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We thank Beatriz Garcillan for revision and critical reading of the manuscript. Cristina Rubin is supported by a grant from the Ministerio de Economia y Competitividad (Instituto de Salud Carlos III, Rio Hortega CM21/00025) and co-funded by Fondo Europeo de Desarrollo Regional (FEDER).

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

Publicado en:Journal of Clinical Medicine. 11 (15): 4518- - 2022-08-01 11(15), DOI: 10.3390/jcm11154518

Autores: Chaparro, Maria; Baston-Rey, Iria; Fernandez Salgado, Estela; Gonzalez Garcia, Javier; Ramos, Laura; Diz-Lois Palomares, Maria Teresa; Arguelles-Arias, Federico; Iglesias Flores, Eva; Cabello, Mercedes; Rubio Iturria, Saioa; Nunez Ortiz, Andrea; Charro, Mara; Ginard, Daniel; Duenas Sadornil, Carmen; Merino Ochoa, Olga; Busquets, David; Iyo, Eduardo; Gutierrez Casbas, Ana; Ramirez de la Piscina, Patricia; Maia Bosca-Watts, Marta; Arroyo, Maite; Jose Garcia, Maria; Hinojosa, Esther; Gordillo, Jordi; Martinez Montiel, Pilar; Velayos Jimenez, Benito; Quilez Ivorra, Cristina; Vazquez Moron, Juan Maria; Maria Huguet, Jose; Gonzalez-Lama, Yago; Munagorri Santos, Ana Isabel; Manuel Amo, Victor; Martin Arranz, Maria Dolores; Bermejo, Fernando; Martinez Cadilla, Jesus; Rubin de Celix, Cristina; Fradejas Salazar, Paola; Lopez San Roman, Antonio; Jimenez, Nuria; Garcia-Lopez, Santiago; Figuerola, Anna; Jimenez, Itxaso; Martinez Cerezo, Francisco Jose; Taxonera, Carlos; Varela, Pilar; de Francisco, Ruth; Monfort, David; Molina Arriero, Gema; Hernandez-Camba, Alejandro; Garcia Alonso, Francisco Javier; Van Domselaar, Manuel; Pajares-Villarroya, Ramon; Nunez, Alejandro; Rodriguez Moranta, Francisco; Marin-Jimenez, Ignacio; Robles Alonso, Virginia; Martin Rodriguez, Maria del Mar; Camo-Monterde, Patricia; Garcia Tercero, Ivan; Navarro-Llavat, Mercedes; Arias Garcia, Lara; Hervias Cruz, Daniel; Kloss, Sebastian; Passey, Alun; Novella, Cynthia; Vispo, Eugenia; Barreiro-de Acosta, Manuel; Gisbert, Javier P

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Resumen

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.

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