Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
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Published:2022-08-03
Issue:15
Volume:11
Page:4518
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ISSN:2077-0383
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Container-title:Journal of Clinical Medicine
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language:en
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Short-container-title:JCM
Author:
Chaparro MaríaORCID, Baston-Rey Iria, Fernández Salgado Estela, González García Javier, Ramos Laura, Diz-Lois Palomares María Teresa, Argüelles-Arias FedericoORCID, Iglesias Flores Eva, Cabello Mercedes, Rubio Iturria Saioa, Núñez Ortiz Andrea, Charro Mara, Ginard Daniel, Dueñas Sadornil Carmen, Merino Ochoa Olga, Busquets David, Iyo Eduardo, Gutiérrez Casbas Ana, Ramírez de la Piscina Patricia, Boscá-Watts Marta Maia, Arroyo MaiteORCID, García María JoséORCID, Hinojosa Esther, Gordillo Jordi, Martínez Montiel Pilar, Velayos Jiménez Benito, Quílez Ivorra Cristina, Vázquez Morón Juan María, Huguet José MaríaORCID, González-Lama Yago, Muñagorri Santos Ana Isabel, Amo Víctor Manuel, Martín Arranz María Dolores, Bermejo Fernando, Martínez Cadilla JesúsORCID, Rubín de Célix CristinaORCID, Fradejas Salazar Paola, López San Román Antonio, Jiménez Nuria, García-López SantiagoORCID, Figuerola Anna, Jiménez Itxaso, Martínez Cerezo Francisco José, Taxonera Carlos, Varela Pilar, de Francisco RuthORCID, Monfort David, Molina Arriero Gema, Hernández-Camba AlejandroORCID, García Alonso Francisco Javier, Van Domselaar Manuel, Pajares-Villarroya Ramón, Núñez Alejandro, Rodríguez Moranta Francisco, Marín-Jiménez Ignacio, Robles Alonso Virginia, Martín Rodríguez María del Mar, Camo-Monterde Patricia, García Tercero Iván, Navarro-Llavat MercedesORCID, García Lara Arias, Hervías Cruz Daniel, Kloss Sebastian, Passey Alun, Novella Cynthia, Vispo Eugenia, Barreiro-de Acosta ManuelORCID, Gisbert Javier P.ORCID
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.
Funder
Janssen Cilag Spain
Cited by
3 articles.
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