dc.creator | Chaparro, María | es |
dc.creator | Baston-Rey, Iria | es |
dc.creator | Fernández Salgado, Estela | es |
dc.creator | González García, Javier | es |
dc.creator | Ramos, Laura | es |
dc.creator | Diz Lois Palomares, María Teresa | es |
dc.creator | Argüelles Arias, Federico | es |
dc.creator | Gisbert, Javier P. | es |
dc.date.accessioned | 2023-05-17T13:26:32Z | |
dc.date.available | 2023-05-17T13:26:32Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Chaparro, 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.issn | 2077-0383 | es |
dc.identifier.uri | https://hdl.handle.net/11441/146249 | |
dc.description.abstract | Ustekinumab 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.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Journal of Clinical Medicine (JCM), 11 (15), 4518. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Crohn’s disease | es |
dc.subject | Ustekinumab | es |
dc.subject | Predictive factors | es |
dc.title | Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Medicina | es |
dc.relation.projectID | CM21/00025 | es |
dc.relation.publisherversion | https://www.mdpi.com/2077-0383/11/15/4518 | es |
dc.identifier.doi | 10.3390/jcm11154518 | es |
dc.journaltitle | Journal of Clinical Medicine (JCM) | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 15 | es |
dc.publication.initialPage | 4518 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Instituto de Salud Carlos III | es |