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/文摘