Pediatric Long COVID subphenotypes: An EHR-based study from the RECOVER Program

Caution: This preprint is a work in progress that has not been peer-reviewed. It should not be relied upon to guide clinical practice or health behaviors, and it should not be reported in news media as established information. We will update this web page if this preprint becomes a peer-reviewed publication. (Not all research reports move past the preprint stage.)

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Published

September 2024

Journal

medRxiv

Abstract

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.

Authors

Vitaly Lorman, L Charles Bailey, Xing Song, Suchitra Rao, Mady Hornig, Levon Utidjian, Hanieh Razzaghi, Asuncion Mejias, John Erik Leikauf, Seuli Bose Brill, Andrea Allen, H Timothy Bunnell, Cara Reedy, Abu Saleh Mohammad Mosa, Benjamin D Horne, Carol Reynolds Geary, Cynthia H Chuang, David A Williams, Dimitri A Christakis, Elizabeth A Chrischilles, Eneida A Mendonca, Lindsay G Cowell, Lisa McCorkell, Mei Liu, Mollie R Cummins, Ravi Jhaveri, Saul Blecker, Christopher B Forrest

Keywords

Not available