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Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis

Open access
Alkodaymi, Mohamad Salim, Omrani, Osama Ali, Ashraf, Nader, Shaar, Bader Abou, Almamlouk, Raghed, Riaz, Muhammad, Obeidat, Mustafa, Obeidat, Yasin, Gerberi, Dana, Taha, Rand M., Kashour, Zakaria, Kashour, Tarek, Berbari, Elie F., Alkattan, Khaled, Tleyjeh, Imad M.·2022·Clinical Microbiology and Infection

Abstract

Background Post-acute coronavirus 2019 (COVID-19) syndrome is now recognized as a complex systemic disease that is associated with substantial morbidity. Objectives To estimate the prevalence of persistent symptoms and signs at least 12 weeks after acute COVID-19 at different follow-up periods. Data sources Searches were conducted up to October 2021 in Ovid Embase, Ovid Medline, and PubMed. Study eligibility criteria, participants and interventions Articles in English that reported the prevalence of persistent symptoms among individuals with confirmed severe acute respiratory syndrome coronavirus 2 infection and included at least 50 patients with a follow-up of at least 12 weeks after acute illness. Methods Random-effect meta-analysis was performed to produce a pooled prevalence for each symptom at four different follow-up time intervals. Between-study heterogeneity was evaluated using the I2 statistic and was explored via meta-regression, considering several a priori study-level variables. Risk of bias was assessed using the Joanna Briggs Institute tool and the Newcastle-Ottawa Scale for prevalence studies and comparative studies, respectively. Results After screening 3209 studies, a total of 63 studies were eligible, with a total COVID-19 population of 257 348. The most commonly reported symptoms were fatigue, dyspnea, sleep disorder, and difficulty concentrating (32%, 25%, 24%, and 22%, respectively, at 3- to <6-month follow-up); effort intolerance, fatigue, sleep disorder, and dyspnea (45%, 36%, 29%, and 25%, respectively, at 6- to <9-month follow-up); fatigue (37%) and dyspnea (21%) at 9 to <12 months; and fatigue, dyspnea, sleep disorder, and myalgia (41%, 31%, 30%, and 22%, respectively, at >12-month follow-up). There was substantial between-study heterogeneity for all reported symptom prevalences. Meta-regressions identified statistically significant effect modifiers: world region, male sex, diabetes mellitus, disease severity, and overall study quality score. Five of six studies including a comparator group consisting of COVID-19–negative cases observed significant adjusted associations between COVID-19 and several long-term symptoms. Conclusions This systematic review found that a large proportion of patients experience post-acute COVID-19 syndrome 3 to 12 months after recovery from the acute phase of COVID-19. However, available studies of post-acute COVID-19 syndrome are highly heterogeneous. Future studies need to have appropriate comparator groups, standardized symptom definitions and measurements, and longer follow-up.

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Metadata

Authors
Alkodaymi, M. S., Omrani, O. A., Ashraf, N., Shaar, B. A., Almamlouk, R., Riaz, M., Obeidat, M., Obeidat, Y., Gerberi, D., Taha, R. M., Kashour, Z., Kashour, T., Berbari, E. F., Alkattan, K., & Tleyjeh, I. M.
Venue
Clinical Microbiology and Infection
Publisher
Elsevier BV
Published
2022-05
Volume
28
Issue
5
Pages
657-666
ISSN
1198-743X (ISSN-L)
OpenAlex
W4210729388
Cited by
532
References
91
FWCI
21.38
Type
journal article
Language
en
Open access
bronze

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Alkodaymi, M. S., Omrani, O. A., Ashraf, N., Shaar, B. A., Almamlouk, R., Riaz, M., Obeidat, M., Obeidat, Y., Gerberi, D., Taha, R. M., Kashour, Z., Kashour, T., Berbari, E. F., Alkattan, K., & Tleyjeh, I. M. (2022). Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clinical Microbiology and Infection. https://doi.org/10.1016/j.cmi.2022.01.014

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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.