Article
Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
Author/s | Chaparro, María
Baston-Rey, Iria Fernández Salgado, Estela González García, Javier Ramos, Laura Diz Lois Palomares, María Teresa Argüelles Arias, Federico Gisbert, Javier P. |
Department | Universidad de Sevilla. Departamento de Medicina |
Publication Date | 2022 |
Deposit Date | 2023-05-17 |
Published in |
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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 ... 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.. |
Funding agencies | Ministerio de Economía y Competitividad (MINECO). España Instituto de Salud Carlos III |
Project ID. | CM21/00025 |
Citation | Chaparro, M., Baston-Rey, I., Fernández Salgado, E., González García, J., Ramos, L., Diz Lois Palomares, M.T.,...,Gisbert, J.P. (2022). Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab. Journal of Clinical Medicine (JCM), 11 (15), 4518. https://doi.org/10.3390/jcm11154518. |
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