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Multicenter Study
. 2025 Dec;57(1):2514093.
doi: 10.1080/07853890.2025.2514093. Epub 2025 Jun 9.

Changes of respiratory microbiota associated with prognosis in pulmonary infection patients with invasive mechanical ventilation-supported respiratory failure

Yong Sun  1 , Kai Guo  1 , Jing Tang  1 , Junjie Zhao  1 , Xiaojing Zhang  2 , Youqin Yan  3 , Lingmin Yuan  4 , Yi Zhang  5 , Canhu Qiu  6 , Jian Luo  7 , Weiwen Zhang  7 , Honglong Fang  7 , Juan Chen  8
Affiliations
Multicenter Study

Changes of respiratory microbiota associated with prognosis in pulmonary infection patients with invasive mechanical ventilation-supported respiratory failure

Yong Sun et al. Ann Med. 2025 Dec.

Abstract

Background: Respiratory failure (RF) is an important cause of intensive care unit (ICU) admission and mortality due to respiratory diseases. This study aimed to evaluate the clinical performance of metagenomic next-generation sequencing (mNGS) testing in pathogen diagnosis, medication guidance and to explore dynamic changes in the respiratory microbiota associated with prognosis.

Methods: This multicenter retrospective study enrolled ICU patients from five hospitals who underwent invasive mechanical ventilation (IMV) and had pathogenic microorganisms identified by both mNGS and conventional microbiological tests (CMT) from December 2021 to April 2024. Patients were classified into two groups based on discharge outcomes: survivors (n=122) and non-survivors (n=35).

Results: Compared with the survivors, non-survivors had a significantly higher proportion of smokers, dyspnea, type I RF, blood urea nitrogen, and C-reactive protein (p < 0.05). All the above indicators were identified as independent risk factors for mortality, except for type I RF. mNGS showed a better performance for pathogen identification than CMT in both groups, and nearly 60% showed consistent results between the two methods. Among survivors, antibiotic adjustment was mainly based on mNGS results (35.25%), whereas non-survivors more frequently received adjustments based on mNGS and CMT results (34.29%). The richness and abundance of lung microorganisms in the non-survivors were significantly lower than those in the survivors (p < 0.05).

Conclusions: mNGS is a promising method for identifying pathogens in pulmonary infections in IMV-supported RF patients and for exploring changes in lung microbial composition to provide a reference for patient prognosis.

Keywords: Invasive mechanical ventilation; lung microbial; metagenomic next-generation sequencing; pulmonary infection; respiratory failure.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1.
Figure 1.
Flowchart of analyzed patients.
Figure 2.
Figure 2.
Diagnostic performance of mNGS and CMT. (A) Positive rates of mNGS and CMT in the survivors and non-survivors. (B) Types and percentages of infections identified by mNGS and CMT methods. (C) Distribution of infection types identified by mNGS and CMT methods. B, bacteria; F, Fungi; V, virus. (D) Concordance of mNGS and CMT pathogen detection results in the survivors. (E) Concordance of mNGS and CMT pathogen detection results in the non-survivors.
Figure 3.
Figure 3.
Bacterial profiles identified by mNGS and CMT.
Figure 4.
Figure 4.
Fungi and virus profiles identified by mNGS and CMT.
Figure 5.
Figure 5.
Changes in antibiotic therapy resulting from mNGS and CMT etiology results. (A) The percentage of antibiotic therapy resulting from mNGS and CMT in survivors and non-survivors. (B) The type of antibiotic therapy resulting from mNGS and CMT in survivors and non-survivors. *p < 0.05.
Figure 6.
Figure 6.
Changes in microbiota diversity and community type in survivors and non-survivors. (A) Simpson and Shannon even indexes. (B) ANOSIM plots. (C) Average relative abundances of microbial community composition for each group are shown by bar plots. (D) LefSe results between the survivors and non-survivors.
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