Abstract 39: Development of a Hypoglycemia Prediction Model for Veterans with Diabetes Using Supervised Machine Learning Applied to Electronic Health Record Data

Accurate assessment of hypoglycemia risk is critical for treatment selection in individuals with diabetes and cardiovascular disease (CVD) - patients for whom hypoglycemia is particularly harmful. We developed and validated a hypoglycemia prediction model in diabetes patients with and without CVD using data routinely available in electronic health records (EHR) and compared performance to a published prediction model. We studied 128,893 US Veterans with diabetes and angiographic assessment of CVD from 2005 to 2018. We used a random 2/3 of the sample for model development and the remaining 1/3 for validation. The primary outcome was severe hypoglycemia based on a previously validated algorithm that uses diagnosis codes and glucose measurements. We evaluated 33 potential predictors, including demographics, diabetes-related variables, comorbidities, and CVD risk factors. We sequentially used two machine learning algorithms for model development. First, we used multivariable adaptive regression splines, which can accommodate interactions and non-linearities for continuous variables, to select predictors. Second, we used adaptive elastic net, which can accommodate time-to-event outcomes, to fit a model with the selected variables. We tested model discrimination using the area under the ROC curve (AUC) and calibration by plotting predicted versus observed event rates in the independent validation cohort. The best-fitting prediction model included 18 predictors; a history of hypoglycemia was the strongest predictor (Table). In external validation, AUC was 0.729 for 2-year events, and the slope of the calibration curve was 1.05, exceeding performance of the published model in this patient population for both discrimination and calibration (Table). Conclusions: Applying supervised machine learning to EHR data may provide an efficient approach to tailoring prediction of preventable clinical outcomes, e.g., hypoglycemia, for high risk patients receiving care in an integrated healthcare system.