1311-P: Machine Learning (ML) Application to Predict Patient Risk of Nonadherence in Type 2 Diabetes Management Using U.S. Claims Databases

Medication non-adherence is one of the common leading public health challenges facing the U.S. Poor medication adherence in patients with diabetes, especially type 2 diabetes mellitus (T2DM), may be associated with inadequate glycemic control, increased morbidity and mortality and lead to increased health services utilization and hospital admissions. The use of predictive models based on ML using “big” healthcare data can help identify and predict a subpopulation with high risk of nonadherence, thus providing a scope for improving value in health care and reducing the cost burden. In this study, we extracted 111,180 T2DM patients initiating metformin monotherapy (index date) from the Truven database to train the ML models and predict patients’ level of adherence to metformin monotherapy. Patients must have had at least 6 months of pre-index (baseline) and at least 2 years of post-index (follow-up) data available. Adherence was measured as proportion of days covered (PDC) for metformin in the second year after the index date with PDC >= 0.8 labeled as high-adherence and Disclosure X. Chen: Employee; Self; Merck & Co., Inc. G. Fernandes: None. J. Chen: None. Z. Liu: Employee; Self; Merck & Co., Inc. R. Baumgartner: None.