PLOS digital health · 2025
Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.
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Lorman Vitaly, Bailey L Charles, Song Xing, Rao Suchitra, Hornig Mady, Utidjian Levon, Razzaghi Hanieh, Mejias Asuncion, Leikauf John Erik, Brill Seuli Bose, Allen Andrea, Bunnell H Timothy, Reedy Cara, Mosa Abu Saleh Mohammad, Horne Benjamin D, Geary Carol Reynolds, Chuang Cynthia H, Williams David A, Christakis Dimitri A, Chrischilles Elizabeth A, Mendonca Eneida A, Cowell Lindsay G, McCorkell Lisa, Liu Mei, Cummins Mollie R, Jhaveri Ravi, Blecker Saul, Forrest Christopher B, RECOVER Consortium
Аннотация
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.