Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population.

ABSTRACT:

Objective

Identifying individuals with subclinical cardiovascular (CV) disease could improve monitoring and risk stratification. While peak left ventricular (LV) systolic strain has emerged as a strong prognostic factor, few studies have analyzed the whole temporal profiles of the deformation curves during the complete cardiac cycle. Therefore, in this longitudinal study, we applied an unsupervised machine learning approach based on time-series-derived features from the LV strain curve to identify distinct strain phenogroups that might be related to the risk of adverse cardiovascular events in the general population.

Method

We prospectively studied 1,185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic dat

SUBMITTER: Ntalianis E 

PROVIDER: S-EPMC10720328 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature