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

摘要 越来越多的证据表明，感染SARS-CoV-2会导致感染后的严重炎症 儿科患者群体中的症状，包括多系统炎症综合征(MIS-C)。确实有 我们对发病的危险因素和生物标记物认识上的多个关键差距 需要ICU入院，并发展为严重的心血管并发症。它也仍然不清楚 冠状病毒感染后的MIS-C是单相性“一次性”炎症状态还是代表慢性 炎症性疾病和可能的自身免疫，这使得出院后的风湿病治疗 很有挑战性。此外，针对特定的治疗方法，对MIS-C患者进行了合理的分层 由于缺乏来自具有人口代表性的大型队列的数据，这一研究具有挑战性。由于许多卫生系统， 包括我们自己，有少量的儿科MIS-C患者，很难全面了解 以及在单个站点内的信息管理系统-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)的数据描述和 描述MIS-C患者群体特征。摩羯座包括芝加哥的12个医疗系统，其中包括3个 儿科医院和不同的护理环境，并提供访问全面的成像和 实验室检查以及在常规护理期间收集的主要人口学和临床数据 病人。在这项建议中，我们将(1)在威斯康星大学麦迪逊分校和鲁里儿童医院使用具有良好特征的儿科队列 医院将开发算法来识别和表征感染SARS-CoV2后的患者 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 确定特定的临床和实验室属性是否与短期和长期相关 MIS-C结果。因此，本项目将利用海量人群的财富带来病历数据 对在护理患者过程中收集的关键临床数据之间的关系的新见解， SARS-CoV2感染与冠状病毒感染后炎症性疾病发展及严重程度的关系 孩子们。

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.