Abstract
INTRODUCTION: We have developed the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model, an individual-level, discrete-time microsimulation model specifically designed for type 2 diabetes (T2D) management. This study aims to validate the model's performance when populated exclusively with a fully de-identified dataset to ensure its applicability in secure settings. METHODS: Patient-level data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial were fully de-identified by removing all identifiable information and masking numerical values (e.g., age, body mass index) within ranges to minimize the risk of re-identification. To populate the simulation, we imputed the masked numerical values using data from the National Health and Nutrition Examination Survey (NHANES). We applied the BRAVO model to the baseline data to predict 7-year study outcomes for the EXSCEL trial and assessed its discrimination power and calibration using C-statistics and Brier scores. RESULTS: The model demonstrated acceptable discrimination and calibration in predicting the first occurrence of non-fatal myocardial infarction, non-fatal stroke, heart failure, revascularization, and all-cause mortality. Even with the fully deidentified data from the EXSCEL trial primarily presented in ranges rather than specific values, the BRAVO model exhibited robust prediction performance for diabetes complications and mortality. CONCLUSIONS: This study demonstrates the feasibility of using the BRAVO model in settings where only fully de-identified patient-level data are available.