A Multi-Dimensional Linked Registry to Identify Biological, Clinical, Health System, and Socioeconomic Risk Factors for COVID-19-Related Cardiovascular Events

多维关联登记系统，用于识别与 COVID-19 相关的心血管事件的生物、临床、卫生系统和社会经济风险因素

• 批准号: 10183512
• 负责人: ROBERT E GERSZTEN
• 金额: $ 87.32万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10183512
• 关键词: Address; Admission activity; Adult; Affect; American Heart Association; Biochemical; Biochemical Markers; Biological; Biological Markers; Blood Proteins; Blood Vessels; Blood coagulation; Blood specimen; COVID-19; COVID-19 patient; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Characteristics; Clinical; Clinical Management; Communities; Comprehensive Health Care; Dangerousness; Data; Data Linkages; Deep Vein Thrombosis; Development; Disease; Elderly; Embolism; Event; Future; Geography; Health; Health system; Healthcare; Heart Arrest; Heart failure; Hospitalization; Hospitals; Israel; Link; Longterm Follow-up; Lung; Machine Learning; Measures; Medical; Medical center; Medicare; Medicare claim; Modeling; Morbidity - disease rate; Myocardial Infarction; Myocarditis; Outcome; Pathology; Patient Care; Patient risk; Patient-Focused Outcomes; Patients; Predictive Factor; Prevention approach; Proteins; Proteomics; Registries; Research; Risk; Risk Factors; SARS-CoV-2 infection; Site; Socioeconomic Factors; Source; Standardization; Stroke; Structure; Time; Ventricular Arrhythmia; Work; base; biobank; biomarker discovery; cardiovascular risk factor; care delivery; clinical phenotype; clinical predictors; clinical risk; cohort; data resource; deprivation; disease registry; effective therapy; experience; heart damage; high risk; improved; indexing; insight; medical information system; mortality; older patient; pandemic disease; patient subsets; personalized care; personalized management; predictive marker; predictive modeling; prognostic; risk prediction; rurality; socioeconomics

PROJECT SUMMARY/ABSTRACT There is mounting concern that patients hospitalized with COVID-19 experience unexpectedly high rates of cardiac and vascular events. Identifying which patients are at highest risk for COVID-19-related cardiovascular events and delineating how these events affect short- and long-term outcomes may help support individualized patient care, illuminate underlying pathophysiologic mechanisms, and accelerate the development of effective therapies. However, little is known about how multi-dimensional risk factors, including prior medical conditions, socioeconomic indicators, and circulating levels of biomarkers affect patient outcomes. Building on our team's expertise in data linkage, prediction modeling, and biomarker discovery, we will create a unique and powerful linked data resource to characterize the biological, clinical, health system, and socioeconomic risk factors for the development of cardiovascular sequelae of COVID-19 and examine their impact on health outcomes. To create this data resource, we have partnered with the American Heart Association, whose COVID-19 Cardiovascular Disease Registry is actively capturing high-quality, standardized information on all adults hospitalized with confirmed SARS-CoV-2 infection at >100 U.S. sites spanning 30 states. We will link this registry to comprehensive health care claims, a national socioeconomic deprivation index, and detailed health care system information. In Aim 1, we will apply traditional and machine learning approaches to the linked multicenter registry in order to identify the clinical, health system, and socioeconomic factors that predict in-hospital major adverse cardiovascular events (MACE) among COVID-19 patients. In Aim 2, we will characterize long-term MACE (i.e., at 1 and 2 years after discharge from the index COVID-19 hospitalization) among older adults in a large multicenter registry linked with longitudinal Medicare claims, and identify the clinical, health system, and socioeconomic factors that predict their occurrence. Based on this work, we will create clinically implementable risk scores which will estimate, at the time of admission for and discharge from an index COVID-19 hospitalization, a patient's risk of developing a major cardiovascular event. In Aim 3, we evaluate the proteomic profiles of a subset of patients in the linked registry with biobanked serial blood samples, and identify biochemical markers that predict the occurrence of MACE, both during index hospitalization for COVID-19 and after discharge. This research will advance our collective understanding of the biological, clinical, and socioeconomic predictors of COVID-19-related cardiovascular morbidity and mortality. By identifying patients at greatest at risk of cardiovascular events, our work will help frontline clinicians better individualize clinical management strategies and health systems improve care delivery during future waves of the pandemic.

项目摘要/摘要 人们持续关注，患者与19 Covid-19住院的患者出乎意料的高率 心脏和血管事件。确定哪些患者患相关19的患者风险最高 事件并描述这些事件如何影响短期和长期结果可能有助于支持个性化的 患者护理，照明潜在的病理生理机制，并加速有效的发展 疗法。但是，关于多维风险因素，包括先前的医疗状况， 社会经济指标和生物标志物循环水平会影响患者的结果。建立在我们的基础上 团队在数据链接，预测建模和生物标志物发现方面的专业知识，我们将创建一个独特的 以及强大的链接数据资源以表征生物学，临床，卫生系统和 社会经济危险因素开发了Covid-19的心血管后遗症，并检查 它们对健康结果的影响。为了创建此数据资源，我们与美国心脏合作 协会，其COVID-19心血管疾病注册中心正在积极捕获高质量的标准化 有关所有已确认的SARS-COV-2感染的成年人的信息，涉及美国100个地点30 国家。我们将将此注册表与全面的医疗保健索赔联系起来，这是国家社会经济剥夺 索引和详细的医疗保健系统信息。在AIM 1中，我们将应用传统和机器学习 链接的多中心注册表的方法是为了确定临床，卫生系统和社会经济 预测院内主要不良心血管事件（MACE）的因素中，在199例患者中。目标 2，我们将表征长期的狼牙棒（即，从指数Covid-19 在大型多中心注册表中，老年人与纵向医疗保险主张有关的老年人和 确定预测其发生的临床，卫生系统和社会经济因素。基于此 工作，我们将创建可实施的风险评分，该评分将在入学时估算和 从指数Covid-19 Hospicyation出院，这是患者发生重大心血管事件的风险。 在AIM 3中，我们评估了与生物循环的连接注册表中一部分患者子集的蛋白质组学特征 血液样本，并确定在指数期间预测MACE发生的生化标记 19009年和出院后的住院。这项研究将提高我们对 共同19-9相关心血管发病率的生物，临床和社会经济预测因素 死亡。通过确定最大风险患心血管事件的患者，我们的工作将有助于前线 临床医生更好地个性化临床管理策略和卫生系统改善医疗服务 大流行的未来浪潮。