External Validation of a Thiopurine Monitoring Algorithm on the SONIC Clinical Trial Dataset

Abstract

Several reports leverage patterns in Electronic Medical Record (EMR) data to create algorithms to optimize outcomes in healthcare.1–3 More recently, there has been an increasing call for the use of clinical trial data repositories.4 Open clinical trial data sharing has allowed us evaluate the generalizability of algorithms to predict response to thiopurines in patients with inflammatory bowel disease. Yale University Open Data Access (YODA, http://yoda.yale.edu/) is a data-sharing platform which provides access to multiple clinical trial datasets. Previously we internally validated machine learning algorithms for both clinical and biological remission among patients on thiopurines.2,3 We now sought to externally validate the previously developed algorithm to predict objective remission in the SONIC clinical trial of azathioprine, infliximab, or the combination of azathioprine and infliximab in Crohn’s disease using open clinical trial data. Thiopurines continue to be used as monotherapy or as part of combination therapy for the treatment of inflammatory bowel disease (IBD). Worldwide, thiopurines are frequently used as a monotherapy as they remain a low-cost option for steroid-sparing therapy. Experts in IBD rely on patterns in the complete blood count and differential (CBCD) and comprehensive chemistry panel (COMP) to monitor their patients, as thiopurine metabolites perform poorly in the evaluation of clinical response.5 Metabolites have also failed to show benefit in two prospective randomized controlled trials.6–7 Patterns in the CBCD and COMP can be successfully used to predict objective remission with high accuracy, and have been internally validated.3