Abstract
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. To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. 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. All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. 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. 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. 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.
Highlights
Inflammatory bowel disease (IBD) has been estimated to affect more than 1.6 million people in the United States on the basis of the adjusted prevalence rates in the 2010 US white population.[1,2] the advent of expensive biological medications revolutionized the treatment of IBD,[3] the increasing use of these agents has revealed substantial heterogeneity of treatment effect across the population with IBD and likely overuse of these medications
The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78
A prediction model using the week-6 albumin to C-reactive protein (CRP) ratio had an AUROC of 0.76
Summary
Inflammatory bowel disease (IBD) has been estimated to affect more than 1.6 million people in the United States on the basis of the adjusted prevalence rates in the 2010 US white population.[1,2] the advent of expensive biological medications revolutionized the treatment of IBD,[3] the increasing use of these agents has revealed substantial heterogeneity of treatment effect across the population with IBD and likely overuse of these medications. Interest has been growing in personalizing treatment strategies by using methods such as machine learning to match patients with IBD to the treatment most likely to work for them. These techniques using clinical and laboratory predictors that outperform drug levels alone may decrease avoidable drug costs, hospitalizations, surgical procedures, and complications while allowing patients with IBD to achieve symptomatic and biological remission much sooner than the typical 52-week clinical trial end point.[8,9] Such approaches have not yet been applied to ustekinumab. Some suggest a doserelated association between serum drug concentrations of ustekinumab and treatment efficacy in Crohn disease similar to that seen with other novel agents[8]; others suggest that serum drug concentrations of ustekinumab are relatively poor predictors of clinical response in Crohn disease.[10]
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