Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.
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 CO...
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| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000747 |
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| author | 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 RECOVER Consortium |
| author_facet | 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 RECOVER Consortium |
| author_sort | Vitaly Lorman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1abf7de51a3b4d1da2a94ad1008c92a3 |
| institution | DOAJ |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-1abf7de51a3b4d1da2a94ad1008c92a32025-08-20T03:13:50ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-04-0144e000074710.1371/journal.pdig.0000747Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.Vitaly LormanL Charles BaileyXing SongSuchitra RaoMady HornigLevon UtidjianHanieh RazzaghiAsuncion MejiasJohn Erik LeikaufSeuli Bose BrillAndrea AllenH Timothy BunnellCara ReedyAbu Saleh Mohammad MosaBenjamin D HorneCarol Reynolds GearyCynthia H ChuangDavid A WilliamsDimitri A ChristakisElizabeth A ChrischillesEneida A MendoncaLindsay G CowellLisa McCorkellMei LiuMollie R CumminsRavi JhaveriSaul BleckerChristopher B ForrestRECOVER ConsortiumPediatric 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.https://doi.org/10.1371/journal.pdig.0000747 |
| spellingShingle | 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 RECOVER Consortium Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. PLOS Digital Health |
| title | Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. |
| title_full | Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. |
| title_fullStr | Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. |
| title_full_unstemmed | Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. |
| title_short | Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. |
| title_sort | pediatric long covid subphenotypes an ehr based study from the recover program |
| url | https://doi.org/10.1371/journal.pdig.0000747 |
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