Phenomapping for Novel Classification of Heart Failure with Preserved Ejection Fraction

Details

• Alternative Title:
  Circulation
• Personal Author:
  Shah, Sanjiv J. ; Katz, Daniel H. ; Selvaraj, Senthil ; Burke, Michael A. ; Yancy, Clyde W. ; Gheorghiade, Mihai ; Bonow, Robert O. ; Huang, Chiang-Ching ; Deo, Rahul C.
• Description:
  Introduction
  
  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 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, 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 comprising a novel classification of HFpEF. All phenomapping analyses were performed 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 African-American, 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 (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio 4.2, 95% CI 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107).
  
  Conclusions
  
  Phenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.
  
  
• Subjects:
  Aged Article Cluster Analysis Cohort Studies Diastolic Heart Failure Echocardiography Female Follow-Up Studies Heart Failure Humans Male Middle Aged Outcomes Phenotype Principal Components Analysis Prospective Studies Stroke Volume

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• Source:
  Circulation. 131(3):269-279.
• Pubmed ID:
  25398313
• Pubmed Central ID:
  PMC4302027
• Document Type:
  Journal Article
• Funding:
  K08 HL098361/HL/NHLBI NIH HHS/United States ; UL1 TR000150/TR/NCATS NIH HHS/United States ; R01 HL107557/HL/NHLBI NIH HHS/United States ; R01 HL107577/HL/NHLBI NIH HHS/United States ; K08 HL093861/HL/NHLBI NIH HHS/United States ; DP2HL123228/DP/NCCDPHP CDC HHS/United States ; DP2 HL123228/HL/NHLBI NIH HHS/United States
• Volume:
  131
• Issue:
  3
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:581a662a96109d471e0ba2317331a3c58dcd3a7acfd3376ec4b867ced7a0773c
• Download URL:
  https://stacks.cdc.gov/view/cdc/30254/cdc_30254_DS1.pdf
• File Type:
  [PDF - 1.40 MB ]