The application prospect of metagenomic next-generation sequencing technology in diagnosing suspected lower respiratory tract infections.

OBJECTIVE

Lower respiratory tract infections present substantial diagnostic and therapeutic challenges, negatively impacting individual health. This study aims to utilize metagenomic next-generation sequencing (mNGS) technology to comprehensively explore the spectrum of pathogens, the detection of antibiotic resistance genes, and contributing factors associated with lung infections.

METHOD: The mNGS data of 217 patients with suspected lung infections attending the Respiratory Department of Nanjing Lishui People's Hospital and Gaochun People's Hospital from September 2022 to September 2023 were retrospectively analyzed. The study assessed the pathogenic spectrum of lung infections and compared the performance of patients with mNGS results from conventional microbiological techniques (CMT).

RESULTS

The overall positivity rate of mNGS was 95.20%, demonstrating superior sensitivity (97.01% vs. 41.79%) and accuracy (75.56% vs. 56.67%) compared to CMT. Bacterial infections were the most prevalent, accounting for 60.76% of cases. And the most prevalent bacteria, fungus and virus were Mycobacterium tuberculosis (14.41%), Candida albicans (15.72%), and EB virus (14.85%), respectively. The primary resistance genes detected were tetM (17, 8.29%), mel (6, 2.93%), and PC1 beta-lactamase (blaZ) (3, 1.46%). Notably, TEM-183, PDC-5 and PDC-3 were exclusively detected in the Chronic Obstructive Pulmonary Disease (COPD) group. The multivariate binary logistic regression analysis revealed that there was no significant association between gender, presence of hypertension, or COPD with the type of infection in patients (p=0.679, p=0.229, p=0.345). However, the immune status was found to be statistically significant (p=0.009).

CONCLUSION

With the guidance of mNGS, patients with suspected respiratory tract infections can rapidly and accurately establish a pathogenic basis for their conditions. mNGS effectively identify mixed infections, enrich the pathogen spectrum of lung infections, and provide a large and reliable information base for the clinical realization of targeted medication.