The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Characterising long COVID: a living systematic review
Open accessAbstract
Background: While it is now apparent clinical sequelae (long COVID) may persist after acute COVID-19, their nature, frequency and aetiology are poorly characterised. This study aims to regularly synthesise evidence on long COVID characteristics, to help inform clinical management, rehabilitation strategies and interventional studies to improve long-term outcomes. Methods: A living systematic review. Medline, CINAHL (EBSCO), Global Health (Ovid), WHO Global Research on COVID-19 database, LitCovid and Google Scholar were searched till 17 March 2021. Studies including at least 100 people with confirmed or clinically suspected COVID-19 at 12 weeks or more post onset were included. Risk of bias was assessed using the tool produced by Hoy et al . Results were analysed using descriptive statistics and meta-analyses to estimate prevalence. Results: A total of 39 studies were included: 32 cohort, 6 cross-sectional and 1 case–control. Most showed high or moderate risk of bias. None were set in low-income countries and few included children. Studies reported on 10 951 people (48% female) in 12 countries. Most included previously hospitalised people (78%, 8520/10 951). The longest mean follow-up time was 221.7 (SD: 10.9) days post COVID-19 onset. Over 60 physical and psychological signs and symptoms with wide prevalence were reported, most commonly weakness (41%; 95% CI 25% to 59%), general malaise (33%; 95% CI 15% to 57%), fatigue (31%; 95% CI 24% to 39%), concentration impairment (26%; 95% CI 21% to 32%) and breathlessness (25%; 95% CI 18% to 34%). 37% (95% CI 18% to 60%) of patients reported reduced quality of life; 26% (10/39) of studies presented evidence of reduced pulmonary function. Conclusion: Long COVID is a complex condition with prolonged heterogeneous symptoms. The nature of studies precludes a precise case definition or risk evaluation. There is an urgent need for prospective, robust, standardised, controlled studies into aetiology, risk factors and biomarkers to characterise long COVID in different at-risk populations and settings. PROSPERO registration number: CRD42020211131.
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Davis, H. E., Assaf, G. S., McCorkell, L., Wei, H., Low, R. J., Re'em, Y., Redfield, S., Austin, J. P., & Akrami, A. (2021) · eClinicalMedicine · doi:10.1016/j.eclinm.2021.101019 · Cited by 2,881
Unexplained post-acute infection syndromes
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Notarte, K. I., Catahay, J. A., Velasco, J. V., Pastrana, A., Ver, A. T., Pangilinan, F. C., Peligro, P. J., Casimiro, M., Guerrero, J. J., Gellaco, M. M. L., et al. (2022) · eClinicalMedicine · doi:10.1016/j.eclinm.2022.101624 · Cited by 480
Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study
Ayoubkhani, D., Bermingham, C., Pouwels, K. B., Glickman, M., Nafilyan, V., Zaccardi, F., Khunti, K., Alwan, N. A., & Walker, A. S. (2022) · BMJ · doi:10.1136/bmj-2021-069676 · Cited by 414
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Metadata
- Authors
- Michelen, M., Manoharan, L., Elkheir, N., Cheng, V., Dagens, A., Hastie, C., O'Hara, M., Suett, J., Dahmash, D., Bugaeva, P., Rigby, I., Munblit, D., Harriss, E., Burls, A., Foote, C., Scott, J., Carson, G., Olliaro, P., Sigfrid, L., & Stavropoulou, C.
- Venue
- BMJ Global Health
- Publisher
- BMJ
- Published
- 2021-09
- Volume
- 6
- Issue
- 9
- Pages
- e005427
- ISSN
- 2059-7908 (ISSN-L)
- OpenAlex
- W3197720849
- Cited by
- 960
- References
- 13
- FWCI
- 30.44
- Type
- journal article
- Language
- en
- Open access
- gold
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- Michelen, M., Manoharan, L., Elkheir, N., Cheng, V., Dagens, A., Hastie, C., O'Hara, M., Suett, J., Dahmash, D., Bugaeva, P., Rigby, I., Munblit, D., Harriss, E., Burls, A., Foote, C., Scott, J., Carson, G., Olliaro, P., Sigfrid, L., & Stavropoulou, C. (2021). Characterising long COVID: a living systematic review. BMJ Global Health. https://doi.org/10.1136/bmjgh-2021-005427
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.