Key result
The BRAVO risk engine effectively predicted SGLT2 inhibitor cardiovascular benefits, though a mild bias (~20%) was observed in predicting mortality and angina for the EMPA-REG OUTCOME trial.
Why the study?
This study evaluated the ability of the BRAVO risk engine to accurately project cardiovascular outcomes in three major clinical trials of SGLT2 inhibitors for type 2 diabetes.
Can the BRAVO risk engine accurately project cardiovascular outcomes in patients with type 2 diabetes treated with SGLT2 inhibitors?
Can the BRAVO risk engine accurately project cardiovascular outcomes in patients with type 2 diabetes treated with SGLT2 inhibitors?
Answers are grounded in this paper and cite their sources.
The BRAVO risk engine can effectively simulate and explain the cardiovascular benefits of SGLT2 inhibitors in type 2 diabetes based on changes in standard risk factors like A1C, SBP, and BMI.
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-BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME), Canagliflozin Cardiovascular Assessment Study (CANVAS), and Dapagliflozin Effect on Cardiovascular Events-Thrombolysis in Myocardial Infarction (DECLARE-TIMI 58) trial-on sodium-glucose cotransporter 2 inhibitors (SGLT2is) 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 with those observed in each trial. RESULTS: BRAVO achieved the best prediction accuracy when simulating outcomes of the CANVAS and DECLARE-TIMI 58 trials. For EMPA-REG OUTCOME, a mild bias was observed (∼20%) in the prediction of mortality and angina. The effect of risk reduction on outcomes in treatment versus 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 benefits 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 SGLT2is 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.
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Shao et al. (2020) studied Type 2 diabetes. Sodium-glucose cotransporter 2 inhibitors (SGLT2is) vs. Placebo was evaluated on Cardiovascular outcomes. The BRAVO risk engine effectively predicted SGLT2 inhibitor cardiovascular benefits, though a mild bias (~20%) was observed in predicting mortality and angina for the EMPA-REG OUTCOME trial.
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