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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.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.issn 1879-0844
dc.identifier.uri http://hdl.handle.net/10230/36970
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.language.iso eng
dc.publisher Wiley
dc.relation.ispartof European Journal of Heart Failure. 2019 Jan;21(1):74-85.
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.title Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://dx.doi.org/10.1002/ejhf.1333
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.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2014‐52923‐R
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.type.version info:eu-repo/semantics/acceptedVersion
dc.embargo.liftdate 2019-10-17
dc.date.embargoEnd info:eu-repo/date/embargoEnd/2019-10-17

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