Investigation of Heart and Vascular Outcomes in Older Veterans

该项目目标是利用一个纵向的、VA国家心血管疾病虚拟队列 调查心力衰竭(HF)、心房颤动(AF)和中风的传统和[新]危险因素 在美国退伍军人中，≥为65。这一群体将由现代和全面的电子产品组成 健康记录(EHR)数据，并将包括继续管理和验证复杂表型使用 创新分析。[这项针对老年退伍军人的研究的具体目的是：1)评估心力衰竭的决定因素 退伍军人医院诊断为心衰后的风险和预后，2)评估房颤风险的决定因素和 3)评估卒中风险和预后的决定因素。] [在上一个资金周期中，我们利用了退伍军人管理局大规模的国家纵向EHR数据， 链接到其他数据源，包括医疗保险和医疗补助服务中心(CMS)和 国家死亡指数(NDI)以创建一个全面的心血管数据库。针对传统数据库的数据管理 RFS、ASCVD预防疗法和几项ASCVD成果也在先前的资助中完成 周而复始。我们的新提议将包括继续进行数据管理，以包括新的RF，如红细胞指数， 白细胞指数、阻塞性睡眠呼吸暂停、虚弱、饮酒、主动脉狭窄、用药、生活方式 因素和血液指标[估计肾小球滤过率(EGFR)，血清白蛋白]。我们将实现这一目标 通过采用高通量概率表型以及多模式和网络数据建模。结果 随着时间推移，将酌情评估风险因素。] 电子健康记录(EHR)数据将用于确定退伍军人的基准索引日期，其基础是 VAMC的一次门诊访问，当时这位退伍军人进行了血液检测，以检测传统血脂。信息来自 基线和后续访问将通过数据库编程获得，将包括：年龄、性别、收缩压 血压、糖尿病状况、胆固醇、高密度脂蛋白胆固醇、甘油三酯和心血管疾病预防药物。过滤算法将是 用于跨不同医院实验室的实验室措施，如已经由 研究团队。同样，BMI、血糖和血红蛋白A1c(HbA1c)的测量，以及血管疾病 将添加ICD-9/10代码。 心衰、房颤和卒中的发生率和预后将评估已建立的和新的风险的作用 配置文件。要分析的结果包括心肌梗死、中风、心血管死亡率和所有原因 死亡率。对于心力衰竭，我们也将在30天内检查心力衰竭住院和再次入院 诊断。我们将在适当的情况下比较当前发布的三种条件下的算法 将结果与我们自己开发的回归模型进行比较，其中包括使用中介分析的新RFs 以及适当时的重新分类战略。我们还将报告每个县的成果地图。]

The objective of this project is to utilize a longitudinal, VA national cardiovascular disease virtual cohort to investigate traditional, and [new] risk factors (RFs), for Heart Failure (HF), Atrial Fibrillation (AF), and stroke among United States Veterans ≥ 65. This cohort will consist of contemporary and comprehensive electronic health record (EHR) data and will include continued curation and validation of complex phenotypes using innovative analytics. [The specific aims of this research in older Veterans are to: 1) Assess determinants of HF risk and prognosis after HF diagnosis in a VA Healthcare facility, 2), Assess determinants of AF risk and prognosis and 3) Assess determinants of Stroke risk and prognosis.] [During the previous funding cycle, we leveraged the VA’s large scale national longitudinal EHR data, linked to additional data sources including the Centers for Medicare and Medicaid Services (CMS) and the National Death Index (NDI) to create a comprehensive cardiovascular database. Data curation for traditional RFs, ASCVD preventive therapies, and several ASCVD outcomes was also completed in the previous funding cycle. Our new proposal will include continued data curation to include novel RFs, such as red cell indices, white blood cell indices, obstructive sleep apnea, frailty, alcohol intake, aortic stenosis, medication use, lifestyle factors, and blood measures [estimated glomerular filtration rate (eGFR), serum albumin]. We will achieve this by employing high-throughput probabilistic phenotyping and multimodal and network data modeling. Outcomes and risk factors will be evaluated over time where appropriate.] Electronic health record (EHR) data will be used to identify a baseline index date for Veterans based on an outpatient VAMC visit at which time the Veteran had blood assayed for traditional lipids. Information from the baseline and follow up visits will be obtained with database programming and will include: age, sex, systolic BP, diabetes status, cholesterol, HDL-C, TG, and CVD preventive medications. Filtering algorithms will be used for laboratory measures across the different hospital laboratories, as has already been undertaken by this research team. Similarly, BMI, glucose and hemoglobin A1c (HbA1c) measurements, and vascular disease ICD-9/10 codes will be added. [Incidence and prognosis of HF, AF, and stroke will assess the role of established and novel risk profiles. Outcomes to be analyzed include myocardial infarction, stroke, cardiovascular mortality, and all-cause mortality. For HF we will also examine HF hospital admissions and HF re-admissions within 30 days of diagnosis. We will compare currently published algorithms for the three conditions where appropriate and compare the results to our own developed regression models that include novel RFs using mediation analyses and reclassification strategies where appropriate. We will also report county- specific mapping of outcomes.]