Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783481190644187136 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6921575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wangjiahui metagenomicnextgenerationsequencingformixedpulmonaryinfectiondiagnosis AT hanyelei metagenomicnextgenerationsequencingformixedpulmonaryinfectiondiagnosis AT fengjing metagenomicnextgenerationsequencingformixedpulmonaryinfectiondiagnosis |