Evidence map›Paper›PMID 41612232›Full record

ArticleBMC infectious diseases2026

Comparison of the diagnostic value of targeted next-generation sequencing, metagenomic next-generation sequencing, and Xpert MTB/RIF in adult pulmonary tuberculosis.

Qiaoqian Chen, Qionghui Yin, Jin Chen, Lijun Jin, Wenhu Guo, Mingxiang Huang

Abstract readComparative Study
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qiaoqian Chen *Department of Clinical Laboratory, Fujian Medical University Clinical Teaching Hospital, Fuzhou Pulmonary Hospital of Fujian Province, Fuzhou, People's Republic of China.
Qionghui Yin *Department of Medical, Fuzhou Ji'Ang Medical Laboratory, Fuzhou, People's Republic of China.
Jin ChenDepartment of Medical, Fuzhou Ji'Ang Medical Laboratory, Fuzhou, People's Republic of China.
Lijun JinDepartment of Bioinformatics, Fuzhou Ji'Ang Medical Laboratory, Fuzhou, People's Republic of China.
Wenhu GuoSchool of Medical Technology and Engineering, Fujian Medical University, Fuzhou, People's Republic of China. wguo@fjmu.edu.cn.
Mingxiang HuangDepartment of Clinical Laboratory, Fujian Medical University Clinical Teaching Hospital, Fuzhou Pulmonary Hospital of Fujian Province, Fuzhou, People's Republic of China. hmg119@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) has high morbidity and mortality rates, and drug-resistant strains pose an increasing challenge. Traditional methods for detecting the Mycobacterium tuberculosis complex (MTBC) are insufficient for rapid clinical diagnosis. This prospective study compared the diagnostic efficacy of targeted next-generation sequencing (tNGS), metagenomic next-generation sequencing (mNGS), and Xpert MTB/RIF using bronchoalveolar lavage fluid (BALF) samples from 121 patients with suspected pulmonary TB. Against the reference standard of mycobacterial culture, tNGS demonstrated the highest sensitivity (97.44%), followed by Xpert MTB/RIF (92.31%) and mNGS (84.62%), specificities were 69.51%, 69.51%, and 75.61%, respectively. To address the limitations of culture as an imperfect reference standard and the potential bias from clinical diagnosis, Bayesian Latent Class Analysis (BLCA) was employed. BLCA, which does not assume a perfect gold standard, estimated sensitivities of 98.7%, 99.8%, and 91.0% for tNGS, Xpert MTB/RIF, and mNGS, with corresponding specificities of 89.3%, 93.3%, and 97.8%, respectively. Both tNGS and Xpert MTB/RIF consistently detected rifampicin resistance mutations (rpoB) (p = 0.219, Kappa = 0.730). In conclusion, tNGS offers comparable specificity and sensitivity to Xpert MTB/RIF for TB diagnosis, with the advantage of distinguishing between MTBC, non-tuberculous Mycobacteria (NTM), and other microorganisms. Simultaneously, it provides insights into anti-TB drug resistance. Thus, tNGS is a valuable tool for diagnosing TB in various clinical settings. Clinical trial number, Not applicable.

Indexed as

High-Throughput Nucleotide SequencingMetagenomicsMolecular Diagnostic TechniquesMycobacterium tuberculosisTuberculosis, PulmonaryAdultAgedBronchoalveolar Lavage FluidFemaleHumansMaleMiddle AgedProspective StudiesSensitivity and SpecificityMetagenomic next-generation sequencing (mNGS)Pulmonary tuberculosisTargeted next-generation sequencing (tNGS)Xpert MTB/RIF

Identifiers

PMID41612232
PMCPMC13122886

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.