Phenomapping for novel classification of heart failure with preserved ejection fraction.

ABSTRACT:

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 an

SUBMITTER: Shah SJ 

PROVIDER: S-EPMC4302027 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature