Understanding pediatric Long COVID using a tree-based scan statistic approach: An EHR-based cohort study from the RECOVER Program
Lorman, V; Rao, S; Jhaveri, R; et al., JAMIA Open
Published
April 2023
Journal
JAMIA Open
Abstract
Objectives: Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC. Materials and methods: We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N = 1309) to children with (N = 6545) and without (N = 6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls. Results: We found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise. Discussion: Our study addresses methodological limitations of prior studies that rely on prespecified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes. Conclusion: We identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.
Authors
Vitaly Lorman, Suchitra Rao, Ravi Jhaveri, Abigail Case, Asuncion Mejias, Nathan M Pajor, Payal Patel, Deepika Thacker, Seuli Bose-Brill, Jason Block, Patrick C Hanley, Priya Prahalad, Yong Chen, Christopher B Forrest, L Charles Bailey, Grace M Lee, Hanieh Razzaghi
Keywords
COVID-19; long COVID; post-acute sequelae of SARS-CoV-2 infection
Short Summary
RECOVER researchers wanted to identify conditions and symptoms associated with Long COVID in children (also known as pediatric Long COVID). The researchers analyzed electronic health record (EHR) data to detect conditions and symptoms associated with pediatric Long COVID. The study identified multiple conditions and body systems associated with pediatric Long COVID related to many different organs, such as heart and lung problems.
This research is important because these findings use a data-driven approach to detect several new or under-reported conditions and symptoms that should be studied further. Researchers believe that further study may reveal the biological processes that cause these Long COVID symptoms and conditions.