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

Chaparro, MaríaBaston Rey, IriaFernández Salgado, EstelaGonzález García, JavierRamos, LauraDiz Lois Palomares, María TeresaArgüelles Arias, FedericoIglesias Flores, EvaCabello, MercedesRubio Iturria, SaioaNúñez Ortiz, AndreaCharro, MaraGinard, DanielDueñas Sadornil, CarmenMerino Ochoa, OlgaBusquets, DavidIyo, EduardoGutiérrez Casbas, AnaRamírez de la Piscina, PatriciaBoscá Watts, Marta MaiaArroyo, MaiteGarcía, María JoséHinojosa, EstherGordillo, JordiMartínez Montiel, PilarVelayos Jiménez, BenitoQuílez Ivorra, CristinaVázquez Morón, Juan MaríaHuguet, José MaríaGonzález Lama, YagoMuñagorri Santos, Ana IsabelAmo, Víctor ManuelMartín Arranz, María DoloresBermejo, FernandoMartínez Cadilla, JesúsRubín de Célix, CristinaFradejas Salazar, PaolaLópez San Román, AntonioJiménez, NuriaGarcía López, SantiagoFiguerola, AnnaJiménez, ItxasoMartínez Cerezo, Francisco JoséTaxonera, CarlosVarela, PilarFrancisco, Ruth deMonfort, DavidMolina Arriero, GemaHernández Camba, AlejandroGarcía Alonso, Francisco JavierVan Domselaar, ManuelPajares Villarroya, RamónNúñez, AlejandroRodríguez Moranta, FranciscoMarín Jiménez, IgnacioRobles Alonso, VirginiaMartín Rodríguez, María Del MarCamo Monterde, PatriciaGarcía Tercero, IvánNavarro Llavat, MercedesGarcía, Lara AriasHervías Cruz, DanielKloss, SebastianPassey, AlunNovella, CynthiaVispo, EugeniaBarreiro de Acosta, ManuelGisbert, Javier P.2022-09-122022-09-122022-08-032077-0383https://hdl.handle.net/2445/188898Ustekinumab 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.17 p.application/pdfengcc by (c) Chaparro, María et al., 2022http://creativecommons.org/licenses/by/3.0/es/Malaltia de CrohnFactors de risc en les malaltiesCrohn's diseaseRisk factors in diseasesUsing Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumabinfo:eu-repo/semantics/article2022-08-25info:eu-repo/semantics/openAccess35956133