range 72-86] years, 52% of patients were female and most frequent comorbidities were hypertension (82%), atrial fibrillation (68%), and ischaemic heart disease (48%). Latent class analysis identified five distinct clusters: cluster 1 (10% of patients) were young patients with a low comorbidity burden and the highest proportion of implantable devices; cluster 2 (30%) patients had atrial fibrillation, hypertension without diabetes; cluster 3 (25%) patients were the oldest with many cardiovascular comorbidities and hypertension; cluster 4 (15%) patients had obesity, diabetes and hypertension; and cluster 5 (20%) patients were older with ischaemic heart disease, hypertension and renal failure and were most frequently prescribed diuretics. The clusters were reproduced in the CHECK-HF cohort. Patients in cluster 1 had the best prognosis, while patients in clusters 3 and 5 had the worst age- and sex-adjusted prognosis.
conclusionsFive distinct clusters of HFpEF patients were identified that differed in clinical characteristics, heart failure drug therapy and prognosis. These results confirm the heterogeneity of HFpEF and form a basis for tailoring trial design to individualized drug therapy in HFpEF patients.
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