Frontiers in cellular and infection microbiology · 2026
Clinical utility of targeted next-generation sequencing for rapid detection, species differentiation, drug resistance profiling, and prognostic evaluation in Mycobacterium tuberculosis: a multi-center retrospective cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Shi Li, Ni Mingde, Yang Fan, Shi Wei, Liu Xiuwu, Ni Chen, He Fang, Huang Yunhui, Yang Xiaoyun, Pei Yi
Аннотация
BACKGROUND: Tuberculosis (TB) remains a global health threat, which is complicated by challenges in distinguishing TB from nontuberculous mycobacteria (NTM) infections and drug resistance. Conventional diagnostics (including culture, Xpert MTB/RIF, AFB smear, T-SPOT.TB, etc.) are limited by inadequate differentiation of TB and NTM, incomplete resistance profiling, and prolonged turnaround times.
METHODS: This retrospective multicenter study enrolled 130 patients with suspected mycobacterial infection between August 2023 and June 2024 at two tertiary hospitals in China. Patients were classified as TB (n=91), NTM (n=18), Other (n=17) and Mixed infection (n=4) based on a composite reference standard (final clinical diagnosis) established independently of targeted next-generation sequencing (tNGS) results. Performance of tNGS versus conventional methods was evaluated across different sample types. Diagnostic performance, drug-resistance detection, therapeutic impact and clinical outcomes were analyzed.
RESULTS: The overall agreement of tNGS detection with clinical diagnoses of pathogens was 80.9% in sputum and 71.3% in bronchoalveolar lavage fluid (BALF), exceeding conventional methods (which remained below 55%). Critically, tNGS effectively distinguished TB from NTM, with diagnostic sensitivity of 84.0% (95% CI, 75.0%-90.8%) for TB and 90.5% (95% CI, 69.6%-98.8%) for NTM, and specificity of 100.0% (95% CI, 90.3%-100.0%) for TB and 99.1% (95% CI, 95.0%-100.0%) for NTM. tNGS simultaneously detected resistance-associated variants relevant to 11 antimycobacterial drugs within approximately 24 hours. Treatment regimens for 52 patients (41.3%) were adjusted following tNGS reporting. Of these, 82.7% showed subsequent clinical improvement, however, these outcome data remain descriptive and do not establish causality.
CONCLUSIONS: tNGS enables rapid, accurate, and comprehensive diagnosis of mycobacterial infections, reliably differentiates TB from NTM, and provides drug-resistance profiles to inform therapy, with the potential to assist in optimizing clinical management of TB.
METHODS: This retrospective multicenter study enrolled 130 patients with suspected mycobacterial infection between August 2023 and June 2024 at two tertiary hospitals in China. Patients were classified as TB (n=91), NTM (n=18), Other (n=17) and Mixed infection (n=4) based on a composite reference standard (final clinical diagnosis) established independently of targeted next-generation sequencing (tNGS) results. Performance of tNGS versus conventional methods was evaluated across different sample types. Diagnostic performance, drug-resistance detection, therapeutic impact and clinical outcomes were analyzed.
RESULTS: The overall agreement of tNGS detection with clinical diagnoses of pathogens was 80.9% in sputum and 71.3% in bronchoalveolar lavage fluid (BALF), exceeding conventional methods (which remained below 55%). Critically, tNGS effectively distinguished TB from NTM, with diagnostic sensitivity of 84.0% (95% CI, 75.0%-90.8%) for TB and 90.5% (95% CI, 69.6%-98.8%) for NTM, and specificity of 100.0% (95% CI, 90.3%-100.0%) for TB and 99.1% (95% CI, 95.0%-100.0%) for NTM. tNGS simultaneously detected resistance-associated variants relevant to 11 antimycobacterial drugs within approximately 24 hours. Treatment regimens for 52 patients (41.3%) were adjusted following tNGS reporting. Of these, 82.7% showed subsequent clinical improvement, however, these outcome data remain descriptive and do not establish causality.
CONCLUSIONS: tNGS enables rapid, accurate, and comprehensive diagnosis of mycobacterial infections, reliably differentiates TB from NTM, and provides drug-resistance profiles to inform therapy, with the potential to assist in optimizing clinical management of TB.