Application of metagenomic next-generation sequencing (mNGS) in diagnosing pneumonia of adults
DOI:
https://doi.org/10.3855/jidc.18696Keywords:
Metagenomic next-generation sequencing, conventional method, pulmonary infection, pathogenAbstract
Introduction: Accurate identification of pathogens that cause pulmonary infections is essential for effective treatment and hastening recovery in adults diagnosed with pneumonia. At present, despite metagenomic next-generation sequencing (mNGS) technology has been widely used in clinical practice for pathogen identification, the clinical significance and necessity of detecting pathogen in bronchoalveolar lavage fluid (BALF) for pneumonia-stricken adults remain ambiguous.
Methodology: In this study, 80 patients suffering from pulmonary infection were enrolled, who were admitted to the Affiliated Changzhou Second People’s Hospital of Nanjing Medical University between January 2020 and September 2022. The diagnostic performances of mNGS and conventional methods (CM) were systematically analyzed based on BALF samples, and we further investigated the influence of mNGS and CM in diagnosis modification and treatment.
Results: We found a significantly higher positive rate for the mNGS method in contrast to CM. Bacteria were the most common pathogens, and Streptococcus pneumoniae was the most commonly identified pathogen. Candida albicans and Epstein-Barr virus were the most frequently identified fungus and virus. Atypical pathogens such as Mycobacterium tuberculosis, virus Nontuberculous mycobacteria, and Chlamydia psittaci were also identified. A total of 77 patients were identified with mixed infections by mNGS. As the disease progressed and recurrent antibiotic treatment persisted, significant dynamic changes in the clinical manifestation from the BALF samples could be found by mNGS.
Conclusions: This study underscores the efficacy of mNGS in detecting pathogens in BALF samples from patients suffering pulmonary infections. Compared with the CM, mNGS significantly enhanced the positive diagnosis ratio, particularly in diagnosing Mycobacterium tuberculosis, atypical pathogens, and viral or fungal infections.
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Copyright (c) 2023 Zhiguang Liu, Chuang Sun, Xinru Xiao, Lianzheng Zhou, Yanhua Huang, Yujia Shi, Xiaowei Yin; Zhengdao Mao; Qian Zhang
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Funding data
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“333 Project” of Jiangsu Province
Grant numbers BRA2020015 to Q.Z -
China Postdoctoral Science Foundation
Grant numbers 2020M670011ZX to Zhengdao Mao