Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

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 determin...

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Autores: Chaparro, María, Baston-Rey, Iria, Fernández Salgado, Estela, González García, Javier, Ramos, Laura, Diz Lois Palomares, María Teresa, Argüelles Arias, Federico, Gisbert, Javier P.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/146249
Acceso en línea:https://hdl.handle.net/11441/146249
https://doi.org/10.3390/jcm11154518
Access Level:acceso abierto
Palabra clave:Crohn’s disease
Ustekinumab
Predictive factors
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spelling Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on UstekinumabChaparro, MaríaBaston-Rey, IriaFernández Salgado, EstelaGonzález García, JavierRamos, LauraDiz Lois Palomares, María TeresaArgüelles Arias, FedericoGisbert, Javier P.Crohn’s diseaseUstekinumabPredictive factorsUstekinumab 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..MDPIMedicinaMinisterio de Economía y Competitividad (MINECO). EspañaInstituto de Salud Carlos III2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/146249https://doi.org/10.3390/jcm11154518reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Clinical Medicine (JCM), 11 (15), 4518.CM21/00025https://www.mdpi.com/2077-0383/11/15/4518info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1462492026-06-17T12:51:07Z
dc.title.none.fl_str_mv Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
spellingShingle Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
Chaparro, María
Crohn’s disease
Ustekinumab
Predictive factors
title_short Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_full Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_fullStr Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_full_unstemmed Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_sort Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
dc.creator.none.fl_str_mv Chaparro, María
Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz Lois Palomares, María Teresa
Argüelles Arias, Federico
Gisbert, Javier P.
author Chaparro, María
author_facet Chaparro, María
Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz Lois Palomares, María Teresa
Argüelles Arias, Federico
Gisbert, Javier P.
author_role author
author2 Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz Lois Palomares, María Teresa
Argüelles Arias, Federico
Gisbert, Javier P.
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Medicina
Ministerio de Economía y Competitividad (MINECO). España
Instituto de Salud Carlos III
dc.subject.none.fl_str_mv Crohn’s disease
Ustekinumab
Predictive factors
topic Crohn’s disease
Ustekinumab
Predictive factors
description 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..
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/146249
https://doi.org/10.3390/jcm11154518
url https://hdl.handle.net/11441/146249
https://doi.org/10.3390/jcm11154518
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Clinical Medicine (JCM), 11 (15), 4518.
CM21/00025
https://www.mdpi.com/2077-0383/11/15/4518
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
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