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

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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.