Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Shawn T O'Neil; Charisse Madlock-Brown; Kenneth J Wilkins; Brenda M McGrath; Hannah E Davis; Gina S Assaf; Hannah Wei; Parya Zareie; Evan T French; Johanna Loomba; Julie A McMurry; Andrea Zhou; Christopher G Chute; Richard A Moffitt; Emily R Pfaff; Yun Jae Yoo; Peter Leese; Robert F Chew; Michael Lieberman; Melissa A Haendel; N3C and RECOVER Consortia

doi:10.1038/s41746-024-01286-3

Back

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Journal article

Open access

Peer reviewed

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Shawn T O'Neil, Charisse Madlock-Brown, Kenneth J Wilkins, Brenda M McGrath, Hannah E Davis, Gina S Assaf, Hannah Wei, Parya Zareie, Evan T French, Johanna Loomba, …

NPJ digital medicine, Vol.7(1), 296

10/21/2024

DOI: 10.1038/s41746-024-01286-3

PMCID: PMC11494196

PMID: 39433942

Files and links (1)

url

https://doi.org/10.1038/s41746-024-01286-3View

Published (Version of record) Open Access

Abstract

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Details

Title: Subtitle: Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs
Creators: Shawn T O'Neil - University of North Carolina at Chapel Hill
Charisse Madlock-Brown - University of Tennessee Health Science Center
Kenneth J Wilkins - National Institutes of Health
Brenda M McGrath - Ochin
Hannah E Davis - Patient-Led Research Collaborative
Gina S Assaf - Patient-Led Research Collaborative
Hannah Wei - Patient-Led Research Collaborative
Parya Zareie - University of California, Davis
Evan T French - Virginia Commonwealth University
Johanna Loomba - University of Virginia
Julie A McMurry - University of North Carolina Health Care
Andrea Zhou - University of Virginia
Christopher G Chute - Johns Hopkins University
Richard A Moffitt - Emory University
Emily R Pfaff - University of North Carolina Health Care
Yun Jae Yoo - Emory University
Peter Leese - University of North Carolina Health Care
Robert F Chew - RTI International
Michael Lieberman - Ochin
Melissa A Haendel - University of North Carolina Health Care
N3C and RECOVER Consortia
Resource Type: Journal article
Publication Details: NPJ digital medicine, Vol.7(1), 296
DOI: 10.1038/s41746-024-01286-3
PMID: 39433942
PMCID: PMC11494196
NLM abbreviation: NPJ Digit Med
ISSN: 2398-6352
eISSN: 2398-6352
Publisher: NATURE PORTFOLIO
Grant note: OT2HL161847-01 / U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) TR002306 / U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
Language: English
Date published: 10/21/2024
Academic Unit: Nursing
Record Identifier: 9984738166102771

Metrics

9 Record Views

3 Times Cited - Web of Science

See more details