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Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the
Vitaly Lorman1, Suchitra Rao2, Ravi Jhaveri3
1Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Insights
Pediatric post-acute sequelae of SARS-CoV-2 infection (PASC) involves diverse symptoms. Data mining identified new PASC conditions in cardiac, respiratory, and neurological systems, including dyspnea and fatigue.
Area of Science:
- Pediatric Health
- Infectious Diseases
- Data Science in Medicine
Background:
- Post-acute sequelae of SARS-CoV-2 infection (PASC) presents heterogeneously in children, lacking clear definition.
- Existing PASC research often relies on clinician-driven diagnostic clusters, potentially missing novel associations.
Purpose of the Study:
- To employ data mining techniques to identify conditions and symptoms associated with pediatric PASC.
- To overcome limitations of prior studies by utilizing a data-driven approach rather than clinical experience.
Main Methods:
- A propensity-matched cohort design compared children with a PASC diagnosis code (U09.9) to SARS-CoV-2 infected and uninfected children.
- A tree-based scan statistic was used to detect condition clusters co-occurring more frequently in pediatric PASC cases.
Main Results:
- Significant PASC associations were found across multiple systems: cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal.
- Key findings included dyspnea, difficulty breathing, fatigue, and malaise, particularly within circulatory and respiratory systems.
Conclusions:
- The study identified numerous conditions and body systems linked to pediatric PASC using a novel data-driven methodology.
- Several under-reported symptoms and conditions warrant further investigation to better understand and phenotype pediatric PASC.
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.
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