Association of Machine Learning-Derived Pheno-groupings 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 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 blinded to the study outcome in 1000 patients with stable CAD on 15 transthoracic echocardiographic (TTE) variables. We evaluated associations of cluster membership with HF hospitalization using Cox proportional hazards regression analysis. RESULTS: The echo-derived clusters partitioned subjects into 4 pheno-groupings: 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 levels of cardiopulmonary structural and functional abnormalities. Over 7.1±3.2 years of follow-up, there were198 HF hospitalizations. After multivariable adjustment for traditional cardiovascular risk factors, phenogroup 1 was associated with a nearly 5-fold increased risk (HR 4.8; 95%CI: 2.4–9.5), phenogroup 2 was associated with a nearly 3-fold increased risk (HR 2.7; 95%CI: 1.4–5.0), and phenogroup 3 was associated with a nearly 2-fold increased risk (HR 1.9; 95%CI: 1.0–3.8) of HF hospitalization, relative to phenogroup 4. CONCLUSIONS: TTE variables can be used to classify stable CAD patients into separate pheno-groupings that differentiate cardiopulmonary structural and functional abnormalities, and can predict HF hospitalization, independent of traditional cardiovascular risk factors.