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: 10527735
• 负责人: Judith Anne Smith
• 金额: $ 26.39万
• 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/10527735
• 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; Disease Outcome; 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; Lead; 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; Prognosis; Records; Refractory Disease; Risk; Risk Factors; Running; SARS-CoV-2 infection; Severities; Shock; Site; Steroids; Stratification; Syndrome; Testing; Therapeutic; Time; Translational Research; Troponin; United States; Virus; Virus Diseases; Wisconsin; autoimmune pathogenesis; base; biomarker identification; chronic inflammatory disease; clinical care; clinically actionable; cohort; data modeling; disparity reduction; gastrointestinal; health care disparity; health data; 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.

抽象的 越来越多的证据表明 SARS-CoV-2 感染可导致严重的感染后炎症 儿科患者群体的综合症，包括多系统炎症综合症（MIS-C）。 我们对发展 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差距 是否需要入住 ICU，以及严重心血管并发症的发生情况也仍不清楚。 新冠肺炎后 MIS-C 是否是一种单相“一次性”炎症状况，还是代表慢性疾病的发作 炎症性疾病和可能的自身免疫，这使得出院后的风湿病管理 此外，针对特定治疗方法对 MIS-C 患者进行合理分层也具有挑战性。 由于缺乏来自大量具有代表性的人群的数据，因此具有挑战性。 包括我们自己在内，有一小部分儿科 MIS-C 患者，很难了解全部范围 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) 的数据来描述和 CAPriCORN 包括芝加哥的 12 个卫生系统，其中 3 个。 儿科医院和各种护理机构，并提供全面的成像和 实验室测试以及 MIS-C 常规护理期间收集的主要人口统计和临床数据 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-CoV2 感染后的 MIS-C 患者 EHR 数据并在本地和区域数据集中评估这些算法；(2) 使用来自的队列数据； CAPriCORN 确定特定的临床和实验室属性是否与短期和长期相关 因此，该项目将利用大量人口医疗记录数据来带来成果。 对患者护理过程中收集的关键临床数据之间关系的新见解， SARS-CoV2 感染期间和之后以及 COVID 后炎症性疾病的发展和严重程度 孩子们。