A systematic review and meta-analysis of long COVID symptoms

Author:

Natarajan Arun,Shetty Ashish,Delanerolle Gayathri,Zeng Yutian,Zhang Yingzhe,Raymont Vanessa,Rathod Shanaya,Halabi Sam,Elliot Kathryn,Shi Jian Qing,Phiri PeterORCID

Abstract

Abstract Background Ongoing symptoms or the development of new symptoms following a SARS-CoV-2 diagnosis has caused a complex clinical problem known as “long COVID” (LC). This has introduced further pressure on global healthcare systems as there appears to be a need for ongoing clinical management of these patients. LC personifies heterogeneous symptoms at varying frequencies. The most complex symptoms appear to be driven by the neurology and neuropsychiatry spheres. Methods A systematic protocol was developed, peer reviewed, and published in PROSPERO. The systematic review included publications from the 1st of December 2019–30th June 2021 published in English. Multiple electronic databases were used. The dataset has been analyzed using a random-effects model and a subgroup analysis based on geographical location. Prevalence and 95% confidence intervals (CIs) were established based on the data identified. Results Of the 302 studies, 49 met the inclusion criteria, although 36 studies were included in the meta-analysis. The 36 studies had a collective sample size of 11,598 LC patients. 18 of the 36 studies were designed as cohorts and the remainder were cross-sectional. Symptoms of mental health, gastrointestinal, cardiopulmonary, neurological, and pain were reported. Conclusions The quality that differentiates this meta-analysis is that they are cohort and cross-sectional studies with follow-up. It is evident that there is limited knowledge available of LC and current clinical management strategies may be suboptimal as a result. Clinical practice improvements will require more comprehensive clinical research, enabling effective evidence-based approaches to better support patients.

Publisher

Springer Science and Business Media LLC

Subject

Medicine (miscellaneous)

Reference15 articles.

1. Centers for Disease Control and Prevention. Post-COVID Conditions. 2021. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects.html.

2. Lancet T. Facing up to long COVID. Lancet. 2020;396:1861.

3. Carfì A, Bernabei R, Landi F, for the Gemelli Against COVID-19 Post-Acute Care Study Group. Persistent symptoms in patients after acute COVID-19. JAMA. 2020;324:603–5.

4. National Institute for Health and Care Excellence (NICE). OVID-19 rapid guideline: managing the long-term effects of COVID-19. 2020. Overview | COVID-19 rapid guideline: managing the long-term effects of COVID-19 | Guidance | NICE.

5. Darley DR, Dore GJ, Byrne AL, et al. Limited recovery from post-acute sequelae of SARS-CoV-2 at 8 months in a prospective cohort. ERJ Open Res. 2021;7:00384–2021. https://doi.org/10.1183/23120541.00384-2021.

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