The Utility of Metagenomic Next-Generation Sequencing (mNGS) in the Management of Patients With Bronchiectasis: A Single-Center Retrospective Study of 93 Cases

Abstract

Abstract Background Bronchiectasis is a chronic inflammatory respiratory disease mainly caused by pathogenic infections. However, standard methods of pathogen detection show prolonged cycle durations and unsatisfactory sensitivity and detection rates. Macrogenomic next-generation sequencing (mNGS) emerges as a promising technique for swift, effective, and unbiased pathogen detection and subsequent data interpretation. Methods Here, a retrospective analysis of 93 patients with suspected bronchiectasis was performed to assess the clinical applicability of mNGS. Bronchoalveolar alveolar lavage fluid (BALF) samples were collected from these subjects, followed by standard assays and mNGS separately. The turnaround time, detection rate, and pathogen identification using mNGS were compared with those of standard methods. Results mNGS identified a greater number of bacteria (72 vs 16), fungi (26 vs 19), and viruses (14 vs 0) than standard methods. Specifically, the commonly identified bacteria were Haemophilus, Mycobacterium intracellulare, Pseudomonas, and Streptococcus pneumoniae, while the most detected fungi were Aspergillus and the most prevalent viruses were human herpesviruses. Of note, 29 out of 30 patients (96.67%) who received optimized treatment strategies based on mNGS results experienced recovery. Conclusions Collectively, these findings suggest that mNGS has the potential to improve the diagnosis and treatment of bronchiectasis patients by enabling rapid and precise pathogen detection, which can lead to timely and effective treatment strategies.