Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction
Open accessAbstract
Background—: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories. Methods and Results—: We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0–9.1] even after adjustment for traditional risk factors [ P <0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions—: Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.
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- Authors
- Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Gheorghiade, M., Bonow, R. O., Huang, C.-C., & Deo, R. C.
- Affiliations
- QB3 (US)
- ORCID
- Venue
- Circulation
- Published
- 2015-01-20
- Volume
- 131
- Issue
- 3
- Pages
- 269-279
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC4302027
- OpenAlex
- W1976196376
- Cited by
- 1,157
- References
- 31
- Citations by year
- 2026: 136 · 2025: 103 · 2024: 107 · 2023: 107 · 2022: 118 · 2021: 147 · 2020: 136 · 2019: 86 · 2018: 70 · 2017: 71 · 2016: 49 · 2015: 24 · 2012: 1
- FWCI
- 32.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR000150)
- National Heart, Lung, and Blood Institute (DP2HL123228, R01 HL107557., K08HL098361, R01HL107577, K08 HL093861)
- National Center for Chronic Disease Prevention and Health Promotion (DP2HL123228)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heart failure with preserved ejection fraction, phenomapping, phenotypic classification, phenogroups, clustering, model-based clustering, risk stratification, heart failure hospitalization, patient heterogeneity, hemodynamics
- MeSH terms
- Aged, Female, Follow-Up Studies, Heart Failure, Humans, Male, Middle Aged, Phenotype, Prospective Studies, Stroke Volume, Cohort Studies
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