Assessing Clinical Disease Recurrence Using Laboratory Data in Surgically Resected Patients From the TOPPIC Trial

• Published in: Crohn's & colitis 360 2020-10, Vol.2 (4), p.otaa088-otaa088
• Main Authors: Waljee, Akbar K, Cohen-Mekelburg, Shirley, Liu, Yumu, Liu, Boang, Zhu, Ji, Higgins, Peter D R
• Format: Article
• Language: English
• ISSN: 2631-827X; 2631-827X
• DOI: 10.1093/crocol/otaa088

Abstract Background Machine learning methodologies play an important role in predicting progression of disease or responses to medical therapy. We previously derived and validated a machine learning algorithm to predict response to thiopurines in an inflammatory bowel disease population. We aimed to apply a modified algorithm to predict postsurgical treatment response using clinical trial data. Methods TOPPIC was a multicenter randomized double-blinded placebo-controlled trial of 240 patients, evaluating the effectiveness of 6-mercaptopurine in preventing or delaying postsurgical Crohn disease recurrence. We adapted a well-established machine learning algorithm to predict clinical recurrence postresection using age and multiple laboratory-specific covariates, and compared this to the thiopurine metabolite, 6-thioguanine. Results The random forest machine learning algorithm demonstrates a mean under the receiver operator curve (AuROC) of 0.62 [95% confidence interval (CI) 0.47, 0.78]. Similar results were evident when adding thiopurine metabolite (6-thioguanine) results. Alanine aminotransferase/mean corpuscular volume (ALT/MCV) and potassium × alkaline phosphatase (POT × ALK) predicted endoscopic and biologic recurrence, respectively, with AuROCs of 0.714 (95% CI 0.601, 0.827) and 0.730 (95% CI 0.618, 0.841). Conclusions A machine learning algorithm with laboratory data from within the first 3 months postsurgically does not discriminate clinical recurrence well. Alternative noninvasive measures should be considered and further evaluated. Lay Summary We applied a machine learning algorithm to predict postsurgical treatment response using clinical trial data. However, unlike medical treatment response, a machine learning algorithm did not discriminate clinical recurrence well. Other noninvasive methods of monitoring postsurgical recurrence are necessary.