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: 10599322
• 负责人: ROBERT E GERSZTEN
• 金额: $ 86.58万
• 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/10599322
• 关键词: Acceleration; 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; COVID-19 risk; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Characteristics; Clinical; Clinical Management; Communities; Comprehensive Health Care; Dangerousness; Data; Data Linkages; Deep Vein Thrombosis; Development; Dimensions; Disease; Elderly; Embolism; Event; Future; Geography; Health; Health system; Healthcare; Heart Arrest; Heart failure; Hospitalization; Hospitals; Institution; 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; Time; Ventricular Arrhythmia; Work; biobank; biomarker discovery; cardiovascular risk factor; care delivery; clinical phenotype; clinical predictors; clinical risk; cohort; data resource; deprivation; disease registry; economic indicator; 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.

项目总结/摘要 人们越来越担心，因COVID-19住院的患者的死亡率出乎意料地高， 心脏和血管事件确定哪些患者患有COVID-19相关心血管疾病的风险最高 事件和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 病人护理，阐明潜在的病理生理机制，并加速有效的发展， 治疗然而，人们对多维风险因素，包括先前的医疗条件， 社会经济指标和生物标志物的循环水平影响患者的结果。充分发挥两国 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 和强大的链接数据资源，以表征生物，临床，卫生系统， COVID-19心血管后遗症发展的社会经济风险因素， 对健康结果的影响。为了创建这个数据资源，我们与美国心脏协会合作， 协会，其COVID-19心血管疾病登记处正在积极收集高质量、标准化的 美国30个地区>100个地点所有确诊SARS-CoV-2感染住院的成年人的信息 states.我们将把这一登记与全面的医疗保健索赔联系起来， 索引和详细的医疗保健系统信息。在目标1中，我们将应用传统和机器学习 采用链接的多中心登记研究方法，以确定临床、卫生系统和社会经济 预测COVID-19患者住院期间主要不良心血管事件（MACE）的因素。在Aim中 2、我们将描述长期MACE的特征（即，在从COVID-19指数出院后1年和2年 在与纵向医疗保险索赔相关的大型多中心登记研究中， 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，我们将创建临床可实施的风险评分，将估计，在入院时， 从COVID-19指数住院中出院，患者发生重大心血管事件的风险。 在目标3中，我们使用生物库序列评估了链接注册表中一部分患者的蛋白质组学谱。 血液样本，并确定预测MACE发生的生化标志物， 因COVID-19住院及出院后。这项研究将促进我们对 COVID-19相关心血管疾病发病率的生物学、临床和社会经济学预测因素， mortality.通过识别心血管事件风险最高的患者，我们的工作将有助于一线 临床医生更好地个性化临床管理策略和卫生系统改善护理服务， 大流行病的未来浪潮。