A clinical case definition of post-COVID-19 condition by a Delphi consensus

Joan B Soriano, Srinivas Murthy, John C Marshall, Pryanka Relan, Janet V Diaz, WHO Clinical Case Definition Working Group on Post-COVID-19 Condition, Joan B Soriano, Srinivas Murthy, John C Marshall, Pryanka Relan, Janet V Diaz, WHO Clinical Case Definition Working Group on Post-COVID-19 Condition

Abstract

People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.

Conflict of interest statement

Declaration of interests We declare no competing interests.

Copyright © 2022 World Health Organization. Published by Elsevier Ltd.All rights reserved. Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1
Figure 1
STROBE flowchart of participation in the two Delphi rounds
Figure 2
Figure 2
Distribution of participants worldwide Distribution of participants in May and June, 2021.
Figure 3
Figure 3
Domains that achieved consensus by participants in each Delphi stage

References

    1. WHO World health statistics. 2021.
    1. The Royal Society Long Covid: what is it, and what is needed? Oct 23, 2020.
    1. Thompson EJ, Williams DM, Walker AJ, et al. Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK. medRxiv. 2021 doi: 10.1101/2021.06.24.21259277. published online Jan 1. (preprint).
    1. Whitaker M, Elliott J, Chadeau-Hyam M, et al. Persistent symptoms following SARS-CoV-2 infection in a random community sample of 508,707 people. medRxiv. 2021 doi: 10.1101/2021.06.28.21259452. published online Jan 1. (preprint).
    1. Dennis A, Wamil M, Alberts J, et al. Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study. BMJ Open. 2021;11
    1. WHO Emergency use ICD codes for COVID-19 disease outbreak.
    1. Diaz JV, Soriano JB. A Delphi consensus to advance on a clinical case definition for post covid-19 condition: a WHO protocol. Protoc Exch. 2021 doi: 10.21203/rs.3.pex-1480/v1. published online June 25.
    1. Dalkey N, Helmer O. An experimental application of the Delphi method to the use of experts. Manage Sci. 1963;9:458–467.
    1. Brown BB. Delphi process: a methodology used for the elicitation of opinions of experts. 1968.
    1. Green KC, Armstrong JS, Graefe A. Methods to elicit forecasts from groups: Delphi and prediction markets compared. 2007.
    1. Rowe G, Wright G. The Delphi technique as a forecasting tool: issues and analysis. Int J Forecast. 1999;15:353–375.
    1. COMET Initiative Delphi Manager.
    1. Murphy E, Black N, Lamping D, et al. Consensus development methods, and their use in clinical guideline development: a review. Health Technol Assess. 1998;2:i–iv. 1-88.
    1. Davis HE, Assaf GS, McCorkell L, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinMed. 2021;38
    1. Barber C. The problem of ‘long haul’ COVID. Sci Am. Dec 29, 2020
    1. Shanbehzadeh M, Kazemi-Arpanahi H, Mazhab-Jafari K, Haghiri H. Coronavirus disease 2019 (COVID-19) surveillance system: development of COVID-19 minimum data set and interoperable reporting framework. J Educ Health Promot. 2020;9:203.
    1. Nasa P, Azoulay E, Khanna AK, et al. Expert consensus statements for the management of COVID-19-related acute respiratory failure using a Delphi method. Crit Care. 2021;25:106.
    1. Schell CO, Khalid K, Wharton-Smith A, et al. Essential emergency and critical care: a consensus among global clinical experts. BMJ Glob Health. 2021;6
    1. Alwan NA, Burgess RA, Ashworth S, et al. Scientific consensus on the COVID-19 pandemic: we need to act now. Lancet. 2020;396:e71–e72.
    1. Davis HE, Assaf GS, McCorkell L, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinMed. 2021;38
    1. Burns KE, Duffett M, Kho ME, et al. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ. 2008;179:245–252.
    1. The Lancet Respiratory Medicine COVID-19 pathophysiology: looking beyond acute disease. Lancet Respir Med. 2021;9:545.
    1. Sakurai A, Sasaki T, Kato S, et al. Natural history of asymptomatic SARS-CoV-2 infection. N Engl J Med. 2020;383:885–886.
    1. Rando HM, Bennett TD, Byrd JB, et al. Challenges in defining long COVID: striking differences across literature, electronic health records, and patient-reported information. medRxiv. 2021 doi: 10.1101/2021.03.20.21253896. published online March 26. (preprint).
    1. Iqbal FM, Lam K, Sounderajah V, Clarke JM, Ashrafian H, Darzi A. Characteristics and predictors of acute and chronic post-COVID syndrome: a systematic review and meta-analysis. EClinMed. 2021;36
    1. Soriano JB, Waterer G, Peñalvo JL, Rello J. Nefer, Sinuhe and clinical research assessing post COVID-19 condition. Eur Respir J. 2021;57
    1. Sun C, Hong S, Song M, Li H, Wang Z. Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning. BMC Med Inform Decis Mak. 2021;21:45.
    1. The Lancet Digital Health Artificial intelligence for COVID-19: saviour or saboteur? Lancet Digit Health. 2021;3:e1.
    1. Muller JE, Nathan DG. COVID-19, nuclear war, and global warming: lessons for our vulnerable world. Lancet. 2020;395:1967–1968.
    1. Norton A, Olliaro P, Sigfrid L, et al. Long COVID: tackling a multifaceted condition requires a multidisciplinary approach. Lancet Infect Dis. 2021;21:601–602.
    1. Lerner AM, Robinson DA, Yang L, et al. Toward understanding COVID-19 recovery: National Institutes of Health Workshop on Postacute COVID-19. Ann Intern Med. 2021;174:999–1003.
    1. Diaz JV, Herridge M, Bertagnolio S, et al. Towards a universal understanding of post COVID-19 condition. Bull World Health Organ. 2021;99:901–903.

Source: PubMed

3
購読する