Applying Machine Learning Models Derived from Administrative Claims Data to Predict Medication Non-Adherence in Patients Self-Administering Biologic Medications for Inflammatory Bowel Disease

Abstract

Abstract Background Adherence to self-administered biologic therapies is important to induce remission and prevent adverse clinical outcomes in inflammatory bowel disease. This study aimed to use administrative claims data and machine learning methods to predict nonadherence in an academic medical center test population. Methods A model-training dataset of beneficiaries with inflammatory bowel disease and first unique dispense of a self-administered biologic between June 30, 2016 and June 30, 2019 was extracted from the Commercial Claims and Encounters and Medicare Supplemental Administrative Claims Database. Known correlates of medication nonadherence were identified in the dataset. Nonadherence to biologic therapies was defined as a proportion of days covered ratio <80% at 1 year. A similar dataset was obtained from a tertiary academic medical center electronic medical record data for use in model testing. A total of 48 machine learning models were trained and assessed utilizing area under the receiver operating characteristic curve as the primary measure of predictive validity. Results The training dataset included 6,998 beneficiaries (n = 2,680 nonadherent, 38.3%) while the testing dataset included 285 patients (n = 134 nonadherent, 47.0%). When applied to test data, highest performing models had an area under the receiver operating characteristic curve of 0.55, indicating poor predictive performance. The majority of models trained had low sensitivity and high specificity. Conclusion Administrative claims-trained models were unable to predict biologic medication nonadherence in patients with inflammatory bowel disease. Future research may benefit from datasets with enriched demographic and clinical data in training predictive models.