| Resum: |
Background/Objectives: Heart failure with preserved ejection fraction (HFpEF) has emerged as one of the most challenging syndromes in modern cardiology due to its complex pathophysiology, diagnostic ambiguity, and lack of effective targeted therapies. Unlike heart failure with reduced ejection fraction (HFrEF), HFpEF encompasses a highly heterogeneous patient population unified only by a preserved left ventricular ejection fraction (LVEF) ≥ 50%. This broad definition overlooks important biological and clinical differences, leading to inconclusive results in large-scale therapeutic trials and suboptimal patient outcomes. In recent years, advances in data-driven methodologies-such as unsupervised machine learning, cluster analysis, and latent class modeling-have enabled the identification of distinct HFpEF phenotypes. These phenotypes, often defined by demographic, clinical, hemodynamic, and biomarker profiles, exhibit differential prognoses and treatment responses. Methods: This systematic review synthesizes findings from 20 studies published between 2010 and 2025, examining phenotypic classification strategies and their clinical implications. Results: Despite methodological variation, several recurring phenotypes emerge, including metabolic-obese, frail-elderly, atrial-fibrillation-dominant, cardiorenal, and pulmonary hypertension/right-heart phenotypes. Each presents a distinct pathophysiological mechanism and risk profile, highlighting the inadequacy of current one-size-fits-all treatment approaches. The review also explores the prognostic value of phenotypes, the impact of phenotypic variation on treatment efficacy, and the methodological challenges that hinder translation into clinical practice-such as inconsistent input variables, lack of external validation, and limited integration with real-world data. Conclusions: Ultimately, the findings underscore the need for a paradigm shift from ejection fraction-based classification to phenotype-guided management in HFpEF. Embracing a precision medicine framework could enable personalized treatment strategies, improve clinical trial design, and enhance outcomes for this diverse patient population. The review concludes by outlining future directions, including the development of standardized phenotyping algorithms, integration of multi-omic and digital health data, and the implementation of pragmatic, phenotype-stratified clinical trials. |
visitant ::
identificació
(Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
