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.

项目概要/摘要 人们越来越担心因 COVID-19 住院的患者出现意外的高发病率 心脏和血管事件。确定哪些患者患 COVID-19 相关心血管疾病的风险最高 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 患者护理，阐明潜在的病理生理机制，并加速开发有效的治疗方法 疗法。然而，人们对包括既往医疗状况在内的多维风险因素如何影响却知之甚少。 社会经济指标和生物标志物的循环水平影响患者的治疗结果。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 强大的链接数据资源来描述生物、临床、卫生系统和 COVID-19 心血管后遗症发生的社会经济危险因素并进行检查 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、标准化的信息 美国 30 个地区超过 100 个地点住院的所有确诊 SARS-CoV-2 感染的成年人的信息 州。我们将把这个登记处与综合医疗保健索赔联系起来，这是一项国家社会经济剥夺 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 建立关联的多中心登记处的方法，以确定临床、卫生系统和社会经济 预测 COVID-19 患者院内主要不良心血管事件 (MACE) 的因素。瞄准 2，我们将描述长期 MACE（即从 COVID-19 指数出院后 1 年和 2 年） 与纵向医疗保险索赔相关的大型多中心登记处的老年人中的住院治疗），以及 确定预测其发生的临床、卫生系统和社会经济因素。基于此 工作中，我们将创建临床上可实施的风险评分，该评分将在入院时进行估计 从 COVID-19 住院指标出院后，患者发生重大心血管事件的风险。 在目标 3 中，我们利用生物库序列评估了链接注册中心中一部分患者的蛋白质组谱。 血液样本，并识别预测 MACE 发生的生化标志物，两者均在索引期间 因 COVID-19 住院以及出院后。这项研究将增进我们对以下问题的集体理解 COVID-19 相关心血管发病率的生物学、临床和社会经济预测因素 死亡。通过识别心血管事件风险最大的患者，我们的工作将帮助前线人员 临床医生可以更好地制定个性化临床管理策略，卫生系统可以改善医疗服务提供 未来几波大流行。