Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering.

ABSTRACT: Background: Stratified medicine may enable the development of effective treatments for particular groups of patients with heart failure with preserved ejection fraction (HFpEF); however, the heterogeneity of this syndrome makes it difficult to group patients together by common disease features. The aim of the present study was to find new subgroups of HFpEF using machine learning. Methods: K-means clustering was used to stratify patients with HFpEF. We retrospectively enrolled 350 outpatients with HFpEF. Their clinical characteristics, blood sample test results and hemodynamic parameters assessed by echocardiography, electrocardiography and jugular venous pulse, and clinical outcomes were applied to k-means clustering. The optimal k was detected using Hartigan's rule. Resu

SUBMITTER: Harada D 

PROVIDER: S-EPMC7734143 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature