Investigation of Heart and Vascular Outcomes in Older Veterans

老年退伍军人心脏和血管结局的调查

• 批准号: 10618301
• 负责人: Kelly Cho
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10618301
• 关键词: Adult; Affect; Age; Age Factors; Age Years; Alcohol consumption; Algorithms; Ambulatory Care; American; American Heart Association; Aortic Valve Stenosis; Assessment tool; Atherosclerosis; Atrial Fibrillation; Biological Assay; Biological Markers; Black Populations; Blood; Blood Cells; Blood Pressure; Blood Vessels; Body mass index; Calendar; Cardiology; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cessation of life; Chronic Disease; Chronic Kidney Failure; Classification; Code; Complex; County; Data; Data Sources; Databases; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Disease Outcome; EFRAC; Elderly; Electronic Health Record; Equation; Erythrocyte Indices; Ethnic Origin; Evaluation; Event; Formulation; Funding; Glomerular Filtration Rate; Glucose; Glycosylated hemoglobin A; Health care facility; Heart; Heart Diseases; Heart failure; Hemoglobin; High Density Lipoprotein Cholesterol; High Density Lipoproteins; Hispanic Populations; Hospitalization; ICD-9; Incidence; Insurance; Investigation; LDL Cholesterol Lipoproteins; Laboratories; Length of Stay; Leukocytes; Link; Lipids; Maps; Measurement; Measures; Mediation; Medical; Modeling; Modernization; Myocardial Infarction; Non-Insulin-Dependent Diabetes Mellitus; Obstructive Sleep Apnea; Outcome; Outpatients; Pharmaceutical Preparations; Phenotype; Population; Preventive; Preventive therapy; Productivity; Prognosis; Progress Reports; Publishing; Race; Recurrence; Renal function; Reporting; Research; Research Personnel; Risk; Risk Assessment; Risk Estimate; Risk Factors; Role; Serum Albumin; Stroke; Testing; Thyrotropin; Time; United States; United States Centers for Medicare and Medicaid Services; United States Department of Veterans Affairs; Validation; Vascular Diseases; Veterans; Veterans Health Administration; Visit; Visualization; White Blood Cell Count procedure; alcohol use disorder; aortic valve replacement; cardiovascular disorder risk; cardiovascular risk factor; cigarette smoking; cohort; college; data curation; data modeling; disease prognosis; experience; follow-up; frailty; hospital laboratories; hospital readmission; indexing; innovation; interest; lifestyle factors; lipoprotein cholesterol; male; mortality; multimodality; novel; prediction algorithm; preservation; prospective; risk prediction; sex; stroke risk; suburb; venous thromboembolism; virtual

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.]

该项目的目的是利用纵向VA国家心血管疾病虚拟队列 研究传统和[新的]风险因素（RFS），用于心力衰竭（HF），房颤（AF）和中风 在美国退伍军人≥65中。该队列将包括现代和全面的电子 健康记录（EHR）数据，并将包括持续的策划和使用复杂表型的验证 创新分析。 [这项研究在老年退伍军人中的具体目的是：1）评估HF的确定词 在VA医疗机构中HF诊断后的风险和提示，2），评估AF风险和 预后和3）评估中风风险和预后的决定者。] [在上一个基础周期中，我们利用VA的大规模国家纵向EHR数据， 链接到其他数据来源，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。传统的数据策划 RFS，ASCVD预防疗法和以前的资金也完成了几个ASCVD结果 循环。我们的新建议将包括持续的数据策划，包括新的RF，例如红细胞指数， 白细胞指数，阻塞性睡眠呼吸暂停，脆弱，酒精摄入量，主动脉狭窄，药物使用，生活方式 因素和血液测量[估计的肾小球过滤率（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，多模式和网络数据建模。结果 在适当的情况下，随着时间的推移将评估风险因素。 电子健康记录（EHR）数据将用于确定基于退伍军人的基线指数日期 在那个时候，老兵将鲜血分配给传统脂质的情况。来自 基线和后续访问将通过数据库编程获得，其中包括：年龄，性别，收缩期 BP，糖尿病状态，胆固醇，HDL-C，TG和CVD预防药物。过滤算法将是 正如这已经进行的，用于不同医院实验室的实验室措施 研究团队。同样，BMI，葡萄糖和血红蛋白A1C（HBA1C）测量和血管疾病 将添加ICD-9/10代码。 [HF，AF和中风的发病率和预后将评估已建立和新型风险的作用 概况。要分析的结果包括心肌梗塞，中风，心血管死亡率和全因 死亡。对于HF，我们还将检查HF医院入院和HF重新入院 诊断。我们将在适当的情况下比较当前发布的三种条件算法 将结果与我们自己开发的回归模型进行比较，该模型包括使用中介分析的新型RF 并在适当的地方进行重新分类策略。我们还将报告县的结果。]