Sussman Jeremy B, Wiitala Wyndy L, Zawistowski Matthew, Hofer Timothy P, Bentley Douglas, Hayward Rodney A
*Veterans Affairs Center for Clinical Management Research †Department of Internal Medicine and Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
Med Care. 2017 Sep;55(9):864-870. doi: 10.1097/MLR.0000000000000781.
Accurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD.
We created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality.
For the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable.
We found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.
准确估计心血管疾病风险是心血管疾病（CVD）预防中做出良好决策的基础，但在某一人群中开发的风险评分在不同人群中往往表现不佳。我们试图研究一个大型综合医疗系统是否可以利用其电子健康数据更好地预测个体患者发生CVD的风险。
我们使用了所有年龄在45 - 80岁、于2006年使用退伍军人事务部（VA）门诊护理服务且无CVD、心力衰竭或袢利尿剂病史的患者组成队列。我们的结局变量是2007 - 2011年新发的CVD。然后我们开发了一系列重新校准的评分，包括完全重新拟合的“VA心血管疾病风险评分（VARS - CVD）”。我们使用预测质量的标准指标测试了不同的评分。
对于研究中的1,512,092名患者，动脉粥样硬化性心血管疾病风险评分与VARS - CVD具有相似的辨别力（男性的c统计量为0.66，女性为0.73），但动脉粥样硬化性心血管疾病模型校准不佳，预测的事件比观察到的多63%。在完全重新校准的VARS - CVD工具中校准效果极佳，但测试的更简单技术证明可靠性较低。
我们发现，与基于研究人群建立的风险评分相比，本地电子健康记录数据可用于更好地估计CVD。重新校准显著改善了估计，并且重新校准的类型很重要。此类工具还可以轻松集成到医疗系统的电子健康记录中，并且可以更轻松地更新。
