Using machine learning algorithms to optimize treatment with high-cost biologics in a national cohort of patients with inflammatory bowel disease.

Objectives: Prediction models using statistical or machine learning (ML) approaches can enhance clinical decision support tools. Infliximab (IFX), a biologic with a newly introduced biosimilar for Crohn's disease (CD) and ulcerative colitis (UC), presents an opportunity to evaluate these tools at time of biosimilar switch to predict disease flares. This study sought to evaluate real-world safety and effectiveness of nonmedical IFX biosimilar switch in a national US cohort of CD and UC patients, and to develop and compare interpretable models for predicting adverse clinical events among patients on maintenance IFX. Materials and Methods: This retrospective cohort study used administrative and clinical data from the National Veterans Health Administration Corporate Data Warehouse. It included 2529 Veterans with CD or UC on maintenance IFX (2017-2020), either continuing originator IFX or switching to a biosimilar. The primary outcome was disease-related flare. Classification and survival models were developed using traditional and ML methods and assessed via receiver operating characteristic curve, precision-recall curve, and decision curve analysis. Results: In 2529 Veterans with CD or UC, biosimilar switch had low predictive importance across survival models. Objective laboratory-related information yielded the highest validation. Random forest+ (RF+) outperformed all other statistical and ML models. Prior flares and total health-care encounters were the 2 most important predictors, while hemoglobin was the top laboratory predictor. Conclusions: Prediction models, particularly RF+, may aid in optimizing biologic therapy for CD and UC by identifying patients at higher risk of flare following a biosimilar switch.