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Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

BACKGROUND: Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. METHODS: From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis...

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Autores principales: Wang, Jiahui, Han, Yelei, Feng, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921575/
https://www.ncbi.nlm.nih.gov/pubmed/31856779
http://dx.doi.org/10.1186/s12890-019-1022-4
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author Wang, Jiahui
Han, Yelei
Feng, Jing
author_facet Wang, Jiahui
Han, Yelei
Feng, Jing
author_sort Wang, Jiahui
collection PubMed
description BACKGROUND: Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. METHODS: From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. RESULTS: The sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0–92.5%), and the negative predictive value was 92.3% (95% CI, 62.1–99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. CONCLUSIONS: mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity. TRIAL REGISTRATION: (retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019.
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spelling pubmed-69215752019-12-30 Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis Wang, Jiahui Han, Yelei Feng, Jing BMC Pulm Med Research Article BACKGROUND: Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. METHODS: From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. RESULTS: The sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0–92.5%), and the negative predictive value was 92.3% (95% CI, 62.1–99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. CONCLUSIONS: mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity. TRIAL REGISTRATION: (retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019. BioMed Central 2019-12-19 /pmc/articles/PMC6921575/ /pubmed/31856779 http://dx.doi.org/10.1186/s12890-019-1022-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wang, Jiahui
Han, Yelei
Feng, Jing
Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title_full Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title_fullStr Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title_full_unstemmed Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title_short Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
title_sort metagenomic next-generation sequencing for mixed pulmonary infection diagnosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921575/
https://www.ncbi.nlm.nih.gov/pubmed/31856779
http://dx.doi.org/10.1186/s12890-019-1022-4
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