Accuracy of next-generation sequencing for rapid diagnosis of tuberculous pleurisy: A protocol of systematic review and meta-analysis

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Abstract

Background Early, rapid and precise diagnosis of tuberculous pleurisy is difficult. Next-generation sequencing (NGS) has been used for the diagnosis of TB in some high-income countries. We completed the protocol of the study in order to better guide the systematic review and meta-analysis for evaluating the role of NGS in the diagnosis of tuberculous pleurisy, which was the purpose of the current study. Methods We designed and drafted this protocol as per the Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) guidelines. Three English databases (Cochrane Library, Embase and Medline via PubMed) and two Chinese databases (China National Knowledge Infrastructure and Wanfang Database) will be used to search for relevant studies. We followed the PICT principles to develop the eligibility criteria. Once the final compliant literature is identified, we will extract the valid information from the literature for statistical analysis. We will assess the quality of the included studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When there are more than 4 eligible studies, we will use Stata (version 15.0; Stata Corp., College Station, TX, the USA) with midas module to conduct the relevant meta-analysis and plot the relevant forest plots and curves. When there are less than 4 eligible studies, we will use Meta-DiSc (version 1.4) to perform the associated meta-analysis and plot the associated forest plots and curves. We will use the I2 statistic to assess heterogeneity between included studies. We will conduct meta-regression analysis, subgroup analysis and sensitivity analysis to explore possible sources of heterogeneity when significant heterogeneity exists.

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Ling, Y., Shen, Y., Xu, X., & Yu, G. (2025). Accuracy of next-generation sequencing for rapid diagnosis of tuberculous pleurisy: A protocol of systematic review and meta-analysis. PLoS ONE, 20(2 February). https://doi.org/10.1371/journal.pone.0319175

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