Duke Clinical Research Institute, Duke University School of Medicine, P.O. Box 17969, Durham, NC, 27715, USA.
Department of Cardiology, Texas Heart Institute, Houston, TX, USA.
Cardiovasc Diabetol. 2022 Aug 27;21(1):166. doi: 10.1186/s12933-022-01603-8.
Among individuals with atherosclerotic cardiovascular disease (ASCVD), type 2 diabetes mellitus (T2DM) is common and confers increased risk for morbidity and mortality. Differentiating risk is key to optimize efficiency of treatment selection. Our objective was to develop and validate a model to predict risk of major adverse cardiovascular events (MACE) comprising the first event of cardiovascular death, myocardial infarction (MI), or stroke for individuals with both T2DM and ASCVD.
Using data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS), we used Cox proportional hazards models to predict MACE among participants with T2DM and ASCVD. All baseline covariates collected in the trial were considered for inclusion, although some were excluded immediately because of large missingness or collinearity. A full model was developed using stepwise selection in each of 25 imputed datasets, and comprised candidate variables selected in 20 of the 25 datasets. A parsimonious model with a maximum of 10 degrees of freedom was created using Cox models with least absolute shrinkage and selection operator (LASSO), where the adjusted R-square was used as criterion for selection. The model was then externally validated among a cohort of participants with similar criteria in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial. Discrimination of both models was assessed using Harrell's C-index and model calibration by the Greenwood-Nam-D'Agostino statistic based on 4-year event rates.
Overall, 1491 (10.2%) of 14,671 participants in TECOS and 130 (9.3%) in the ACCORD validation cohort (n = 1404) had MACE over 3 years' median follow-up. The final model included 9 characteristics (prior stroke, age, chronic kidney disease, prior MI, sex, heart failure, insulin use, atrial fibrillation, and microvascular complications). The model had moderate discrimination in both the internal and external validation samples (C-index = 0.65 and 0.61, respectively). The model was well calibrated across the risk spectrum-from a cumulative MACE rate of 6% at 4 years in the lowest risk quintile to 26% in the highest risk quintile.
Among patients with T2DM and prevalent ASCVD, this 9-factor risk model can quantify the risk of future ASCVD complications and inform decision making for treatments and intensity.
在患有动脉粥样硬化性心血管疾病（ASCVD）的个体中，2 型糖尿病（T2DM）很常见，并增加了发病和死亡的风险。区分风险是优化治疗选择效率的关键。我们的目标是开发和验证一种模型，以预测患有 T2DM 和 ASCVD 的个体发生主要不良心血管事件（MACE）的风险，该事件由心血管死亡、心肌梗死（MI）或中风的首次事件组成。
使用来自评估沙格列汀心血管结局的试验（TECOS）的数据，我们使用 Cox 比例风险模型来预测 T2DM 和 ASCVD 患者的 MACE。试验中收集的所有基线协变量均被考虑纳入，但由于大量缺失或共线性，有些协变量立即被排除在外。在 25 个插补数据集中的每个数据集都使用逐步选择方法开发全模型，并由在 25 个数据集中的 20 个数据集中选择的候选变量组成。使用具有最小绝对收缩和选择算子（LASSO）的 Cox 模型创建具有最大 10 个自由度的简约模型，其中调整后的 R 平方用作选择标准。然后，在 ACCORD（糖尿病控制和心血管风险行动）试验中具有类似标准的参与者队列中对该模型进行外部验证。使用 Harrell 的 C 指数评估两个模型的区分度，并使用基于 4 年事件率的 Greenwood-Nam-D'Agostino 统计量评估模型校准。
总体而言，TECOS 研究中的 14671 名参与者中有 1491 名（10.2%）和 ACCORD 验证队列中的 130 名（9.3%）（n=1404）在 3 年中位随访期间发生了 MACE。最终模型纳入了 9 个特征（既往卒中、年龄、慢性肾脏病、既往 MI、性别、心力衰竭、胰岛素使用、心房颤动和微血管并发症）。该模型在内部和外部验证样本中均具有中等的区分度（C 指数分别为 0.65 和 0.61）。该模型在风险谱上具有良好的校准度-在最低风险五分位数中，4 年时的累积 MACE 率为 6%，在最高风险五分位数中为 26%。
在患有 T2DM 和 ASCVD 的患者中，这个 9 因素风险模型可以量化未来 ASCVD 并发症的风险，并为治疗和强度决策提供信息。
