Pediatric Long COVID Subphenotypes: An EHR-based Study from the RECOVER Program

Overview

Journal medRxiv

Date 2024 Oct 7

PMID 39371163

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

Affiliations

Soon will be listed here.

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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