Publication date: Jul 07, 2025
Targeted next-generation sequencing (tNGS) is an adjunct tool endorsed by WHO for tuberculosis diagnosis. The study aimed to investigate the diagnostic performance of tNGS across different clinical scenarios (populations, sample and tuberculosis types, and platforms), which has not been comprehensively evaluated. A literature search was conducted in PubMed, MEDLINE, Web of Science, Wanfang Data, VIP, and CNKI to identify studies published in English and Chinese from January 1, 2005, to October 14, 2024, that evaluated tNGS for tuberculosis diagnosis using reference strains and original samples or isolates from patients with presumptive or confirmed tuberculosis. Included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, and diagnostic accuracy was calculated against microbiological reference standard and composite reference standard. The primary outcomes were the pooled sensitivity and specificity. This study is registered with PROSPERO (CRD42024560190). Out of 3,383 records screened, 16 studies comprising 2,565 participants were included in the meta-analysis. Overall, tNGS demonstrated a pooled sensitivity of 0. 86 (95 % CI, 0. 77-0. 91) and specificity of 0. 95 (0. 86-0. 99). The positive and negative likelihood ratios were 18. 24 (5. 61-59. 26) and 0. 15 (0. 09-0. 26). The diagnostic odds ratio and the area under the summary receiver operating characteristic curve was 120. 88 (26. 93-542. 57) and 0. 95 (0. 93-0. 97), respectively. The diagnostic performance of tNGS varied substantially depending on sample type and sequencing platform, where the highest sensitivity was observed with bronchoalveolar lavage fluid 0. 91 (0. 75-0. 98) and the target nanopore sequencing platform 0. 85 (0. 80-0. 89). Furthermore, tNGS demonstrated a significantly higher detection rate compared to GeneXpert MTB/RIF (odds ratio: 1. 78 (1. 39-2. 29); p = 0. 01). tNGS demonstrates high diagnostic accuracy for tuberculosis, with its performance influenced by sample type and sequencing platform. These findings support the optimization of tNGS application in specific clinical settings to enhance tuberculosis management.
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | tuberculosis |
| pathway | KEGG | Tuberculosis |
| disease | IDO | quality |