Artificial intelligence-enabled deep clinical phenotyping in heart failure with preserved ejection fraction: from patient subtypes to precision care.
- 1Department of Internal Medicine, Parc Taulí Hospital Universitari, Sabadell, Spain.
- 2Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain.
- 3Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome defined by shared manifestations but produced by diverse combinations of cardiometabolic, vascular, inflammatory, renal, pulmonary, atrial, skeletal-muscle, and aging-related mechanisms. This heterogeneity limits the value of classifications based only on left ventricular ejection fraction and helps explain why many mechanistically plausible interventions have produced neutral average results. Deep phenotyping can move the field from descriptive heterogeneity toward biologically coherent and clinically actionable subtypes by integrating clinical trajectories, advanced imaging, exercise and invasive hemodynamics, circulating biomarkers, multiomics, and digital physiology. Artificial intelligence (AI) offers methods to combine these high-dimensional data, but statistical clusters should not be equated with disease endotypes. A useful subtype must be reproducible across cohorts, anchored to plausible biology, associated with a distinct clinical course, and able to improve diagnosis, therapeutic selection, or clinical-trial design. This Review examines the translational continuum from experimental HFpEF models to human patient subtypes, summarizes the main deep-phenotyping domains and AI approaches, and proposes seven clinically interpretable subtype families: cardiometabolic-inflammatory, hypertensive-vascular aging, atrial myopathy, pulmonary vascular-right ventricular, cardiorenal-congestive, peripheral-frailty, and specific etiologic phenocopies. Current evidence supports a layered treatment strategy in which broadly effective therapies, particularly sodium-glucose cotransporter 2 inhibitors, are combined with phenotype-directed management of obesity, diabetes, mineralocorticoid receptor activation, atrial fibrillation, congestion, pulmonary vascular disease, and physical deconditioning. The central next step is prospective validation of dynamic subtype assignments in external populations and phenotype-enriched trials. Phenogroup labels should therefore be regarded as pragmatic, transitional proxies for dominant mechanisms when those mechanisms cannot yet be measured directly at the individual-patient level; their value lies in improving decisions while the field moves toward fully individualized mechanism-based treatment. Deep phenotyping will be clinically valuable only when it changes a decision and improves an outcome, rather than merely increasing descriptive complexity.
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BibTeX
@article{epelde2026artificial,
title = {{Artificial intelligence-enabled deep clinical phenotyping in heart failure with preserved ejection fraction: from patient subtypes to precision care.}},
author = {Francisco Epelde},
journal = {Frontiers in cardiovascular medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1956165},
url = {https://europepmc.org/article/MED/42903401},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence-enabled deep clinical phenotyping in heart failure with preserved ejection fraction: from patient subtypes to precision care.
AU - Francisco Epelde
PY - 2026
DA - 2026/10/06
JO - Frontiers in cardiovascular medicine
DO - 10.3389/fcvm.2026.1956165
UR - https://europepmc.org/article/MED/42903401
AB - Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome defined by shared manifestations but produced by diverse combinations of cardiometabolic, vascular, inflammatory, renal, pulmonary, atrial, skeletal-muscle, and aging-related mechanisms. This heterogeneity limits the value of classifications based only on left ventricular ejection fraction and helps explain why many mechanistically plausible interventions have produced neutral average results. Deep phenotyping can move the field from descriptive heterogeneity toward biologically coherent and clinically actionable subtypes by integrating clinical trajectories, advanced imaging, exercise and invasive hemodynamics, circulating biomarkers, multiomics, and digital physiology. Artificial intelligence (AI) offers methods to combine these high-dimensional data, but statistical clusters should not be equated with disease endotypes. A useful subtype must be reproducible across cohorts, anchored to plausible biology, associated with a distinct clinical course, and able to improve diagnosis, therapeutic selection, or clinical-trial design. This Review examines the translational continuum from experimental HFpEF models to human patient subtypes, summarizes the main deep-phenotyping domains and AI approaches, and proposes seven clinically interpretable subtype families: cardiometabolic-inflammatory, hypertensive-vascular aging, atrial myopathy, pulmonary vascular-right ventricular, cardiorenal-congestive, peripheral-frailty, and specific etiologic phenocopies. Current evidence supports a layered treatment strategy in which broadly effective therapies, particularly sodium-glucose cotransporter 2 inhibitors, are combined with phenotype-directed management of obesity, diabetes, mineralocorticoid receptor activation, atrial fibrillation, congestion, pulmonary vascular disease, and physical deconditioning. The central next step is prospective validation of dynamic subtype assignments in external populations and phenotype-enriched trials. Phenogroup labels should therefore be regarded as pragmatic, transitional proxies for dominant mechanisms when those mechanisms cannot yet be measured directly at the individual-patient level; their value lies in improving decisions while the field moves toward fully individualized mechanism-based treatment. Deep phenotyping will be clinically valuable only when it changes a decision and improves an outcome, rather than merely increasing descriptive complexity.
SN - 2297-055X
ER -