Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA.
Division of Cardiovascular Medicine, Department of Internal Medicine, Medical School, University of Michigan, Ann Arbor, MI, 48109, USA.
Atherosclerosis. 2021 Oct;334:23-29. doi: 10.1016/j.atherosclerosis.2021.08.034. Epub 2021 Aug 21.
Multivariable algorithms have been developed to predict the risk of atherosclerotic cardiovascular disease (ASCVD) to identify high-risk patients. Shortly after the introduction of the AHA/ACC Pooled Cohort Equations (PCE), a systematic overestimation of risk was identified. As such, a revised PCE was proposed to more accurately assess ASCVD risk. This study aims to compare the accuracy of both PCE in predicting ASCVD risk within a large, real-world patient sample in the US.
This retrospective cohort study identified 20,843 patients aged between 40 and 75 years with no previous ASCVD in an academic healthcare system. Model fit, calibration, and discrimination were compared between PCE using Bayesian Information Criterion (BIC), Hosmer-Lemeshow test, area under the ROC curves (AUC), Brier score, and precision-recall analysis. In addition, we examined race and sex subgroups for effect modification.
Both PCE showed poor calibration (Hosmer-Lemeshow χ > 20; p < 0.05) and discrimination (AUC<0.7). The lack of improvement in discrimination of the revised PCE (AUC: 0.677 vs 0.679; p = 0.357) was confirmed with the AUC precision-recall curves (AUC: 0.0717 vs 0.0698). In contrast, the AHA/ACC PCE showed a strong positive risk prediction (ΔBIC>10) compared to the revised PCE, although calibration curves had overlapped.
In this single center analysis, both PCE had poor calibration and discrimination of ASCVD risk in a large, real-world patient sample followed up for over 2 years. There was no evidence of improvement in the accuracy of the revised PCE in assessing the risk of ASCVD in relation to the AHA/ACC PCE.
已经开发了多变量算法来预测动脉粥样硬化性心血管疾病（ASCVD）的风险，以识别高危患者。在 AHA/ACC 汇总队列方程（PCE）推出后不久，就发现了风险的系统高估。因此，提出了修订后的 PCE 以更准确地评估 ASCVD 风险。本研究旨在比较这两种 PCE 在预测美国大型真实世界患者样本中 ASCVD 风险的准确性。
这项回顾性队列研究在一个学术医疗系统中确定了 20843 名年龄在 40 至 75 岁之间且无既往 ASCVD 的患者。使用贝叶斯信息准则（BIC）、Hosmer-Lemeshow 检验、ROC 曲线下面积（AUC）、Brier 评分和精度-召回分析比较了两种 PCE 的模型拟合、校准和区分度。此外，我们还检查了种族和性别亚组的效应修饰。
两种 PCE 的校准均较差（Hosmer-Lemeshow χ>20；p<0.05），且区分度较差（AUC<0.7）。修订后的 PCE 的区分度没有改善（AUC：0.677 与 0.679；p=0.357），这一点通过 AUC 精度-召回曲线得到了证实（AUC：0.0717 与 0.0698）。相比之下，AHA/ACC PCE 与修订后的 PCE 相比，对 ASCVD 风险的预测具有强烈的阳性风险预测（ΔBIC>10），尽管校准曲线有重叠。
在这项单中心分析中，两种 PCE 在随访超过 2 年的大型真实世界患者样本中，ASCVD 风险的校准和区分度均较差。修订后的 PCE 在评估与 AHA/ACC PCE 相关的 ASCVD 风险的准确性方面没有改善的证据。
