Identification of Risk Factors for predicting outcomes of COVID-19-Related Multisystem Inflammatory Syndrome in Children (MISC) using Real World Clinical Data

使用真实世界临床数据识别预测 COVID-19 相关儿童多系统炎症综合征 (MISC) 结果的风险因素

• 批准号: 10679093
• 负责人: Judith Anne Smith
• 金额: $ 19.96万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-08 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10679093
• 关键词: 2019-nCoV; Address; Admission activity; Adult; Affect; Algorithms; Area; Autoantibodies; Autoimmune Diseases; Autoimmunity; Biological Markers; Biological Products; Black race; COVID-19; Cardiac; Cardiovascular system; Caring; Case Study; Cervical Lymphadenopathy; Characteristics; Chicago; Child; Childhood; Clinical; Clinical Data; Coronary; Data; Data Set; Development; Disease; Electronic Health Record; Exanthema; Fever; Foundations; Growth; Health system; Heart Abnormalities; Heart Diseases; Hospitalization; Human; Illinois; Image; Immunoglobulins; Infection; Inflammatory; Infusion procedures; Injections; Intravenous; Laboratories; Latinx; Length of Stay; Lung diseases; Medical Records; Multisystem Inflammatory Syndrome in Children; Myocarditis; Outcome; Outcomes Research; Pathological Dilatation; Patient Care; Patient-Focused Outcomes; Patients; Pediatric Hospitals; Pediatric cohort; Population; Pressor Support; Probability; Prognosis; Records; Refractory Disease; Risk; Risk Factors; Running; SARS-CoV-2 infection; Severities; Shock; Site; Steroids; Stratification; Syndrome; System; Testing; Therapeutic; Time; Translational Research; Troponin; United States; Virus; Virus Diseases; Wisconsin; autoimmune pathogenesis; biomarker identification; chronic inflammatory disease; clinical care; clinically actionable; cohort; data modeling; disparity reduction; gastrointestinal; health care disparity; improved; inflammatory marker; insight; interest; minority children; novel; outcome prediction; patient oriented; patient population; pediatric patients; post SARS-CoV-2 infection; post-COVID-19; predictive modeling; rare condition; risk prediction; routine care; social health determinants; standard of care; tool

Abstract There is increasing evidence that SARS-CoV-2 infection can lead to significant post-infection inflammatory syndromes in pediatric patient populations, including Multisystem Inflammatory Syndrome (MIS-C). There are multiple critical gaps in our understanding of risk factors and biomarkers for developing MIS-C, severe MIS-C requiring ICU admission, and the development of severe cardiovascular complications. It also remains unclear whether post-COVID MIS-C is a monophasic “one time” inflammatory condition or represents the onset of chronic inflammatory disease and possible autoimmunity, which makes post-discharge rheumatological management challenging. Furthermore, rational stratification of MIS-C patients for specific therapeutic approaches has been challenging due to lack of data from large, population representative cohorts. Since many health systems, including our own, have small populations of pediatric MIS-C patients, it is difficult to understand the full scope and breadth of MIS-C presentation within a single site. We propose to leverage electronic health record (EHR) data from the Chicago Area Patient Centered Outcomes Research Network (CAPriCORN) to describe and characterize MIS-C patient populations. CAPriCORN includes 12 health systems across Chicago, including 3 pediatric hospitals and diverse care settings, and provides access to a comprehensive array of imaging and laboratory tests along with primary demographic and clinical data collected during routine care for MIS-C patients. In this proposal, we will (1) use well-characterized pediatric cohorts at UW-Madison and Lurie Children's Hospital to develop algorithms to identify and characterize patients with MIS-C following SARS-CoV2 infection in EHR data and assess these algorithms in local and regional datasets; and (2) use cohort data from CAPriCORN to determine if specific clinical and laboratory attributes associate with short-term and long-term MIS-C outcomes. Thus, this project will harness the wealth of a large population medical record data to bring novel insights into the relationship between key clinical data collected during the context of care for patients pre, during- and post-SARS-CoV2 infection and development and severity of post-COVID inflammatory disease in children.

摘要 越来越多的证据表明，SARS-CoV-2感染可导致显著的感染后炎症反应， 在儿科患者人群中，包括多系统炎症综合征（MIS-C）。有 我们对发生MIS-C、严重MIS-C的风险因素和生物标志物的理解存在多个关键缺口 需要入住ICU，并出现严重的心血管并发症。目前还不清楚 COVID后MIS-C是否是一种单相“一次性”炎症性疾病或代表慢性 炎症性疾病和可能的自身免疫，这使得出院后的风湿性管理 挑战性此外，MIS-C患者的特定治疗方法的合理分层已经被证明是可行的。 由于缺乏来自大型人群代表性队列的数据，因此具有挑战性。由于许多卫生系统， 包括我们自己的，有儿童MIS-C患者的小群体，很难了解整个范围 和MIS-C演示的广度。我们建议利用电子健康记录（EHR） 来自芝加哥地区以患者为中心的结果研究网络（CAPriCORN）的数据， 描述MIS-C患者人群的特征。CAPriCORN包括芝加哥的12个卫生系统，其中包括3个 儿科医院和各种护理环境，并提供全面的成像和 实验室检查沿着在MIS-C常规护理期间收集的主要人口统计学和临床数据 患者在这项提案中，我们将（1）使用威斯康星大学麦迪逊分校和卢里儿童医院的特征良好的儿科队列， 医院将开发算法来识别和表征SARS-CoV 2感染后的MIS-C患者 在EHR数据中，并在本地和区域数据集中评估这些算法;以及（2）使用来自 CAPriCORN确定特定临床和实验室属性是否与短期和长期 MIS-C结果。因此，该项目将利用大量人口病历数据的财富， 新的见解之间的关系收集的关键临床数据的背景下，护理病人前， 在SARS-CoV 2感染期间和之后， 孩子