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

Overview

Journal J Am Soc Echocardiogr

Specialty Radiology

Date 2020 Jan 18

PMID 31948711

Citations 8

Authors

Rakesh K Mishra

Geoffrey H Tison

Qizhi Fang

Rebecca Scherzer

Mary A Whooley

Nelson B Schiller

Affiliations

Soon will be listed here.

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 levels of cardiopulmonary structural and functional abnormalities. Over 7.1 ± 3.2 years of follow-up, there were 198 HF hospitalizations. After multivariable adjustment for traditional cardiovascular risk factors, phenogroup 1 was associated with a nearly fivefold increased risk (hazard ratio [HR] = 4.8; 95% CI, 2.4-9.5), phenogroup 2 was associated with a nearly threefold increased risk (HR = 2.7; 95% CI, 1.4-5.0), and phenogroup 3 was associated with a nearly twofold increased risk (HR = 1.9; 95% CI, 1.0-3.8) of HF hospitalization, relative to phenogroup 4.

Conclusions: Transthoracic echocardiographic variables can be used to classify stable CAD patients into separate phenogroupings that differentiate cardiopulmonary structural and functional abnormalities and can predict HF hospitalization, independent of traditional cardiovascular risk factors.

Citing Articles

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Machine Learning Augmented Echocardiography for Diastolic Function Assessment.

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