Addressing Regional Differences in Diabetes Progression: Global Calibration for Diabetes Simulation Model

Abstract

ObjectivesTo develop a practical solution for modeling diabetes progression and account for the variations in risks of diabetes complications in different regions of the world, which is critical for model-based evaluations on the value of diabetes intervention across populations from different regions globally. MethodsA literature search was conducted to identify eligible clinical trials to support calibration. The Building, Relating, Assessing, and Validating Outcomes (BRAVO) model was employed to simulate diabetes complications using the baseline characteristics of each clinical trial cohort. We utilized regression methods to estimate regional variations across the United States, Europe, Asia, and other regions (eg, Latin America, Africa) in 6 outcomes: myocardial infarction (MI), congestive heart failure (CHF), stroke, angina, revascularization, and mortality. ResultsRegional variations were detected in 4 outcomes. Compared with other regions, individuals from the United States had higher risks of MI (hazard ratio [HR] 1.64; 95% confidence interval [CI]1.41–1.91) and revascularization (HR 3.6; 95% CI 2.94–4.41). Individuals from Europe had a lower risk of stroke (HR 0.61; 95% CI 0.46–0.81), and individuals from other regions outside of the United States, Europe, and Asia had a lower risk of CHF (HR 0.18; 95% CI 0.06–0.58). Finally, the simulated outcomes were regressed on observed outcomes using an ordinary least squares model, with an intercept (0.026), slope (1.005), and R-squared value (0.789) indicating good prediction accuracy. ConclusionRecalibrating the BRAVO model’s diabetes risk engine to account for regional differences shows improved prediction accuracy when the model is applied to multi-region populations commonly recruited for clinical trials.