Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Attributes and predictors of long COVID

Sudre, Carole H. ; Murray, Benjamin ; Varsavsky, Thomas ; Graham, Mark S. ; Penfold, Rose S. ; Bowyer, Ruth C. ; Pujol, Joan Capdevila ; Klaser, Kerstin ; Antonelli, Michela and Canas, Liane S. , et al. (2021) In Nature Medicine 27(4). p.626-631
Abstract

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week... (More)

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Medicine
volume
27
issue
4
pages
6 pages
publisher
Nature Publishing Group
external identifiers
  • pmid:33692530
  • scopus:85102488133
ISSN
1078-8956
DOI
10.1038/s41591-021-01292-y
language
English
LU publication?
yes
id
741fe468-4e67-4997-bf69-e37c863615e2
date added to LUP
2021-03-29 14:35:54
date last changed
2024-06-29 09:58:30
@article{741fe468-4e67-4997-bf69-e37c863615e2,
  abstract     = {{<p>Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app<sup>1</sup>. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.</p>}},
  author       = {{Sudre, Carole H. and Murray, Benjamin and Varsavsky, Thomas and Graham, Mark S. and Penfold, Rose S. and Bowyer, Ruth C. and Pujol, Joan Capdevila and Klaser, Kerstin and Antonelli, Michela and Canas, Liane S. and Molteni, Erika and Modat, Marc and Jorge Cardoso, M. and May, Anna and Ganesh, Sajaysurya and Davies, Richard and Nguyen, Long H. and Drew, David A. and Astley, Christina M. and Joshi, Amit D. and Merino, Jordi and Tsereteli, Neli and Fall, Tove and Gomez, Maria F. and Duncan, Emma L. and Menni, Cristina and Williams, Frances M.K. and Franks, Paul W. and Chan, Andrew T. and Wolf, Jonathan and Ourselin, Sebastien and Spector, Tim and Steves, Claire J.}},
  issn         = {{1078-8956}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{4}},
  pages        = {{626--631}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Medicine}},
  title        = {{Attributes and predictors of long COVID}},
  url          = {{http://dx.doi.org/10.1038/s41591-021-01292-y}},
  doi          = {{10.1038/s41591-021-01292-y}},
  volume       = {{27}},
  year         = {{2021}},
}