2026-04-17T02:45:03Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/86512025-10-24T10:29:46Zcom_2072_378070com_2072_378040col_2072_378092

Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab Fernández Salgado, Estela Robles Alonso, Virginia Chaparro, María Baston Rey, Iria González García, Javier Ramos, Laura Diz-Lois Palomares, Mª Teresa Crohn, Malaltia de - Tractament Antiinflamatoris - Ús terapèutic Aprenentatge automàtic INFORMATION SCIENCE::Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Machine Learning DISEASES::Digestive System Diseases::Gastrointestinal Diseases::Gastroenteritis::Inflammatory Bowel Diseases::Crohn Disease Other subheadings::Other subheadings::Other subheadings::/drug therapy CHEMICALS AND DRUGS::Chemical Actions and Uses::Pharmacologic Actions::Therapeutic Uses::Anti-Inflammatory Agents CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::aprendizaje automático ENFERMEDADES::enfermedades del sistema digestivo::enfermedades gastrointestinales::gastroenteritis::enfermedad inflamatoria intestinal::enfermedad de Crohn Otros calificadores::Otros calificadores::Otros calificadores::/farmacoterapia COMPUESTOS QUÍMICOS Y DROGAS::acciones y usos químicos::acciones farmacológicas::usos terapéuticos::antiinflamatorios Malaltia de Crohn; Factors predictius; Ustekinumab Enfermedad de Crohn; Factores predictivos; Ustekinumab Crohn’s disease; Predictive factors; 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 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. This work was supported by Janssen-Cilag Spain. This sponsor had a partial role in study design, analysis, and interpretation of data. 2025-10-24T10:29:46Z 2025-10-24T10:29:46Z 2025-10-24T10:29:46Z 2022-12-12T13:25:18Z 2022-12-12T13:25:18Z 2022-08-03 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/8651 Journal of Clinical Medicine;11(15) https://doi.org/10.3390/jcm11154518 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess MDPI Scientia