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A systematic review and meta-analysis of long COVID symptoms
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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220332/ https://www.ncbi.nlm.nih.gov/pubmed/37245047 http://dx.doi.org/10.1186/s13643-023-02250-0 |
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author | Natarajan, Arun Shetty, Ashish Delanerolle, Gayathri Zeng, Yutian Zhang, Yingzhe Raymont, Vanessa Rathod, Shanaya Halabi, Sam Elliot, Kathryn Shi, Jian Qing Phiri, Peter |
author_facet | Natarajan, Arun Shetty, Ashish Delanerolle, Gayathri Zeng, Yutian Zhang, Yingzhe Raymont, Vanessa Rathod, Shanaya Halabi, Sam Elliot, Kathryn Shi, Jian Qing Phiri, Peter |
author_sort | Natarajan, Arun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10220332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102203322023-05-28 A systematic review and meta-analysis of long COVID symptoms Natarajan, Arun Shetty, Ashish Delanerolle, Gayathri Zeng, Yutian Zhang, Yingzhe Raymont, Vanessa Rathod, Shanaya Halabi, Sam Elliot, Kathryn Shi, Jian Qing Phiri, Peter Syst Rev Systematic Review Update 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. BioMed Central 2023-05-27 /pmc/articles/PMC10220332/ /pubmed/37245047 http://dx.doi.org/10.1186/s13643-023-02250-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Systematic Review Update Natarajan, Arun Shetty, Ashish Delanerolle, Gayathri Zeng, Yutian Zhang, Yingzhe Raymont, Vanessa Rathod, Shanaya Halabi, Sam Elliot, Kathryn Shi, Jian Qing Phiri, Peter A systematic review and meta-analysis of long COVID symptoms |
title | A systematic review and meta-analysis of long COVID symptoms |
title_full | A systematic review and meta-analysis of long COVID symptoms |
title_fullStr | A systematic review and meta-analysis of long COVID symptoms |
title_full_unstemmed | A systematic review and meta-analysis of long COVID symptoms |
title_short | A systematic review and meta-analysis of long COVID symptoms |
title_sort | systematic review and meta-analysis of long covid symptoms |
topic | Systematic Review Update |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220332/ https://www.ncbi.nlm.nih.gov/pubmed/37245047 http://dx.doi.org/10.1186/s13643-023-02250-0 |
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