Clinical correlation study of non-tuberculous mycobacterial isolates from bronchoalveolar lavage fluid.

Journal: AMB Express
Published:
Abstract

Non-tuberculous mycobacterial (NTM) infections have emerged as a significant public health concern, posing a threat to human health. This study aims to identify various NTM strains from bronchoalveolar lavage fluid, assess their drug resistance profiles, and investigate the risk factors associated with NTM disease. Gene chip technology was used to identify NTM strains. The broth microdilution method assessed the drug sensitivity of isolated NTM pathogenic bacteria, determining their minimum inhibitory concentrations (MICs). Logistic regression analysis identified potential risk factors for NTM disease. Results showed the slow-growing NTM strains isolated from bronchoalveolar lavage fluid to be predominantly Mycobacterium avium and Mycobacterium intracellulare, accounting for 32.05% and 29.49% of the isolates, respectively. The rapidly growing NTM strains were mainly Mycobacterium chelonae and Mycobacterium abscessus, each constituting 25.64% of the isolates. Mycobacterium avium was found to be sensitive to clarithromycin, while linezolid demonstrated high antibacterial efficacy against Mycobacterium intracellulare. In drug susceptibility testing of Mycobacterium chelonae and Mycobacterium abscessus, amikacin exhibited the highest sensitivity, followed by clarithromycin. For patients with NTM-positive cultures, the risk factors for NTM lung disease included age (45-60 years, > 60 years), a smoking history exceeding 10 years, chronic obstructive pulmonary disease (COPD), bronchiectasis, immunocompromised status, and the presence of thin-walled pulmonary cavities. Collectively, this study elucidates the distribution of NTM strains, their drug susceptibility profiles, and key risk factors for NTM lung disease, highlighting the need for proactive screening, early intervention, and targeted preventive strategies to improve diagnosis and optimize treatment outcomes.

Authors
Hongye Ning, Guiqing He, Yanhong Mei, Jiandan Yu, Jichan Shi, Xiaoya Cui, Chaochao Qiu, Xiangao Jiang