4851236a055c062248866fd782b418e0 jcm-14-04820_2.pdf dd32627af1066c2614f382076d1022b7f948f4ce jcm-14-04820_2.pdf dd3413d5d68b6bcf30aa3353170b26f46e0ee93c65fab8f22a5bf241ea128bcd jcm-14-04820_2.pdf Title: Heterogeneity in Heart Failure with Preserved Ejection Fraction: A Systematic Review of Phenotypic Classifications and Clinical Implications Subject: 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. Keywords: heart failure with preserved ejection fraction; phenotyping; machine learning; precision medicine; cardiovascular heterogeneity Author: Francisco Epelde Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Jul 8 10:39:52 2025 CEST ModDate: Tue Jul 8 10:44:55 2025 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 17 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 534386 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- SSXPJJ+VnURWPalladioL Type 1 Custom yes yes yes 10 0 TNHXQC+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 XMSSBC+URWPalladioL-Bold Type 1 Custom yes yes yes 22 0 IDCTRV+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 PDTLUH+CMSY10 Type 1 Builtin yes yes yes 32 0 BCDJEE+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 70 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 78 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 82 0 BCDLEE+Calibri TrueType WinAnsi yes yes no 85 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-06-02 04:07:35 CEST RepresentationInformation: jcm-14-04820_2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-06-01 14:32:29 CEST Size: 534386 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 376 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Methods Destination: section.2 Children: Item: Title: Study Design Destination: subsection.2.1 Item: Title: Eligibility Criteria Destination: subsection.2.2 Item: Title: Search Strategy Destination: subsection.2.3 Item: Title: Study Selection Destination: subsection.2.4 Item: Title: Data Extraction and Synthesis Destination: subsection.2.5 Item: Title: PRISMA Compliance Destination: subsection.2.6 Item: Title: Results Destination: section.3 Children: Item: Title: Study Selection Destination: subsection.3.1 Item: Title: Phenotyping Methodologies Destination: subsection.3.2 Item: Title: Recurrent Phenotypic Patterns Destination: subsection.3.3 Item: Title: Prognostic Implications Destination: subsection.3.4 Item: Title: Methodological Quality Destination: subsection.3.5 Item: Title: Discussion Destination: section.4 Children: Item: Title: Clinical and Pathophysiological Insights from Recurrent Phenotypes Destination: subsection.4.1 Item: Title: Methodological Challenges in Phenotyping Destination: subsection.4.2 Item: Title: Implications for Clinical Practice and Therapeutics Destination: subsection.4.3 Item: Title: Future Directions and the Promise of Precision Medicine Destination: subsection.4.4 Item: Title: Conclusions and Future Directions Destination: section.5 Children: Item: Title: Future Directions Destination: subsection.5.1 Children: Item: Title: Development of Standardized, Reproducible Phenotyping Algorithms Destination: subsubsection.5.1.1 Item: Title: Prospective Validation of Phenotypes in Diverse Cohorts Destination: subsubsection.5.1.2 Item: Title: Incorporation of Multi-Omic and Digital Health Data Destination: subsubsection.5.1.3 Item: Title: Design of Phenotype-Stratified Clinical Trials Destination: subsubsection.5.1.4 Item: Title: Implementation of Clinical Tools for Phenotype Identification Destination: subsubsection.5.1.5 Item: Title: Understanding Phenotype Evolution and Transitions Destination: subsubsection.5.1.6 Item: Title: Integration of Patient-Centered Outcomes and Quality of Life Destination: subsubsection.5.1.7 Item: Title: References Destination: section.6 Info: Title: Heterogeneity in Heart Failure with Preserved Ejection Fraction: A Systematic Review of Phenotypic Classifications and Clinical Implications Author: Francisco Epelde Subject: 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. 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