Association of Machine Learning-Derived Phenogroupings of Echocardiographic Variables with Heart Failure in Stable Coronary Artery Disease: The Heart and Soul Study.

ABSTRACT:

Background

Many individual echocardiographic variables have been associated with heart failure (HF) in patients with stable coronary artery disease (CAD), but their combined utility for prediction has not been well studied.

Methods

Unsupervised model-based cluster analysis was performed by researchers blinded to the study outcome in 1,000 patients with stable CAD on 15 transthoracic echocardiographic variables. We evaluated associations of cluster membership with HF hospitalization using Cox proportional hazards regression analysis.

Results

The echo-derived clusters partitioned subjects into four phenogroupings: phenogroup 1 (n = 85) had the highest levels, phenogroups 2 (n = 314) and 3 (n = 205) displayed intermediate levels, and phenogroup 4 (n = 396) had the lowest

SUBMITTER: Mishra RK 

PROVIDER: S-EPMC7357873 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature