EHR-based vs population-based CVD risk predictions for older patients with diabetes

摘要 自2010年以来，临床医学和公共卫生受益于临床研究的快速增长， 慢性疾病使用电子健康记录（EHR）的数据。然而，尽管数百万的患者记录 包含在大型EHR网络中，它们不是人口代表性随机样本，这是一个约束 这限制了它们在人群健康研究中的应用。患者的非代表性 在对基于EHR的数据进行跨站点验证时， 调查结果，因为研究结果往往反映了特定卫生服务所服务的人口的独特特征， 护理系统。我们建议对三个独特的数据集进行综合的二次数据分析：1） 健康和退休调查（HRS，始于1992年，目前仍在进行）， 20多年的访谈数据，以及生物标志物、身体评估信息、处方药 数据和索赔联系，包括Medicare D药物索赔; 2）纽约大学Langone Health EHR 数据（NYU-CDRN，2009年至今），包括人口统计学、生命体征、诊断、实验室结果、处方和 3）纽约市临床数据研究网络（NYC-CDRN），这是一个EHR网络， 包括20个纽约市医疗保健机构，包括NYU-CDRN，纵向链接数据超过12个 通用数据模型下的百万患者就诊;以及4）退伍军人事务部安阿伯医疗保健系统 （VAAAHS）企业数据仓库（CDW），为NYC-CDRN提供重要补充 当评估我们的方法对农村患者的可重复性和普遍性时， 人口在照顾。我们将利用这四个数据集来支持基于EHR的三个问题 风险预测：1）评估其对人群推断的效用，2）制定个体化的绝对风险 预测，和3）评估其重现性和跨站点验证。我们将预测随后的风险 2型糖尿病（T2 DM）老年患者（50岁及以上）的偶发心血管疾病（CVD）。 这些方法的更广泛使用将普遍适用于其他疾病的结果。实现这些 目标，我们的研究将1）开发和验证EHR表型和诊断时间算法对黄金 标准图表审查（目标1）; 2）评估基于EHR的风险估计模型的人群普遍性 通过与基于群组的风险估计模型进行比较，并开发EHR偏差调整方法， 群体推断（目标2）; 3）开发基于EHR的个体化绝对风险预测方法（目标3）， 并通过跨中心验证建立开发的方法（目标4）。

Abstract Since 2010, clinical medicine and public health have benefited from a rapid surge of clinical research on chronic diseases using data from electronic health records (EHRs). However, while millions of patient records are included in large EHR networks, they are not population-representative random samples, a constraint which has restrained their utility for population health research. The non-representative nature of patients represented in EHR data also poses a major challenge when performing cross-site validation of EHR-based findings, as study findings tend to reflect the unique characteristics of populations served by specific health care systems. We propose to perform an integrated secondary data analysis of three unique datasets: 1) the Health and Retirement Survey (HRS, begun in 1992 and ongoing) that has nationally representative health interview data for over 20 years, as well as biomarkers, physical assessment information, prescription drug data, and claims linkages including Medicare D drug claims; 2) the New York University Langone Health EHR data (NYU-CDRN, 2009 to now) including demographics, vitals, diagnoses, lab results, prescriptions and procedures; 3) the New York City Clinical Data Research Network (NYC-CDRN) which is an EHR network that comprises 20 NYC healthcare institutions, including the NYU-CDRN, with longitudinally linked data on over 12 million patient encounters under a Common Data Model; and 4) Veterans Affairs Ann Arbor Healthcare System (VAAAHS) Corporate Data Warehouse (CDW), which provides an important complement to the NYC-CDRN patient population when assessing our method’s reproducibility and generalizability for the rural patient population in care. We will leverage these four datasets to support three strands of questions on EHR-based risk predictions: 1) assessing its utility for population inference, 2) developing individualized absolute risk predictions, and 3) assessing its reproducibility and cross-site validation. We will predict risk of subsequent incident cardiovascular disease (CVD) in older patients (age 50 and older) with type 2 diabetes (T2DM). Broader use of these methods will be generally applicable to other diseases outcomes. To achieve these objectives, our study will 1) develop and validate EHR phenotyping and diagnosis time algorithms against gold standard chart review (Aim 1); 2) assess the population-generalizability of EHR-based risk estimation models by comparing with cohort-based risk estimation models and develop EHR bias adjustment methods for population inference (Aim 2); 3) develop methods for EHR-based individualized absolute risk prediction (Aim 3), and establish the developed methods via cross-site validation (Aim 4).