Machine Learning-Based Quantification of Patient Factors Impacting Remission in Patients With Ulcerative Colitis:...

Etrolizumab, an investigational anti-β7 integrin monoclonal antibody, has undergone evaluation for safety and efficacy in phase III clinical trials on patients with moderate to severe ulcerative colitis (UC). Etrolizumab was terminated because mixed efficacy results were shown in the induction and maintenance phase in patients with UC. In this post hoc analysis, we characterized the impact of explanatory variables on the probability of remission using XGBoost machine learning (ML) models alongside with the SHapley Additive exPlanations framework for explainability. We used patient-level data encompassing demographics, physiology, disease history, clinical questionnaires, histology, serum biomarkers, and etrolizumab drug exposure to develop ML models aimed at predicting remission. Baseline covariates and early etrolizumab exposure at week 4 in the induction phase were utilized to develop an induction ML model, whereas covariates from the end of the induction phase and early etrolizumab exposure at week 4 in the maintenance phase were used to develop a maintenance ML model. Both the induction and maintenance ML models exhibited good predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.74 ± 0.03 and 0.75 ± 0.06 (mean ± SD), respectively. Compared with placebo, the highest tertile of etrolizumab exposure contributed to 15.0% (95% confidence interval (CI): 9.7-19.9) and 17.0% (95% CI: 8.1-26.4) increases in remission probability in the induction and maintenance phases, respectively. Additionally, the key covariates that predicted remission were CRP, MAdCAM-1, and stool frequency for the induction phase and white blood cells, fecal calprotectin and age for the maintenance phase. These findings hold significant implications for establishing stratification factors in the design of future clinical trials.