ArticleFrontiers in cellular and infection microbiology2022
Cellular analysis and metagenomic next-generation sequencing of bronchoalveolar lavage fluid in the distinction between pulmonary non-infectious and infectious disease.
Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 6 citations in OpenAlex.
- Synaptic aging and neurodegeneration: the role of synaptic vesicle dynamics and neurotransmitter imbalance.Biogerontology · 2026Review
- Bronchoalveolar lavage fluid in immune checkpoint inhibitor-related pneumonitis: from pathophysiological window to a precision diagnostic tool.Frontiers in immunology · 2026Review
- Diagnostic test accuracy of cellular analysis of bronchoalveolar lavage fluid in distinguishing pulmonary infectious and non-infectious diseases in patients with pulmonary shadow.Frontiers in medicine · 2024Article
- Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.Frontiers in cellular and infection microbiology · 2024Article
- Blood plasma metagenomic next-generation sequencing for identifying pathogens of febrile neutropenia in acute leukemia patients.Scientific reports · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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Abstract
Background: The aim of the current study was to investigate the clinical value of cellular analysis and metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) in differentiating pulmonary non-infectious and infectious diseases in immunocompetent patients. Methods: The present retrospective study was conducted from December 2017 to March 2020, and included immunocompetent patients with suspected pulmonary infection. High-resolution computed tomography, total cell counts and classification of BALF, conventional microbiological tests (CMTs), laboratory tests and mNGS of BALF were performed. Patients were assigned to pulmonary non-infectious disease (PNID) and pulmonary infectious disease (PID) groups based on final diagnoses. PNID-predictive values were analyzed Results: A total of 102 patients suspected of pulmonary infection were enrolled in the study, 23 (22.5%) with PNID and 79 (77.5%) with PID. The diagnostic efficiency of BALF mNGS for differentiating PID from PNID was better than that of CMTs. Neutrophil percentage (N%) and the ratio of neutrophils to lymphocytes (N/L) in BALF were significantly lower in the PNID group than in the PID group. The AUCs for distinguishing PNID and PID were 0.739 (95% confidence interval [CI] 0.636-0.825) for BALF N%, 0.727 (95% CI 0.624-0.815) for BALF N/L, and 0.799 (95% CI 0.702-0.876) for BALF mNGS, with respective cutoff values of 6.7%, 0.255, and negative. Joint models of BALF mNGS combined with BALF N/L or BALF N% increased the respective AUCs to 0.872 (95% CI 0.786-0.933) and 0.871 (95% CI 0.784-0.932), which were significantly higher than those for BALF mNGS, BALF N%, and BALF N/L alone. Conclusions: BALF N% ≤ 6.7% or BALF N/L ≤ 0.255 combined with a negative BALF mNGS result can effectively distinguish PNID from PID in immunocompetent patients with suspected pulmonary infection. BALF mNGS outperforms CMTs for identifying pathogens in immunocompetent patients, and the combination of mNGS and CMTs may be a better diagnostic strategy.
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