ReviewCardiovascular research2023
Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions.
Review in Cardiovascular research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 52 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed.
- Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.JCO clinical cancer informatics · 2024Trial
- Longitudinal phenotyping of heart failure with preserved ejection fraction identifies early- and end-stage disease states.Open heart · 2026Article
- Contemporary medical therapy for heart failure with mildly reduced or preserved ejection fraction.Heart failure reviews · 2026Review
- Determinants of Abnormal Pulmonary Vasodilatory Response With Exercise in HFpEF: Pulmonary Vascular-Left Atrial Axis Abnormalities.Comprehensive Physiology · 2026Article
- HFpEF and MASLD: converging mechanisms and clinical implications.Nature reviews. Cardiology · 2026Review
- Review
- Macrophage-Dependent Intercellular Crosstalk in Multiphenotypic Heart Failure With Preserved Ejection Fraction.Journal of the American Heart Association · 2026Review
- CONFIDENT-HFpEF: a machine learning-based risk stratification for mortality and hospitalization using multimodal real-world data.ESC heart failure · 2026Observational
- Inflammation, metabolism, and aging in heart failure with preserved ejection fraction: Mechanisms and treatment perspectives.Journal of translational internal medicine · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Hypertension Management: Implications for Quality Improvement and Prevention of End-Organ Damage.Life (Basel, Switzerland) · 2026Review
- Phenotyping of acute heart failure with preserved ejection fraction: real-world outcomes in a cohort of older patients.Internal medicine journal · 2026Article
- What Exactly Is Cardiometabolic HFpEF: A Phenotype or an Endotype?Circulation. Heart failure · 2026Article
- Phenomapping in Heart Failure With Reduced Ejection Fraction to Identify Subpopulations With High Residual Risk: A VICTORIA Substudy.Circulation. Heart failure · 2026Article
- Addressing Unmet Needs in Heart Failure with Preserved Ejection Fraction: Multi-Omics Approaches to Therapeutic Discovery.International journal of molecular sciences · 2026Review
- Rationale and Design of the HeartShare/AMP-HF Deep Phenotyping Study to Improve Understanding of Heart Failure With Preserved Ejection Fraction.JACC. Heart failure · 2025Review
- Wnt5a-mediated Adipo-Cardiac Interorgan Communication in HFpEF.bioRxiv : the preprint server for biology · 2025Article
- Review
- Cardiac Magnetic Resonance Imaging in Heart Failure With Preserved Ejection Fraction.Circulation. Cardiovascular imaging · 2025Review
- Microvascular dysfunction across organs in heart failure with preserved ejection fraction: the PROSE-HFpEF case-control study.Cardiovascular diabetology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous entity with complex pathophysiology and manifestations. Phenomapping is the process of applying statistical learning techniques to patient data to identify distinct subgroups based on patterns in the data. Phenomapping has emerged as a technique with potential to improve the understanding of different HFpEF phenotypes. Phenomapping efforts have been increasing in HFpEF over the past several years using a variety of data sources, clinical variables, and statistical techniques. This review summarizes methodologies and key takeaways from these studies, including consistent discriminating factors and conserved HFpEF phenotypes. We argue that phenomapping results to date have had limited implications for clinical care and clinical trials, given that the phenotypes, as currently described, are not reliably identified in each study population and may have significant overlap. We review the inherent limitations of aggregating and utilizing phenomapping results. Lastly, we discuss potential future directions, including using phenomapping to optimize the likelihood of clinical trial success or to drive discovery in mechanisms of the disease process of HFpEF.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.