학술논문

Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
Document Type
article
Author
María ChaparroIria Baston-ReyEstela Fernández SalgadoJavier González GarcíaLaura RamosMaría Teresa Diz-Lois PalomaresFederico Argüelles-AriasEva Iglesias FloresMercedes CabelloSaioa Rubio IturriaAndrea Núñez OrtizMara CharroDaniel GinardCarmen Dueñas SadornilOlga Merino OchoaDavid BusquetsEduardo IyoAna Gutiérrez CasbasPatricia Ramírez de la PiscinaMarta Maia Boscá-WattsMaite ArroyoMaría José GarcíaEsther HinojosaJordi GordilloPilar Martínez MontielBenito Velayos JiménezCristina Quílez IvorraJuan María Vázquez MorónJosé María HuguetYago González-LamaAna Isabel Muñagorri SantosVíctor Manuel AmoMaría Dolores Martín ArranzFernando BermejoJesús Martínez CadillaCristina Rubín de CélixPaola Fradejas SalazarAntonio López San RománNuria JiménezSantiago García-LópezAnna FiguerolaItxaso JiménezFrancisco José Martínez CerezoCarlos TaxoneraPilar VarelaRuth de FranciscoDavid MonfortGema Molina ArrieroAlejandro Hernández-CambaFrancisco Javier García AlonsoManuel Van DomselaarRamón Pajares-VillarroyaAlejandro NúñezFrancisco Rodríguez MorantaIgnacio Marín-JiménezVirginia Robles AlonsoMaría del Mar Martín RodríguezPatricia Camo-MonterdeIván García TerceroMercedes Navarro-LlavatLara Arias GarcíaDaniel Hervías CruzSebastian KlossAlun PasseyCynthia NovellaEugenia VispoManuel Barreiro-de AcostaJavier P. Gisbert
Source
Journal of Clinical Medicine, Vol 11, Iss 4518, p 4518 (2022)
Subject
Crohn’s Disease
ustekinumab
predictive factors
Medicine
Language
English
ISSN
2077-0383
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.