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

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

Document Type: Article

Version: Accepted version

Date: 2019

Name: Bijnens_ejhf_mach ...

Size: 2.397Mb

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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

Permanent link: http://hdl.handle.net/10230/36970

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).