Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program

Vitaly Lorman¹, Suchitra Rao², Ravi Jhaveri³, Abigail Case⁴, Asuncion Mejias⁵, Nathan M Pajor⁶, Payal Patel⁷, Deepika Thacker⁸, Seuli Bose-Brill⁹, Jason Block¹⁰, Patrick C Hanley¹¹, Priya Prahalad¹², Yong Chen¹³, Christopher B Forrest¹, L Charles Bailey¹, Grace M Lee¹⁴, Hanieh Razzaghi¹

Affiliations

¹ Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
² Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital of Colorado, Aurora, Colorado, USA.
³ Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.
⁴ Division of Physical Medicine & Rehabilitation, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
⁵ Division of Infectious Diseases, Department of Pediatrics, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio, USA.
⁶ Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
⁷ Department of Neurology, University of Washington, Seattle, Washington, USA.
⁸ Nemours Cardiac Center, Nemours Children's Health, Wilmington, Delaware, USA.
⁹ Internal Medicine and Pediatrics Section, Division of General Internal Medicine, Department of Internal Medicine, Ohio State University College of Medicine and Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
¹⁰ Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Division of Endocrinology, Nemours Children's Hospital, Wilmington, Delaware, USA.
¹² Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.
¹³ Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
¹⁴ Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

PMID: 36926600
PMCID: PMC10013630
DOI: 10.1093/jamiaopen/ooad016

Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program

Vitaly Lorman et al. JAMIA Open. 2023.

. 2023 Mar 14;6(1):ooad016.

doi: 10.1093/jamiaopen/ooad016. eCollection 2023 Apr.

Authors

Affiliations

¹ Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
² Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital of Colorado, Aurora, Colorado, USA.
³ Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.
⁴ Division of Physical Medicine & Rehabilitation, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
⁵ Division of Infectious Diseases, Department of Pediatrics, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio, USA.
⁶ Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
⁷ Department of Neurology, University of Washington, Seattle, Washington, USA.
⁸ Nemours Cardiac Center, Nemours Children's Health, Wilmington, Delaware, USA.
⁹ Internal Medicine and Pediatrics Section, Division of General Internal Medicine, Department of Internal Medicine, Ohio State University College of Medicine and Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
¹⁰ Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Division of Endocrinology, Nemours Children's Hospital, Wilmington, Delaware, USA.
¹² Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.
¹³ Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
¹⁴ Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

PMID: 36926600
PMCID: PMC10013630
DOI: 10.1093/jamiaopen/ooad016

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.

Keywords: COVID-19; long COVID; post-acute sequelae of SARS-CoV-2 infection.

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Figures

**Figure 1.**
Significant branches of the R00–R09 block of the ICD hierarchy with nodes colored by percent excess cases.

See this image and copyright information in PMC

References

1. RECOVER: Researching COVID to Enhance Recovery. https://recovercovid.org. Accessed July 10, 2022.
1. Younger DS. Post-acute sequelae of SARS-CoV-2 infection (PASC): peripheral, autonomic, and central nervous system features in a child. Neurol Sci 2021; 42 (10): 3959–63. - PMC - PubMed
1. Pfaff ER, Girvin AT, Bennett TD, et al. ; N3C Consortium. Identifying who has long COVID in the USA: a machine learning approach using N3C data. Lancet Digit Health 2022; 4 (7): e532–41. - PMC - PubMed
1. Munipalli B, Seim L, Dawson NL, Knight D, Dabrh AMA.. Post-acute sequelae of COVID-19 (PASC): a meta-narrative review of pathophysiology, prevalence, and management. SN Compr Clin Med 2022; 4 (1): 90. - PMC - PubMed
1. Reese J, Blau H, Casiraghi E, et al. Generalizable long COVID subtypes: Findings from the NIH N3C and RECOVER. eBioMedicine2023; 87: 104413. doi: 10.1016/j.ebiom.2022.104413. - DOI - PMC - PubMed

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Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program

Affiliations

Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program

Authors

Affiliations

Abstract

Figures

References

LinkOut - more resources

Full Text Sources

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