Raghavan Sridharan, Ho Yuk-Lam, Vassy Jason L, Posner Daniel, Honerlaw Jacqueline, Costa Lauren, Phillips Lawrence S, Gagnon David R, Wilson Peter W F, Cho Kelly
Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO (S.R.).
Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO (S.R.).
Circ Cardiovasc Qual Outcomes. 2020 Sep;13(9):e006528. doi: 10.1161/CIRCOUTCOMES.120.006528. Epub 2020 Aug 31.
Estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk in diabetes mellitus patients is used to guide primary prevention, but the performance of risk estimators (2013 Pooled Cohort Equations [PCE] and Risk Equations for Complications of Diabetes [RECODe]) varies across populations. Data from electronic health records could be used to improve risk estimation for a health system's patients. We aimed to evaluate risk equations for initial ASCVD events in US veterans with diabetes mellitus and improve model performance in this population.
We studied 183 096 adults with diabetes mellitus and without prior ASCVD who received care in the Veterans Affairs Healthcare System (VA) from 2002 to 2016 with mean follow-up of 4.6 years. We evaluated model discrimination, using Harrell's C statistic, and calibration, using the reclassification χ test, of the PCE and RECODe equations to predict fatal or nonfatal myocardial infarction or stroke and cardiovascular mortality. We then tested whether model performance was affected by deriving VA-specific β-coefficients. Discrimination of ASCVD events by the PCE was improved by deriving VA-specific β-coefficients (C statistic increased from 0.560 to 0.597) and improved further by including measures of glycemia, renal function, and diabetes mellitus treatment (C statistic, 0.632). Discrimination by the RECODe equations was improved by substituting VA-specific coefficients (C statistic increased from 0.604 to 0.621). Absolute risk estimation by PCE and RECODe equations also improved with VA-specific coefficients; the calibration increased from <0.001 to 0.08 for PCE and from <0.001 to 0.005 for RECODe, where higher indicates better calibration. Approximately two-thirds of veterans would meet a guideline indication for high-intensity statin therapy based on the PCE versus only 10% to 15% using VA-fitted models.
Existing ASCVD risk equations overestimate risk in veterans with diabetes mellitus, potentially impacting guideline-indicated statin therapy. Prediction model performance can be improved for a health system's patients using readily available electronic health record data.
糖尿病患者的估计10年动脉粥样硬化性心血管疾病（ASCVD）风险用于指导一级预防，但风险评估工具（2013年合并队列方程[PCE]和糖尿病并发症风险方程[RECODe]）在不同人群中的表现有所不同。电子健康记录中的数据可用于改善卫生系统患者的风险评估。我们旨在评估美国糖尿病退伍军人首次发生ASCVD事件的风险方程，并改善该人群的模型性能。
我们研究了183096名无既往ASCVD的糖尿病成年患者，他们于2002年至2016年在退伍军人事务医疗系统（VA）接受治疗，平均随访4.6年。我们使用Harrell's C统计量评估PCE和RECODe方程预测致命或非致命心肌梗死或中风以及心血管死亡的模型辨别力，并使用重新分类χ检验评估校准情况。然后，我们测试了得出特定于VA的β系数是否会影响模型性能。通过得出特定于VA的β系数，PCE对ASCVD事件的辨别力得到改善（C统计量从0.560增加到0.597），通过纳入血糖、肾功能和糖尿病治疗指标进一步改善（C统计量为0.632）。通过代入特定于VA的系数，RECODe方程的辨别力得到改善（C统计量从0.604增加到0.621）。使用特定于VA的系数，PCE和RECODe方程的绝对风险估计也得到改善；PCE的校准从<0.001增加到0.08，RECODe从<从0.001增加到0.005，校准越高表明校准越好。基于PCE，约三分之二的退伍军人符合高强度他汀类药物治疗的指南指征，而使用VA拟合模型时仅为10%至15%。
现有的ASCVD风险方程高估了糖尿病退伍军人的风险，可能影响指南推荐的他汀类药物治疗。使用现成的电子健康记录数据可以改善卫生系统患者的预测模型性能。
