Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy

Cikes, Maja; Sanchez Martinez, Sergio; Claggett, Brian; Duchateau, Nicolas; Piella Fenoy, Gemma; Butakoff, Constantine; Pouleur, Anne Catherine; Knappe, Dorit; Biering‐Sørensen, Tor; Kutyifa, Valentina; Moss, Arthur; Stein, Kenneth; Solomon, Scott D.; Bijnens, Bart

doi:http://dx.doi.org/10.1002/ejhf.1333

Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy

Citation

Cikes M, Sanchez‐Martinez S, Claggett B, Duchateau N, Piella Fenoy G, Butakoff C, Pouleur AC, Knappe D, Biering‐Sørensen T, Kutyifa V, Moss A, Stein K, Solomon SD, Bijnens B. Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy. Eur J Heart Fail. 2019 Jan;21(1):74-85. DOI: 10.1002/ejhf.1333

Abstract

We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).