Using the BRAVO Risk Engine to Predict Cardiovascular Outcomes in Clinical Trials with Sodium Glucose Transporter 2 Inhibitors (SGLT2i)

OBJECTIVE This study evaluated the ability of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine to accurately project cardiovascular outcomes in three major clinical trials (EMPA-REG, CANVAS, and DECLARE-TIMI) on sodium-glucose cotransporter-2 inhibitors (SGL2is) to treat patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS Baseline data from the publications of the three trials were obtained and entered into the BRAVO model to predict cardiovascular outcomes. Projected benefits of reducing risk factors of interest (A1c, systolic blood pressure (SBP), LDL, or BMI) on cardiovascular events were evaluated, and simulated outcomes were compared to those observed in each trial.

RESULTS BRAVO achieved the best prediction accuracy when simulating outcomes of the CANVAS and DECLARE-TIMI trials. For the EMPA-REG trial, a mild bias was observed (~20%) in the prediction of mortality and angina. The effect of risk reduction on outcomes in treatment vs placebo groups predicted by the BRAVO model strongly correlated with the observed effect of risk reduction on the trial outcomes as published. Finally, the BRAVO engine revealed that most of the clinical benefit associated with SGLT2i treatment are through A1c control, although reductions in SBP and BMI explain a proportion of the observed decline in cardiovascular events.

CONCLUSIONS The BRAVO risk engine was effective in predicting the benefits of SGLT2i on cardiovascular health through improvements in commonly measured risk factors, including A1c, SBP, and BMI. Since these benefits are individually small, the use of the complex, dynamic BRAVO model is ideal to explain the cardiovascular outcome trial results.