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

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• dc.contributor.author Cikes, Maja
• dc.contributor.author Sanchez Martinez, Sergio
• dc.contributor.author Claggett, Brian
• dc.contributor.author Duchateau, Nicolas
• dc.contributor.author Piella Fenoy, Gemma
• dc.contributor.author Butakoff, Constantine
• dc.contributor.author Pouleur, Anne Catherine
• dc.contributor.author Knappe, Dorit
• dc.contributor.author Biering‐Sørensen, Tor
• dc.contributor.author Kutyifa, Valentina
• dc.contributor.author Moss, Arthur
• dc.contributor.author Stein, Kenneth
• dc.contributor.author Solomon, Scott D.
• dc.contributor.author Bijnens, Bart
• dc.date.accessioned 2019-03-26T11:02:05Z
• dc.date.issued 2019
• dc.description.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).
• dc.description.sponsorship 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.
• dc.format.mimetype application/pdf
• dc.identifier.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
• dc.identifier.doi http://dx.doi.org/10.1002/ejhf.1333
• dc.identifier.issn 1879-0844
• dc.identifier.uri http://hdl.handle.net/10230/36970
• dc.language.iso eng
• dc.publisher Wiley
• dc.relation.ispartof European Journal of Heart Failure. 2019 Jan;21(1):74-85
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2014‐52923‐R
• dc.rights 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.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Machine learning
• dc.subject.keyword Heart failure
• dc.subject.keyword Personalized medicine
• dc.subject.keyword Echocardiography
• dc.subject.keyword Cardiac resynchronization therapy
• dc.title Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion