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. 2024 May 27:17:2465-2474.
doi: 10.2147/IJGM.S459373. eCollection 2024.

Exploring the Application of Metagenomic Next-Generation Sequencing in the Diagnosis of Unexplained Pulmonary Infection

Sida Chen  1 , Ling Wen  1 , Jintao Ou  1 , Yuting Lai  1 , Yan Shen  1
Affiliations

Exploring the Application of Metagenomic Next-Generation Sequencing in the Diagnosis of Unexplained Pulmonary Infection

Sida Chen et al. Int J Gen Med. .

Abstract

Background: Pulmonary infections are significant global health burdens, and conventional diagnostic methods (culture and polymerase chain reaction), are often limited by slow results and low sensitivity. Metagenomic next-generation sequencing (mNGS) offers a rapid, comprehensive alternative for identifying diverse pathogens, including rare and mixed infections. Thus, we assessed the diagnostic performance of mNGS in pulmonary infections, compared the findings with those of traditional pathogen detection methods, and explored its potential to enhance clinical diagnostics and patient care.

Methods: We collected samples from 125 immunocompromised patients diagnosed with pulmonary infection at the Department of Respiratory Medicine of Shenzhen Longgang Central Hospital from March 2020 to July 2022. We compared the rate of pathogen positivity and pathogen distribution between conventional pathogen detection methods and mNGS using samples including sputum, blood, and bronchoalveolar lavage fluid.

Results: Among the 125 cases of unexplained pulmonary infection, 82 (65.6%) and 40 (32.0%) tested positive for pathogens using mNGS and routine culture, respectively (P < 0.05). Both methods of pathogen detection were positive in 28 (22.4%) cases (complete match, 9; complete mismatch, 13; partial match, 6). However, 43.2% of cases only tested positive using mNGS, 9.4% only tested positive using routine tests, and 24.8% tested negative using both methods. A viral infection was present in 55.2% of cases. The detection rate of mycobacteria using mNGS (12.8%) was higher than that using conventional pathogen detection methods (5.6%).

Conclusion: mNGS technology enhances pathogen detection in unexplained pulmonary infections, enabling targeted antimicrobial therapy and consequently helping to reduce broad-spectrum antibiotic use, aligning treatments more closely with the causative pathogens. Thus, mNGS offers significant clinical value by improving treatment efficacy and potentially reducing antibiotic resistance in pulmonary infection cases.

Keywords: BALF; Pathogens; Unexplained pulmonary infection; mNGS.

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Conflict of interest statement

The authors declare that they have no competing interests in this work.

Figures

Figure 1
Figure 1
Flow chart of patient selection.
Figure 2
Figure 2
Detection of pathogens using mNGS and CMTs. (A) Positive mNGS and culture results in 125 patients with pulmonary infection. (B) mNGS and traditional culture methods were positive for 82 and 40 patients, respectively. Among them, 28 cases were positive for both methods, 9 (7%) of which were completely matched.
Figure 3
Figure 3
Spectrum of pathogens detected by mNGS. (A) Distribution of the top 30 pathogens detected in the 125 cases of unexplained pulmonary infection. (B) Distribution of the top 30 pathogens detected in the 18 cases of mixed infection.
Figure 4
Figure 4
Proportion of the top 20 most abundant bacteria identified by mNGS at the genus level.
See this image and copyright information in PMC

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