Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL.
Tulane University School of Public Health and Tropical Medicine, New Orleans, LA.
Diabetes Care. 2020 Jul;43(7):1530-1536. doi: 10.2337/dc20-0227. Epub 2020 Apr 28.
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.
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.
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.
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.
本研究评估了 Building、Relating、Assessing、Validating Outcomes（BRAVO）风险引擎在三项主要临床试验中的能力，以准确预测钠-葡萄糖共转运蛋白 2 抑制剂（SGLT2i）治疗 2 型糖尿病患者的心血管结局：BI 10773（恩格列净）心血管结局事件试验在 2 型糖尿病患者（EMPA-REG OUTCOME）、卡格列净心血管评估研究（CANVAS）和达格列净对心血管事件的影响-心肌梗死溶栓（DECLARE-TIMI 58）试验。
从这三项试验的出版物中获取基线数据并输入到 BRAVO 模型中，以预测心血管结局。评估了降低感兴趣的风险因素（A1C、收缩压[SBP]、LDL 或 BMI）对心血管事件的预测获益，并将模拟结果与每项试验的观察结果进行比较。
当模拟 CANVAS 和 DECLARE-TIMI 58 试验的结果时，BRAVO 达到了最佳的预测准确性。对于 EMPA-REG OUTCOME，在预测死亡率和心绞痛方面存在轻微的偏差（约 20%）。BRAVO 模型预测的治疗组与安慰剂组的风险降低对结局的影响与试验结果的观察到的风险降低效果强烈相关。最后，BRAVO 引擎显示，与 SGLT2i 治疗相关的大部分临床获益是通过 A1C 控制实现的，尽管 SBP 和 BMI 的降低解释了观察到的心血管事件下降的一部分。
BRAVO 风险引擎通过改善常用的风险因素，包括 A1C、SBP 和 BMI，有效地预测了 SGLT2i 对心血管健康的获益。由于这些获益单独很小，因此使用复杂的动态 BRAVO 模型来解释心血管结局试验结果是理想的。
