Cargando…

Exploring the Clinical Utility of Metagenomic Next-Generation Sequencing in the Diagnosis of Pulmonary Infection

INTRODUCTION: We aimed to explore the real-world clinical application value and challenges of metagenomic next-generation sequencing (mNGS) for pulmonary infection diagnosis. METHODS: We retrospectively reviewed the results of mNGS and conventional tests from 140 hospitalized patients with suspected...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Guijuan, Zhao, Bo, Wang, Xun, Bao, Liang, Xu, Yiming, Ren, Xian, Ji, Jiali, He, Ting, Zhao, Hongqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322361/
https://www.ncbi.nlm.nih.gov/pubmed/34117999
http://dx.doi.org/10.1007/s40121-021-00476-w
Descripción
Sumario:INTRODUCTION: We aimed to explore the real-world clinical application value and challenges of metagenomic next-generation sequencing (mNGS) for pulmonary infection diagnosis. METHODS: We retrospectively reviewed the results of mNGS and conventional tests from 140 hospitalized patients with suspected pulmonary infections from January 2019 to December 2020. The sample types included bronchoalveolar lavage fluid, lung tissue by transbronchial lung biopsy, pleural effusion, blood, and bronchial sputum. Apart from the mNGS reports that our patients received, an extra comprehensive and thorough literature search was conducted. RESULTS: Significant differences were noticed in the positive detection rates of pathogens between mNGS and conventional diagnostic testing (115/140, 82.14% vs 50/140, 35.71%, P < 0.05). The percentage of mNGS-positive patients was significantly higher than that of conventional testing-positive patients with regard to bacterial detection (P < 0.01), but no significant differences were found with regard to fungal detection (P = 0.67). Significant statistical differences were found between mixed infection cases (15, 22.70%) and single infection cases (4, 7.84%) in terms of diabetes (P = 0.03). The most frequent pattern of mixed infection was bacteria and fungi mixed infection (40, 40/89 = 44.94%), followed by bacteria mixed infection (29, 29/89 = 32.58%). The sensitivity of mNGS in pulmonary infection diagnosis was much higher than that of conventional test (89.17% vs 50.00%; P < 0.01), but the specificity was the opposite (75.00% vs 81.82%; P > 0.05). CONCLUSION: mNGS is a valuable tool for the detection of pulmonary infections, especially mixed pulmonary infections. The most common combinations we found were bacterial–fungal coinfection and bacterial–bacterial coinfection. Still, there are many challenges in the clinical application of mNGS in the diagnosis of pulmonary infections. There is still a lot of work to be done in interpreting the mNGS reports, because both clinical judgment and literature analysis strategy need to be refined.