Journal
INFLAMMATORY BOWEL DISEASES
Volume 24, Issue 6, Pages 1185-1192Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/ibd/izy031
Keywords
inflammatory bowel disease; Crohn's disease; vedolizumab; machine learning algorithm
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Funding
- United States Department of Veterans Affairs (VA) Health Services Research and Development Service [CDA 11-217]
- National Institutes of Health (NIH) [R01 GM097117]
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Background and Aims: Vedolizumab (VDZ) is effective for Crohn's disease (CD) but costly and is slow to produce remission. Early knowledge of whether vedolizumab is likely to succeed is valuable for physicians, patients, and insurers. Methods: Phase 3 clinical trial data on VZD for CD were used to predict outcomes. Random forest modeling on the training cohort was used to predict the outcome of corticosteroid-free biologic remission at week 52 on the testing cohort. Models were constructed using baseline data, or data through week 6 of VDZ therapy. Results: The clinical trial included 594 subjects who received VDZ with baseline active inflammation [elevated C-reactive protein (>5 mg/L)]. Subjects with missing predictor variables (N = 120) or missing outcome data (N = 2) were excluded to produce a modeling dataset of 472 subjects. The Area Under the Receiver Operating Characteristic curve (AuROC) for corticosteroid-free biologic remission at week 52 using baseline data was only 0.65 (95% CI: 0.53 - 0.77), but was 0.75 (95% CI: 0.64 - 0.86) with data through week 6 of VDZ. Patients predicted to be in corticosteroid-free biologic remission at week 52 by the model achieved this endpoint 35.8% of the time, whereas patients predicted to fail only succeeded 6.7% of the time. Conclusions: An algorithm using laboratory data through week 6 of VDZ therapy was able to identify which CD patients with baseline inflammation would achieve corticosteroid-free biologic remission on VDZ at week 52. A majority of patients can be identified by week 6 as very unlikely to achieve remission.
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