ORIGINAL RESEARCH article
Front. Microbiol.
Sec. Infectious Agents and Disease
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1671819
This article is part of the Research TopicRapid and Efficient Analytical Technologies for Pathogen DetectionView all 17 articles
Targeted next-generation sequencing using bronchoalveolar lavage fluid samples for diagnosing pulmonary infections: a single-center retrospective study
Provisionally accepted- 1Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- 2Department of Pulmonary and Critical Care Medicine, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
- 3Department of Clinical Laboratory Medicine, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
- 4Department of Critical Care Medicine, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
- 5Department of Pulmonary and Critical Care Medicine, Fujian Provincial Hospital, Fuzhou, China
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Objective: To evaluate the diagnostic value of tNGS of BALF samples from patients with pulmonary infections (PIs). Methods: This retrospective study included 110 patients diagnosed with suspected PIs, who underwent tNGS of BALF samples. The diagnostic efficiencies of conventional microbiological tests (CMTs), traditional culture, and tNGS were were compared. Results: The detection sensitivities of tNGS, culture, and CMTs for the diagnosis of PIs significantly differed (P < 0.001). Further analysis showed that the sensitivity of tNGS was higher than those of culture (P < 0.001) and CMTs (P = 0.003). The specificity of the culture was higher than those of tNGS (P < 0.001) and CMTs (P < 0.001). However, the accuracy of culture was lower than those of tNGS (P < 0.001) and CMTs (P = 0.022), and the accuracy of CMTs was lower than that of tNGS (P = 0.022). Additionally, the area under the receiver operating characteristic curve for tNGS was better than that for CMTs (0.627 vs. 0.510). Among all causative pathogens, the bacteria were the most prevalent ones, of which the most commonly detected pathogens were Streptococcus pneumoniae (15.2%, 10/66), Pseudomonas aeruginosa (13.6%, 9/66), atypical pathogens (including Chlamydia psittaci, Chlamydia pneumoniae, and Mycoplasma pneumoniae (13.6%, 9/66). The most commonly detected fungi were Pneumocystis jirovecii (40.0%, 4/10) and Aspergillus (40.0%, 4/10), and the most commonly detected viruses were influenza virus (55.6%, 10/18), and SARS-CoV-2 (27.8%, 5/18). The BALF tNGS results led to changes in the clinical plans of 50 (45.5 %) patients. However, the existing clinical management protocol was maintained in 28 patients (25.5 %). Additionally, 32 (29.1 %) patients underwent adjustment of the clinical regimen or an unchanged clinical regimen based on empirical judgement and/or CMT results. Limitations: This study had certain limitations, such as its retrospective design, relatively low specificity, and difficulty in identifying colonizing microorganisms. Conclusion: The sensitivity and accuracy of tNGS were better than those of culture and CMTs. tNGS results were critically associated with the development of clinical treatment plans for most patients. tNGS can be used as a rapid and accurate auxiliary diagnostic method, along with CMTs, for PIs.
Keywords: Targeted next-generation sequencing, Bronchoalveolar Lavage Fluid, pathogen, Diagnosing, pulmonary infection
Received: 25 Jul 2025; Accepted: 23 Sep 2025.
Copyright: © 2025 Huang, Ye, Weng, Jiang, Lin, Li and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hongyan Li, fjsrmyylhy@163.com
Baosong Xie, xbaosong@126.com
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