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

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 dura...

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Autores principales: Waljee, Akbar K., Wallace, Beth I., Cohen-Mekelburg, Shirley, Liu, Yumu, Liu, Boang, Sauder, Kay, Stidham, Ryan W., Zhu, Ji, Higgins, Peter D. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512283/
https://www.ncbi.nlm.nih.gov/pubmed/31074823
http://dx.doi.org/10.1001/jamanetworkopen.2019.3721
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author Waljee, Akbar K.
Wallace, Beth I.
Cohen-Mekelburg, Shirley
Liu, Yumu
Liu, Boang
Sauder, Kay
Stidham, Ryan W.
Zhu, Ji
Higgins, Peter D. R.
author_facet Waljee, Akbar K.
Wallace, Beth I.
Cohen-Mekelburg, Shirley
Liu, Yumu
Liu, Boang
Sauder, Kay
Stidham, Ryan W.
Zhu, Ji
Higgins, Peter D. R.
author_sort Waljee, Akbar K.
collection PubMed
description 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.
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spelling pubmed-65122832019-05-28 Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease Waljee, Akbar K. Wallace, Beth I. Cohen-Mekelburg, Shirley Liu, Yumu Liu, Boang Sauder, Kay Stidham, Ryan W. Zhu, Ji Higgins, Peter D. R. JAMA Netw Open Original Investigation 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. American Medical Association 2019-05-10 /pmc/articles/PMC6512283/ /pubmed/31074823 http://dx.doi.org/10.1001/jamanetworkopen.2019.3721 Text en Copyright 2019 Waljee AK et al. JAMA Network Open. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Waljee, Akbar K.
Wallace, Beth I.
Cohen-Mekelburg, Shirley
Liu, Yumu
Liu, Boang
Sauder, Kay
Stidham, Ryan W.
Zhu, Ji
Higgins, Peter D. R.
Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title_full Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title_fullStr Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title_full_unstemmed Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title_short Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease
title_sort development and validation of machine learning models in prediction of remission in patients with moderate to severe crohn disease
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512283/
https://www.ncbi.nlm.nih.gov/pubmed/31074823
http://dx.doi.org/10.1001/jamanetworkopen.2019.3721
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