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Meta-Analysis
. 2023 May 27;12(1):88.
doi: 10.1186/s13643-023-02250-0.

A systematic review and meta-analysis of long COVID symptoms

Arun Natarajan #  1 , Ashish Shetty #  2   3 , Gayathri Delanerolle #  4 , Yutian Zeng #  5 , Yingzhe Zhang #  6 , Vanessa Raymont  7 , Shanaya Rathod  8 , Sam Halabi #  9 , Kathryn Elliot #  8 , Jian Qing Shi #  5 , Peter Phiri  10   11
Affiliations
Meta-Analysis

A systematic review and meta-analysis of long COVID symptoms

Arun Natarajan et al. Syst Rev. .

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.

Keywords: Autonomic dysfunction; Gastrointestinal; Long COVID; Neurology; Neuropsychiatry; Pain.

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Conflict of interest statement

PP has received a research grant from Novo Nordisk, and the other, educational from Queen Mary University of London, from John Wiley & Sons, and other from Otsuka, outside the submitted work. SR reports research funding associated with other studies from Janssen, Otsuka, and Lundbeck. AS reports funding associated with other projects from Medtronic. GD reports research funding associated with NIHR RCF. VR reports funding associated with other studies from the Medical Research Council. This research is based on evidence gathered systematically and has not been influenced unduly by expertise. All other authors declare that they have no competing interests.

The views expressed are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, Department of Health and Social Care or Academic institutions.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram
Fig. 2
Fig. 2
Summary of studies included in meta-analysis
Fig. 3
Fig. 3
Forest plots for general symptoms
Fig. 4
Fig. 4
Forest plots for neurological symptoms
Fig. 5
Fig. 5
Forest plots for mental health symptoms
Fig. 6
Fig. 6
Forest plots for cardiopulmonary symptoms
Fig. 7
Fig. 7
Forest plots for gastrointestinal symptoms
Fig. 8
Fig. 8
Forest plots of subgroup analysis
Fig. 9
Fig. 9
Funnel plots of eight symptoms (reported in more than 10 studies)
Fig. 10
Fig. 10
P values for residual selection bias
See this image and copyright information in PMC

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