Attributes and predictors of long COVID

被引：1576

作者：

Sudre, Carole H. ^{[1
,2
,3
]}

Murray, Benjamin ^{[3
]}

Varsavsky, Thomas ^{[3
]}

Graham, Mark S. ^{[3
]}

Penfold, Rose S. ^{[4
]}

Bowyer, Ruth C. ^{[5
]}

Pujol, Joan Capdevila ^{[5
]}

Klaser, Kerstin ^{[3
]}

Antonelli, Michela ^{[3
]}

Canas, Liane S. ^{[3
]}

Molteni, Erika ^{[3
]}

Modat, Marc ^{[3
]}

Cardoso, M. Jorge ^{[3
]}

May, Anna ^{[5
]}

Ganesh, Sajaysurya ^{[5
]}

Davies, Richard ^{[5
]}

Nguyen, Long H. ^{[6
]}

Drew, David A. ^{[6
]}

Astley, Christina M. ^{[7
]}

Joshi, Amit D. ^{[6
]}

Merino, Jordi ^{[8
,9
,10
,11
]}

Tsereteli, Neli ^{[12
]}

Fall, Tove ^{[13
]}

Gomez, Maria F. ^{[12
]}

Duncan, Emma L. ^{[4
]}

Menni, Cristina ^{[4
]}

Williams, Frances M. K. ^{[4
]}

Franks, Paul W. ^{[4
,12
]}

Chan, Andrew T. ^{[6
]}

Wolf, Jonathan ^{[5
]}

Ourselin, Sebastien ^{[3
,14
]}

Spector, Tim ^{[4
]}

Steves, Claire J. ^{[4
]}

机构：

[1] Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England

[2] UCL, Dept Populat Hlth Sci, MRC Unit Lifelong Hlth & Ageing, London, England

[3] UCL, Dept Comp Sci, Ctr Med Image Comp, London, England

[4] Kings Coll London, Dept Twin Res & Genet Epidemiol, London, England

[5] Zoe Global, London, England

[6] Massachusetts Gen Hosp, Clin & Translat Epidemiol Unit, Boston, MA 02114 USA

[7] Boston Childrens Hosp, Div Endocrinol & Computat Epidemiol, Boston, MA USA

[8] Massachusetts Gen Hosp, Diabet Unit, Boston, MA 02114 USA

[9] Massachusetts Gen Hosp, Ctr Genom Med, Boston, MA 02114 USA

[10] Broad Inst, Program Med & Populat Genet, Cambridge, MA USA

[11] Harvard Med Sch, Dept Med, Boston, MA 02115 USA

[12] Lund Univ, Dept Clin Sci, Diabet Ctr, Malmo, Sweden

[13] Uppsala Univ, Dept Med Sci, Mol Epidemiol, Uppsala, Sweden

[14] Univ Cote dAzur, AI Inst 3IA Cote dAzur, Nice, France

来源：

NATURE MEDICINE | 2021年 / 27卷 / 04期

基金：

欧盟地平线“2020”; 英国惠康基金; 英国科研创新办公室; 英国医学研究理事会; 瑞典研究理事会;

关键词：

D O I：

10.1038/s41591-021-01292-y

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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(1). 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.

引用

页码：626 / 631

页数：20

共 12 条

[11]

Sudre CH, 2020, Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app, V2020, P12

[12] Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19) A Review [J].

Wiersinga, W. Joost ;

Rhodes, Andrew ;

Cheng, Allen C. ;

Peacock, Sharon J. ;

Prescott, Hallie C. .

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 324 (08) :782-793

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