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Using the BRAVO Risk Engine to Predict Cardiovascular Outcomes in Clinical Trials With Sodium-Glucose Transporter 2 Inhibitors.

Abstract：

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.

journal_name

Diabetes Care

journal_title

Diabetes care

authors

Shao H,Shi L,Fonseca VA

doi

10.2337/dc20-0227

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

1530-1536

issue

eissn

0149-5992

issn

1935-5548

pii

dc20-0227

journal_volume

pub_type

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Using the BRAVO Risk Engine to Predict Cardiovascular Outcomes in Clinical Trials With Sodium-Glucose Transporter 2 Inhibitors.