ArticleESC heart failure2023
Heart failure with preserved ejection fraction phenogroup classification using machine learning.
Article in ESC heart failure, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- CONFIDENT-HFpEF: a machine learning-based risk stratification for mortality and hospitalization using multimodal real-world data.ESC heart failure · 2026Observational
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Clinical Impact of Semaglutide in Patients with Heart Failure and Preserved Ejection Fraction.Journal of clinical medicine · 2026Article
- Precision Medicine in Heart Failure: Integrating Ventricular-Vascular Interaction and Arterial Stiffness into Patient Phenotyping.Journal of clinical medicine · 2026Review
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Comorbidity-Driven Inflammation in HFpEF: Immune Profiling and Therapeutic Targets.Current heart failure reports · 2026Review
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Phenotyping of acute heart failure with preserved ejection fraction: real-world outcomes in a cohort of older patients.Internal medicine journal · 2026Article
- Clinical factors associated with 30-day heart failure readmission in patients with acute decompensated heart failure with preserved ejection fraction.Frontiers in cardiovascular medicine · 2026Article
- Heart Failure in the Modern Era: A Narrative Overview of Recent Research from 2022-2025.Journal of cardiovascular development and disease · 2025Review
- Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction.EClinicalMedicine · 2025Article
- Clinical Usefulness of Right Ventricular-Pulmonary Artery Coupling in Patients with Heart Failure.Diagnostics (Basel, Switzerland) · 2025Review
- Sex differences in the association between nutritional status and in-hospital mortality in HFpEF patients.ESC heart failure · 2025Article
- Development and validation of risk stratification and shared decision-making tool for catheter ablation for atrial fibrillation in patients with heart failure: a multicentre cohort study.EClinicalMedicine · 2025Article
- Neuron-non-neuron electrical coupling networks are involved in chronic stress-induced electrophysiological changes in lateral habenular neurons.The Journal of physiology · 2025Article
- Machine learning in predicting heart failure survival: a review of current models and future prospects.Heart failure reviews · 2025Review
- Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT-HFpEF Study.Journal of the American Heart Association · 2025Observational
- The mitigatory capabilities of exercise on breast cancer chemotherapy-induced cardiotoxicity.Frontiers in cell and developmental biology · 2025Review
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsHeart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome with a poor prognosis. Phenotyping is required to identify subtype-dependent treatment strategies. Phenotypes of Japanese HFpEF patients are not fully elucidated, whose obesity is much less than Western patients. This study aimed to reveal model-based phenomapping using unsupervised machine learning (ML) for HFpEF in Japanese patients. METHODS AND
resultsWe studied 365 patients with HFpEF (left ventricular ejection fraction >50%) as a derivation cohort from the Nara Registry and Analyses for Heart Failure (NARA-HF), which registered patients with hospitalization by acute decompensated HF. We used unsupervised ML with a variational Bayesian-Gaussian mixture model (VBGMM) with common clinical variables. We also performed hierarchical clustering on the derivation cohort. We adopted 230 patients in the Japanese Heart Failure Syndrome with Preserved Ejection Fraction Registry as the validation cohort for VBGMM. The primary endpoint was defined as all-cause death and HF readmission within 5 years. Supervised ML was performed on the composite cohort of derivation and validation. The optimal number of clusters was three because of the probable distribution of VBGMM and the minimum Bayesian information criterion, and we stratified HFpEF into three phenogroups. Phenogroup 1 (n = 125) was older (mean age 78.9 ± 9.1 years) and predominantly male (57.6%), with the worst kidney function (mean estimated glomerular filtration rate 28.5 ± 9.7 mL/min/1.73 m
conclusionsML could successfully stratify Japanese HFpEF patients into three phenogroups (atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups).
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