Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis

• Published in: Frontiers in public health Vol. 11; p. 1224993
• Main Authors: Li, Yulian, Bian, Wentao, Wu, Shiping, Zhang, Jie, Li, Dan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 11.08.2023
• Subjects: clinical specimen; Coinfection; High-Throughput Nucleotide Sequencing; Humans; infectious disease; metagenomic next-generation sequencing; mixed infections; Mycobacterium tuberculosis - genetics; Public Health; Research Design; Technology; tuberculosis; mixed infections; metagenomic next-generation sequencing; clinical specimen; infectious disease; tuberculosis
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2023.1224993

Metagenomic next-generation sequencing (mNGS) has been gradually applied to the diagnosis of tuberculosis (TB) due to its rapid and highly sensitive characteristics. Despite numerous studies on this subject, their results vary significantly. Thus, the current meta-analysis was performed to assess the performance of the mNGS on tuberculosis. PubMed, Embase, Web of Science, and The Cochrane Library were searched up to June 21, 2023. Studies utilizing the mNGS for tuberculosis detection were included. The risk of bias was assessed by QUADAS-2, and a meta-analysis was performed with STATA14.0 software. Seventeen studies comprising 3,205 specimens were included. The combined sensitivity and specificity of mNGS for clinical specimens were 0.69[0.58-0.79] and 1.00[0.99-1.00], respectively. Subgroup analysis identified sequencing platform, diagnostic criteria, study type, sample size, and sample types as potential sources of heterogeneity. Cerebrospinal Fluid (CSF) has a lower sensitivity of 0.58 (0.39-0.75). In a population with a 10% prevalence rate, the accuracy of sensitivity reached 94%. Metagenomic next-generation sequencing technology exhibits high sensitivity and speed in diagnosing . Its application in mono and mixed infections peoples shows promise, and mNGS is likely to be increasingly used to address challenges posed by complexes in the future.