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激增 使用来自电子健康记录(EHR)的数据的慢性病。然而，虽然数以百万计的患者记录 包括在大型电子病历网络中，它们不是具有总体代表性的随机样本，这是一个限制 这限制了它们在人口健康研究中的应用。患者的非代表性 在执行基于电子病历的跨站点验证时，在电子病历数据中表示也是一个重大挑战 研究结果，因为研究结果往往反映由特定健康服务的人群的独特特征 护理系统。我们建议对三个独特的数据集进行综合二次数据分析：1) 健康和退休调查(HRS)，始于1992年，目前正在进行中，具有全国代表性的健康 20多年的访谈数据，以及生物标志物、体检信息、处方药 数据和索赔联系，包括Medicare D药品索赔；2)纽约大学朗格尼健康电子病历 数据(纽约大学-CDRN，2009年至今)，包括人口统计、生命体征、诊断、实验室结果、处方和 程序；3)纽约市临床数据研究网络(NYC-CDRN)，这是一个电子病历网络， 由包括纽约大学CDRN在内的20家纽约市医疗保健机构组成，纵向链接的数据超过12个 公共数据模型下的百万次患者会诊；以及4)退伍军人事务部Ann Arbor医疗系统 (VAAAHS)企业数据仓库(CDW)，这是对NYC-CDRN的重要补充 评估我们的方法在农村患者中的重复性和推广性时的患者群体 护理中的人口。我们将利用这四个数据集来支持基于电子病历的三类问题 风险预测：1)评估其对总体推断的效用，2)发展个性化的绝对风险 预测，以及3)评估其重复性和跨站点验证。我们将预测后续的风险 老年(50岁及以上)2型糖尿病(T2 DM)患者发生心血管疾病(CVD)。 这些方法的更广泛使用将普遍适用于其他疾病的结果。要实现这些目标 目标，我们的研究将1)开发和验证针对GOLD的EHR表型和诊断时间算法 标准图表审查(目标1)；2)评估基于电子健康记录的风险估计模型的总体泛化能力 通过与基于队列的风险估计模型的比较，开发了EHR偏差调整方法 人口推断(目标2)；3)开发基于eHR的个性化绝对风险预测方法(目标3)， 并通过跨点验证建立所开发的方法(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).