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Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.


ABSTRACT: Importance:Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission. Objective:To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. Design, Setting, and Participants:This cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n?=?401) were individuals with active (C-reactive protein [CRP] measurement of ?5 mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018. Exposures:All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. Main Outcomes and Measures:Random forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5 mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8. Results:In total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5 mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95% CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance. Conclusions and Relevance:In patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.

SUBMITTER: Waljee AK 

PROVIDER: S-EPMC6512283 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Waljee Akbar K AK   Wallace Beth I BI   Cohen-Mekelburg Shirley S   Liu Yumu Y   Liu Boang B   Sauder Kay K   Stidham Ryan W RW   Zhu Ji J   Higgins Peter D R PDR  

JAMA network open 20190503 5


<h4>Importance</h4>Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission.<h4>Objective</h4>To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment.<h4>Design, setting, and participants</h4>This cohort study analyzed data from 3 phase 3 randomized clinical trials  ...[more]

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