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Circ Arrhythm Electrophysiol. 2018 Jan;11(1):e005499. doi: 10.1161/CIRCEP.117.005499.

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

Author information

1
From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison. mmkalsch@medicine.wisc.edu.
2
From the Division of Cardiovascular Medicine, Department of Medicine, School of Medicine and Public Health (M.M.K., R.T.K., M.C.T., M.E.F., L.L.E.), Department of Biostatistics and Medical Informatics (C.M., K.A.B., D.L.D., C.D.P.), University of Wisconsin Institute for Clinical and Translational Research (C.M.), and Department of Computer Sciences (C.D.P.), University of Wisconsin-Madison.

Abstract

BACKGROUND:

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.

METHODS AND RESULTS:

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 (P=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; P<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.

CONCLUSIONS:

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.

KEYWORDS:

algorithms; cardiac resynchronization therapy; heart failure; hospitalization; machine learning

PMID:
29326129
PMCID:
PMC5769699
DOI:
10.1161/CIRCEP.117.005499
[Indexed for MEDLINE]
Free PMC Article

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