Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines

BACKGROUND AND AIMS: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease IBD has proved difficult. Current methods using 6-thioguanine nucleotide 6-TGN metabolites have failed in randomized controlled trials RCTs, and have not been used to predict objective remission OR. Our aims were to: 1) develop machine learning algorithms MLA using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year. METHODS: Objective remission was defined as the absence of objective evidence of intestinal inflammation. MLAs were developed to predict three outcomes: objective remission, non-adherence, and preferential shunting to 6-methylmercaptopurine 6-MMP. The performance of the algorithms was evaluated using the area under the receiver operating characteristic curve AuROC. Clinical event rates of new steroid prescriptions, hospitalisations, and abdominal surgeries were measured. RESULTS: Retrospective review was performed on medical records of 1080 IBD patients on thiopurines. The AuROC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN. The mean number of clinical events per year in patients with sustained algorithm-predicted remission APR was 1.08 vs 3.95 in those that did not have sustained APR p < 1 x 10-5. Reductions in the individual endpoints of steroid prescriptions/year -1.63, p < 1 x 10-5, hospitalisations/year -1.05, p < 1 x 10-5, and surgeries/year -0.19, p = 0.065 were seen with algorithm-predicted remission. CONCLUSIONS: A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including decreased steroid prescriptions, hospitalisations, and surgeries.