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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; Canas, Liane S; Molteni, Erika; Modat, Marc; Jorge Cardoso, M; May, Anna; Ganesh, Sajaysurya; Davies, Richard; Nguyen, Long H; Drew, David A; Astley, Christina M; Joshi, Amit D; Merino, Jordi; Tsereteli, Neli; Fall, Tove; Gomez, Maria F; Duncan, Emma L; Menni, Cristina; Williams, Frances M K; Franks, Paul W; Chan, Andrew T; Wolf, Jonathan; Ourselin, Sebastien; Spector, Tim; Steves, Claire J.
  • Sudre CH; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Murray B; MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK.
  • Varsavsky T; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
  • Graham MS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Penfold RS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Bowyer RC; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Pujol JC; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
  • Klaser K; Zoe Global, London, UK.
  • Antonelli M; Zoe Global, London, UK.
  • Canas LS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Molteni E; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Modat M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Jorge Cardoso M; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • May A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Ganesh S; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Davies R; Zoe Global, London, UK.
  • Nguyen LH; Zoe Global, London, UK.
  • Drew DA; Zoe Global, London, UK.
  • Astley CM; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Joshi AD; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Merino J; Division of Endocrinology & Computational Epidemiology, Boston Children's Hospital, Boston, MA, USA.
  • Tsereteli N; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Fall T; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Gomez MF; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
  • Duncan EL; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Menni C; Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden.
  • Williams FMK; Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Franks PW; Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden.
  • Chan AT; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
  • Wolf J; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
  • Ourselin S; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
  • Spector T; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
  • Steves CJ; Lund University Diabetes Centre, Department of Clinical Sciences, Malmö, Sweden.
Nat Med ; 27(4): 626-631, 2021 04.
Article in English | MEDLINE | ID: covidwho-1127166
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 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Nat Med Journal subject: Molecular Biology / Medicine Year: 2021 Document Type: Article Affiliation country: S41591-021-01292-y

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Nat Med Journal subject: Molecular Biology / Medicine Year: 2021 Document Type: Article Affiliation country: S41591-021-01292-y