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

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To access the full text documents, please follow this link: http://hdl.handle.net/10230/36970

Title:	Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy
Author:	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
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).
Abstract:	The work of S. Sanchez‐Martinez was supported by a fellowship from ‘la Caixa’ Banking Foundation. C. Butakoff was supported by a grant from the Fundació La Marató de TV3 (n. 20154031), Spain. N. Duchateau was supported by ‘Programme Avenir Lyon Saint‐Etienne’ (PALSE‐IMPULSION‐2016, Lyon, France). MADIT‐CRT was sponsored by Boston Scientific, while no additional funding was provided for this analysis. This study was also partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014‐52923‐R; Maria de Maeztu Units of Excellence Programme ‐ MDM‐2015‐0502) and FEDER.
Subject(s):	-Machine learning -Heart failure -Personalized medicine -Echocardiography -Cardiac resynchronization therapy
Rights:	info:eu-repo/semantics/embargoedAccess This is the peer reviewed version of the following article: 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, which has been published in final form at http://dx.doi.org/10.1002/ejhf.1333. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Document type:	Article Article - Accepted version
Published by:	Wiley
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