Machine learning algorithm predicts cardiac resynchronization therapy outcomes: lessons from the COMPANION trial
MM Kalscheur, RT Kipp, MC Tattersall… - Circulation …, 2018 - Am Heart Assoc
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 …
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 …
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