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
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引用次数: 0
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.
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