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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

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 determin...

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Autores principales: Chaparro, María, Baston-Rey, Iria, Fernández Salgado, Estela, González García, Javier, Ramos, Laura, Diz-Lois Palomares, María Teresa, Argüelles-Arias, Federico, 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, Maite, García, María José, Hinojosa, Esther, Gordillo, Jordi, Martínez Montiel, Pilar, Velayos Jiménez, Benito, Quílez Ivorra, Cristina, Vázquez Morón, Juan María, Huguet, José María, González-Lama, Yago, Muñagorri Santos, Ana Isabel, Amo, Víctor Manuel, Martín Arranz, María Dolores, Bermejo, Fernando, Martínez Cadilla, Jesús, Rubín de Célix, Cristina, Fradejas Salazar, Paola, López San Román, Antonio, Jiménez, Nuria, García-López, Santiago, Figuerola, Anna, Jiménez, Itxaso, Martínez Cerezo, Francisco José, Taxonera, Carlos, Varela, Pilar, de Francisco, Ruth, Monfort, David, Molina Arriero, Gema, Hernández-Camba, Alejandro, 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, Mercedes, García, Lara Arias, Hervías Cruz, Daniel, Kloss, Sebastian, Passey, Alun, Novella, Cynthia, Vispo, Eugenia, Barreiro-de Acosta, Manuel, Gisbert, Javier P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369748/
https://www.ncbi.nlm.nih.gov/pubmed/35956133
http://dx.doi.org/10.3390/jcm11154518
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author Chaparro, María
Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz-Lois Palomares, María Teresa
Argüelles-Arias, Federico
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, Maite
García, María José
Hinojosa, Esther
Gordillo, Jordi
Martínez Montiel, Pilar
Velayos Jiménez, Benito
Quílez Ivorra, Cristina
Vázquez Morón, Juan María
Huguet, José María
González-Lama, Yago
Muñagorri Santos, Ana Isabel
Amo, Víctor Manuel
Martín Arranz, María Dolores
Bermejo, Fernando
Martínez Cadilla, Jesús
Rubín de Célix, Cristina
Fradejas Salazar, Paola
López San Román, Antonio
Jiménez, Nuria
García-López, Santiago
Figuerola, Anna
Jiménez, Itxaso
Martínez Cerezo, Francisco José
Taxonera, Carlos
Varela, Pilar
de Francisco, Ruth
Monfort, David
Molina Arriero, Gema
Hernández-Camba, Alejandro
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, Mercedes
García, Lara Arias
Hervías Cruz, Daniel
Kloss, Sebastian
Passey, Alun
Novella, Cynthia
Vispo, Eugenia
Barreiro-de Acosta, Manuel
Gisbert, Javier P.
author_facet Chaparro, María
Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz-Lois Palomares, María Teresa
Argüelles-Arias, Federico
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, Maite
García, María José
Hinojosa, Esther
Gordillo, Jordi
Martínez Montiel, Pilar
Velayos Jiménez, Benito
Quílez Ivorra, Cristina
Vázquez Morón, Juan María
Huguet, José María
González-Lama, Yago
Muñagorri Santos, Ana Isabel
Amo, Víctor Manuel
Martín Arranz, María Dolores
Bermejo, Fernando
Martínez Cadilla, Jesús
Rubín de Célix, Cristina
Fradejas Salazar, Paola
López San Román, Antonio
Jiménez, Nuria
García-López, Santiago
Figuerola, Anna
Jiménez, Itxaso
Martínez Cerezo, Francisco José
Taxonera, Carlos
Varela, Pilar
de Francisco, Ruth
Monfort, David
Molina Arriero, Gema
Hernández-Camba, Alejandro
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, Mercedes
García, Lara Arias
Hervías Cruz, Daniel
Kloss, Sebastian
Passey, Alun
Novella, Cynthia
Vispo, Eugenia
Barreiro-de Acosta, Manuel
Gisbert, Javier P.
author_sort Chaparro, María
collection PubMed
description 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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spelling pubmed-93697482022-08-12 Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab Chaparro, María Baston-Rey, Iria Fernández Salgado, Estela González García, Javier Ramos, Laura Diz-Lois Palomares, María Teresa Argüelles-Arias, Federico 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, Maite García, María José Hinojosa, Esther Gordillo, Jordi Martínez Montiel, Pilar Velayos Jiménez, Benito Quílez Ivorra, Cristina Vázquez Morón, Juan María Huguet, José María González-Lama, Yago Muñagorri Santos, Ana Isabel Amo, Víctor Manuel Martín Arranz, María Dolores Bermejo, Fernando Martínez Cadilla, Jesús Rubín de Célix, Cristina Fradejas Salazar, Paola López San Román, Antonio Jiménez, Nuria García-López, Santiago Figuerola, Anna Jiménez, Itxaso Martínez Cerezo, Francisco José Taxonera, Carlos Varela, Pilar de Francisco, Ruth Monfort, David Molina Arriero, Gema Hernández-Camba, Alejandro 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, Mercedes García, Lara Arias Hervías Cruz, Daniel Kloss, Sebastian Passey, Alun Novella, Cynthia Vispo, Eugenia Barreiro-de Acosta, Manuel Gisbert, Javier P. J Clin Med Article 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. MDPI 2022-08-03 /pmc/articles/PMC9369748/ /pubmed/35956133 http://dx.doi.org/10.3390/jcm11154518 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chaparro, María
Baston-Rey, Iria
Fernández Salgado, Estela
González García, Javier
Ramos, Laura
Diz-Lois Palomares, María Teresa
Argüelles-Arias, Federico
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, Maite
García, María José
Hinojosa, Esther
Gordillo, Jordi
Martínez Montiel, Pilar
Velayos Jiménez, Benito
Quílez Ivorra, Cristina
Vázquez Morón, Juan María
Huguet, José María
González-Lama, Yago
Muñagorri Santos, Ana Isabel
Amo, Víctor Manuel
Martín Arranz, María Dolores
Bermejo, Fernando
Martínez Cadilla, Jesús
Rubín de Célix, Cristina
Fradejas Salazar, Paola
López San Román, Antonio
Jiménez, Nuria
García-López, Santiago
Figuerola, Anna
Jiménez, Itxaso
Martínez Cerezo, Francisco José
Taxonera, Carlos
Varela, Pilar
de Francisco, Ruth
Monfort, David
Molina Arriero, Gema
Hernández-Camba, Alejandro
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, Mercedes
García, Lara Arias
Hervías Cruz, Daniel
Kloss, Sebastian
Passey, Alun
Novella, Cynthia
Vispo, Eugenia
Barreiro-de Acosta, Manuel
Gisbert, Javier P.
Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_full Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_fullStr Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_full_unstemmed Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_short Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
title_sort using interpretable machine learning to identify baseline predictive factors of remission and drug durability in crohn’s disease patients on ustekinumab
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369748/
https://www.ncbi.nlm.nih.gov/pubmed/35956133
http://dx.doi.org/10.3390/jcm11154518
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