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Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis

Vaccine protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres; however, this has not yet been demonstrated for severe COVID-19. To explore whether this relationship also holds for severe COVID-19, we performed a systematic search...

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Autores principales: Cromer, Deborah, Steain, Megan, Reynaldi, Arnold, Schlub, Timothy E., Khan, Shanchita R., Sasson, Sarah C., Kent, Stephen J., Khoury, David S., Davenport, Miles P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036966/
https://www.ncbi.nlm.nih.gov/pubmed/36964146
http://dx.doi.org/10.1038/s41467-023-37176-7
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author Cromer, Deborah
Steain, Megan
Reynaldi, Arnold
Schlub, Timothy E.
Khan, Shanchita R.
Sasson, Sarah C.
Kent, Stephen J.
Khoury, David S.
Davenport, Miles P.
author_facet Cromer, Deborah
Steain, Megan
Reynaldi, Arnold
Schlub, Timothy E.
Khan, Shanchita R.
Sasson, Sarah C.
Kent, Stephen J.
Khoury, David S.
Davenport, Miles P.
author_sort Cromer, Deborah
collection PubMed
description Vaccine protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres; however, this has not yet been demonstrated for severe COVID-19. To explore whether this relationship also holds for severe COVID-19, we performed a systematic search for studies reporting on protection against different SARS-CoV-2 clinical endpoints and extracted data from 15 studies. Since matched neutralising antibody titres were not available, we used the vaccine regimen, time since vaccination and variant of concern to predict corresponding neutralising antibody titres. We then compared the observed vaccine effectiveness reported in these studies to the protection predicted by a previously published model of the relationship between neutralising antibody titre and vaccine effectiveness against severe COVID-19. We find that predicted neutralising antibody titres are strongly correlated with observed vaccine effectiveness against symptomatic (Spearman [Formula: see text] = 0.95, p < 0.001) and severe (Spearman [Formula: see text] = 0.72, p < 0.001 for both) COVID-19 and that the loss of neutralising antibodies over time and to new variants are strongly predictive of observed vaccine protection against severe COVID-19.
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spelling pubmed-100369662023-03-24 Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis Cromer, Deborah Steain, Megan Reynaldi, Arnold Schlub, Timothy E. Khan, Shanchita R. Sasson, Sarah C. Kent, Stephen J. Khoury, David S. Davenport, Miles P. Nat Commun Article Vaccine protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres; however, this has not yet been demonstrated for severe COVID-19. To explore whether this relationship also holds for severe COVID-19, we performed a systematic search for studies reporting on protection against different SARS-CoV-2 clinical endpoints and extracted data from 15 studies. Since matched neutralising antibody titres were not available, we used the vaccine regimen, time since vaccination and variant of concern to predict corresponding neutralising antibody titres. We then compared the observed vaccine effectiveness reported in these studies to the protection predicted by a previously published model of the relationship between neutralising antibody titre and vaccine effectiveness against severe COVID-19. We find that predicted neutralising antibody titres are strongly correlated with observed vaccine effectiveness against symptomatic (Spearman [Formula: see text] = 0.95, p < 0.001) and severe (Spearman [Formula: see text] = 0.72, p < 0.001 for both) COVID-19 and that the loss of neutralising antibodies over time and to new variants are strongly predictive of observed vaccine protection against severe COVID-19. Nature Publishing Group UK 2023-03-24 /pmc/articles/PMC10036966/ /pubmed/36964146 http://dx.doi.org/10.1038/s41467-023-37176-7 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cromer, Deborah
Steain, Megan
Reynaldi, Arnold
Schlub, Timothy E.
Khan, Shanchita R.
Sasson, Sarah C.
Kent, Stephen J.
Khoury, David S.
Davenport, Miles P.
Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title_full Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title_fullStr Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title_full_unstemmed Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title_short Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis
title_sort predicting vaccine effectiveness against severe covid-19 over time and against variants: a meta-analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036966/
https://www.ncbi.nlm.nih.gov/pubmed/36964146
http://dx.doi.org/10.1038/s41467-023-37176-7
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