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dc.creatorChaparro, Maríaes
dc.creatorBaston-Rey, Iriaes
dc.creatorFernández Salgado, Estelaes
dc.creatorGonzález García, Javieres
dc.creatorRamos, Lauraes
dc.creatorDiz Lois Palomares, María Teresaes
dc.creatorArgüelles Arias, Federicoes
dc.creatorGisbert, Javier P.es
dc.date.accessioned2023-05-17T13:26:32Z
dc.date.available2023-05-17T13:26:32Z
dc.date.issued2022
dc.identifier.citationChaparro, M., Baston-Rey, I., Fernández Salgado, E., González García, J., Ramos, L., Diz Lois Palomares, M.T.,...,Gisbert, J.P. (2022). Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab. Journal of Clinical Medicine (JCM), 11 (15), 4518. https://doi.org/10.3390/jcm11154518.
dc.identifier.issn2077-0383es
dc.identifier.urihttps://hdl.handle.net/11441/146249
dc.description.abstractUstekinumab has shown efficacy in Crohn’s Disease (CD) patients. To identify patient profiles of those who benefit the most from this treatment would help to position this drug in the therapeutic paradigm of CD and generate hypotheses for future trials. The objective of this analysis was to determine whether baseline patient characteristics are predictive of remission and the drug durability of ustekinumab, and whether its positioning with respect to prior use of biologics has a significant effect after correcting for disease severity and phenotype at baseline using interpretable machine learning. Patients’ data from SUSTAIN, a retrospective multicenter single-arm cohort study, were used. Disease phenotype, baseline laboratory data, and prior treatment characteristics were documented. Clinical remission was defined as the Harvey Bradshaw Index ≤ 4 and was tracked longitudinally. Drug durability was defined as the time until a patient discontinued treatment. A total of 439 participants from 60 centers were included and a total of 20 baseline covariates considered. Less exposure to previous biologics had a positive effect on remission, even after controlling for baseline disease severity using a non-linear, additive, multivariable model. Additionally, age, body mass index, and fecal calprotectin at baseline were found to be statistically significant as independent negative risk factors for both remission and drug survival, with further risk factors identified for remission..es
dc.formatapplication/pdfes
dc.format.extent17 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofJournal of Clinical Medicine (JCM), 11 (15), 4518.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCrohn’s diseasees
dc.subjectUstekinumabes
dc.subjectPredictive factorses
dc.titleUsing Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumabes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Medicinaes
dc.relation.projectIDCM21/00025es
dc.relation.publisherversionhttps://www.mdpi.com/2077-0383/11/15/4518es
dc.identifier.doi10.3390/jcm11154518es
dc.journaltitleJournal of Clinical Medicine (JCM)es
dc.publication.volumen11es
dc.publication.issue15es
dc.publication.initialPage4518es
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes
dc.contributor.funderInstituto de Salud Carlos IIIes

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