Phenomapping for novel classification of heart failure with preserved ejection fraction.
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 ca...
| Publicado en: | Circulation Vol. 131; no. 3; pp. 269 - 280 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
1/20/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103875906&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103875906 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00097322 1FV jtl: Circulation issn: 00097322 maglogo: N pubinfo: dt: 1/20/2015 vid: 131 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 103875906 NLM25398313 2012876123 10.1161/CIRCULATIONAHA.114.010637 NLM25398313 PMC4302027 103875906 ppf: 269 ppct: 11 formats: tig: atl: Phenomapping for novel classification of heart failure with preserved ejection fraction. aug: au: 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 affil: From the Division of Cardiology, Department of Medicine (S.J.S., D.H.K., S.S., M.A.B., C.W.Y., M.G., R.O.B.), Feinberg Cardiovascular Research Institute (S.J.S.), and Center for Cardiovascular Innovation (M.G., R.O.B.), Northwestern University Feinberg School of Medicine, Chicago, IL; Zilber School of Public Health, University of Wisconsin, Milwaukee (C.-C.H.); and Division of Cardiology, Department of Medicine, Institute for Human Genetics, California Institute for Quantitative Biosciences, and Cardiovascular Research Institute, University of California, San Francisco (R.C.D.). sanjiv.shah@northwestern.edu rahul.deo@ucsf.edu. sug: subj: Heart Failure Diagnosis Heart Failure Physiopathology Phenotype Stroke Volume Physiology Aged Prospective Studies Female Heart Failure Blood Human Male Middle Age Aged: 65+ years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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