Assessing Clinical Disease Recurrence Using Laboratory Data in Surgically Resected Patients From the TOPPIC Trial. Issue 4 (23rd October 2020)

• Record Type: Journal Article
• Authors: Waljee, Akbar K; Cohen-Mekelburg, Shirley; Liu, Yumu; Liu, Boang; Zhu, Ji; Higgins, Peter D R
• Is Part Of: Crohn's & colitis 360. Volume 2:Issue 4(2020)
• Publication Date: 2020-10-23
• Subjects: machine learning -- inflammatory bowel disease -- surgery; Crohn's disease -- Periodicals; Colitis -- Periodicals; 616.344
• DOI: 10.1093/crocol/otaa088
• Languages: English
• ISSNs: 2631-827X

Abstract

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.