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.

项目摘要/摘要 越来越多的人担心，使用新冠肺炎住院的患者出现意外高发病率 心脏和血管事件。确定哪些患者患新冠肺炎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 护理病人，阐明潜在的病理生理机制，并加速有效的发展 治疗。然而，关于多维风险因素，包括先前的医疗条件， 社会经济指标和生物标记物的循环水平影响患者的预后。建立在我们的 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、卫生系统和 新冠肺炎心血管后遗症发生的社会经济危险因素分析 它们对健康结果的影响。为了创建这个数据资源，我们与美国心脏协会合作 协会，其新冠肺炎心血管疾病登记正在积极捕捉高质量、标准化的 美国100个地点30个地点所有确诊感染SARS-CoV-2的成人住院信息 各州。我们将把这一登记与全面医疗保健索赔联系起来，这是一种全国性的社会经济剥夺 索引和详细的医疗保健系统信息。在目标1中，我们将应用传统学习和机器学习 联系多中心登记的方法，以确定临床、卫生系统和社会经济 预测新冠肺炎患者住院期间主要心血管不良事件(MACE)的因素。在AIM 2、我们将表征长期MACE(即，从新冠肺炎指数出院后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中的老年人中)，以及 确定预测其发生的临床、卫生系统和社会经济因素。在此基础上 工作，我们将创建临床可实施的风险评分，该评分将在入院时估计和 出院时从新冠肺炎住院的一项指标来看，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关联注册表中的一部分患者的蛋白质组图谱 血液样本，并确定预测MACE发生的生化标志物，这两个过程 因新冠肺炎入院及出院后。这项研究将促进我们对 新冠肺炎相关心血管发病率的生物学、临床和社会经济学预测因素 死亡率。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前线 临床医生更好地个性化临床管理策略，卫生系统在 未来的大流行浪潮。