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Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis
Many common pathogens are difficult or impossible to detect using conventional microbiological tests. However, the rapid and untargeted nature of metagenomic next-generation sequencing (mNGS) appears to be a promising alternative. To perform a systematic review and meta-analysis of evidence regardin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723114/ https://www.ncbi.nlm.nih.gov/pubmed/36470909 http://dx.doi.org/10.1038/s41598-022-25314-y |
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author | Liu, Jian Zhang, Qiao Dong, Yong-Quan Yin, Jie Qiu, Yun-Qing |
author_facet | Liu, Jian Zhang, Qiao Dong, Yong-Quan Yin, Jie Qiu, Yun-Qing |
author_sort | Liu, Jian |
collection | PubMed |
description | Many common pathogens are difficult or impossible to detect using conventional microbiological tests. However, the rapid and untargeted nature of metagenomic next-generation sequencing (mNGS) appears to be a promising alternative. To perform a systematic review and meta-analysis of evidence regarding the diagnostic accuracy of mNGS in patients with infectious diseases. An electronic literature search of Embase, PubMed and Scopus databases was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary receiver operating characteristics (sROC) and the area under the curve (AUC) were calculated; A random-effects model was used in cases of heterogeneity. A total of 20 papers were eligible for inclusion and synthesis. The sensitivity and specificity of diagnostic mNGS were 75% and 68%, respectively. The AUC from the SROC was 85%, corresponding to excellent performance. mNGS demonstrated satisfactory diagnostic performance for infections and yielded an overall detection rate superior to conventional methods. |
format | Online Article Text |
id | pubmed-9723114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97231142022-12-07 Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis Liu, Jian Zhang, Qiao Dong, Yong-Quan Yin, Jie Qiu, Yun-Qing Sci Rep Article Many common pathogens are difficult or impossible to detect using conventional microbiological tests. However, the rapid and untargeted nature of metagenomic next-generation sequencing (mNGS) appears to be a promising alternative. To perform a systematic review and meta-analysis of evidence regarding the diagnostic accuracy of mNGS in patients with infectious diseases. An electronic literature search of Embase, PubMed and Scopus databases was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary receiver operating characteristics (sROC) and the area under the curve (AUC) were calculated; A random-effects model was used in cases of heterogeneity. A total of 20 papers were eligible for inclusion and synthesis. The sensitivity and specificity of diagnostic mNGS were 75% and 68%, respectively. The AUC from the SROC was 85%, corresponding to excellent performance. mNGS demonstrated satisfactory diagnostic performance for infections and yielded an overall detection rate superior to conventional methods. Nature Publishing Group UK 2022-12-05 /pmc/articles/PMC9723114/ /pubmed/36470909 http://dx.doi.org/10.1038/s41598-022-25314-y Text en © The Author(s) 2022 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/) . |
spellingShingle | Article Liu, Jian Zhang, Qiao Dong, Yong-Quan Yin, Jie Qiu, Yun-Qing Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title | Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title_full | Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title_fullStr | Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title_full_unstemmed | Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title_short | Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
title_sort | diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723114/ https://www.ncbi.nlm.nih.gov/pubmed/36470909 http://dx.doi.org/10.1038/s41598-022-25314-y |
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