Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to...

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Bibliographic Details
Published in:Circulation. Arrhythmia and electrophysiology Vol. 11; no. 1; p. e005499
Main Authors: Kalscheur, Matthew M, Kipp, Ryan T, Tattersall, Matthew C, Mei, Chaoqun, Buhr, Kevin A, DeMets, David L, Field, Michael E, Eckhardt, Lee L, Page, C David
Format: Journal Article
Language:English
Published: United States 01.01.2018
Subjects:
Aged
Algorithms
Cardiac Resynchronization Therapy - methods
Decision Making
Deep Learning
Female
Heart Conduction System - physiopathology
Heart Failure - physiopathology
Heart Failure - therapy
Humans
Machine Learning
Male
Middle Aged
Predictive Value of Tests
Stroke Volume - physiology
Ventricular Function, Left - physiology
algorithms
heart failure
hospitalization
machine learning
cardiac resynchronization therapy
ISSN:1941-3084, 1941-3084
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Summary:Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT. Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance ( =0.08). The random forest model produced quartiles of patients with an 8-fold difference in survival between those with the highest and lowest predicted probability for events (hazard ratio, 7.96; <0.0001). The model also discriminated the risk of the composite end point of all-cause mortality or heart failure hospitalization better than subgroups based on bundle branch block morphology and QRS duration. In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.
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ISSN:1941-3084
1941-3084
DOI:10.1161/CIRCEP.117.005499