Investigation of Heart and Vascular Outcomes in Older Veterans

本项目的目的是利用一个纵向的，VA国家心血管疾病虚拟队列， 研究心力衰竭（HF）、房颤（AF）和卒中的传统和[新]风险因素（RF） 美国退伍军人≥ 65。这一组将包括当代和全面的电子 健康记录（EHR）数据，并将包括使用 创新的分析。[The本研究在老年退伍军人中的具体目的是：1）评估HF的决定因素 VA医疗机构中HF诊断后的风险和预后，2）评估AF风险的决定因素， 预后和3）评估中风风险和预后的决定因素。 [在上一个资助周期中，我们利用了VA的大规模国家纵向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）创建一个全面的心血管数据库。传统的数据管理 RF、ASCVD预防性治疗和几种ASCVD结局也在之前的资助中完成 周期我们的新提案将包括持续的数据管理，包括新的RF，如红细胞指数， 白色血细胞指数、阻塞性睡眠呼吸暂停、虚弱、酒精摄入、主动脉瓣狭窄、药物使用、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分析和多模态和网络数据建模。成果 风险因素将在适当的时候进行评估。] 电子健康记录（EHR）数据将用于确定退伍军人的基线索引日期， 一次门诊VAMC访问，当时退伍军人进行了传统脂质的血液分析。信息从 基线和随访访视将通过数据库编程获得，包括：年龄、性别、收缩压 血压、糖尿病状态、胆固醇、HDL-C、TG和CVD预防药物。过滤算法将是 用于不同医院实验室的实验室测量，正如本组织已经采取的那样， 研究团队。同样，BMI、血糖和血红蛋白A1 c（HbA 1c）测量以及血管疾病 将添加ICD-9/10代码。 [HF、AF和卒中的发生率和预后将评估确定的和新的风险的作用， 数据区.分析的结局包括心肌梗死、卒中、心血管死亡率和全因死亡率。 mortality.对于HF，我们还将检查HF住院和HF再入院的30天内， 诊断.我们将比较目前公布的算法的三个条件，在适当的情况下， 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RF 和适当的重新分类战略。我们还将报告具体国家的成果地图。

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.]