Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis

The aim of this study was to evaluate the diagnostic accuracy of the IGRA, TST, and TBST by combining diagnostic test accuracy (DTA) analysis and network meta-analysis (NMA) to increase the reliability and accuracy of diagnostic methods and promote the eradication of TB. An electronic search of the...

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Main Authors: Li Peng, Weijie Ma, Lei Zhong, Jiaru Yang, Hanxin Wu, Liangyu Zhu, Xun Huang, Rui Yang, Bingxue Li, Weijiang Ma, Xinya Wu, Jieqin Song, Suyi Luo, Fukai Bao, Aihua Liu
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Pathogens
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Online Access:https://www.mdpi.com/2076-0817/13/12/1050
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author Li Peng
Weijie Ma
Lei Zhong
Jiaru Yang
Hanxin Wu
Liangyu Zhu
Xun Huang
Rui Yang
Bingxue Li
Weijiang Ma
Xinya Wu
Jieqin Song
Suyi Luo
Fukai Bao
Aihua Liu
author_facet Li Peng
Weijie Ma
Lei Zhong
Jiaru Yang
Hanxin Wu
Liangyu Zhu
Xun Huang
Rui Yang
Bingxue Li
Weijiang Ma
Xinya Wu
Jieqin Song
Suyi Luo
Fukai Bao
Aihua Liu
author_sort Li Peng
collection DOAJ
description The aim of this study was to evaluate the diagnostic accuracy of the IGRA, TST, and TBST by combining diagnostic test accuracy (DTA) analysis and network meta-analysis (NMA) to increase the reliability and accuracy of diagnostic methods and promote the eradication of TB. An electronic search of the PubMed, Embase, and Cochrane databases was conducted, from the date of establishment to September 30, 2024. Data were synthesized with frequentist random-effects network meta-analyses, a single-group rate meta-analysis algorithm, and a bivariate mixed-effects logistic regression model. Summarized receiver operating characteristic curves and Fagan nomograms were used to assess diagnostic accuracy and clinical utility. Deeks’ funnel plots and the Quality Assessment of Diagnostic Accuracy Studies 2 tools were used to assess publication bias and risk of bias. Sources of heterogeneity were investigated using subgroup analyses. Forty-nine studies were identified. The diagnostic performance of the three diagnostic methods for TB infection is summarized as follows: the pooled sensitivity was 77.9% (95% confidence interval [CI], 0.69–0.856), and the pooled specificity was 80.3% (95% CI, 0.75–0.86). The sensitivity and specificity of the IGRA were 82.1% (95% CI, 0.78–0.86) and 81.1% (95% CI, 0.75–0.86), respectively, both higher than the TST. However, the TBST exhibited the highest specificity, at 98.5% (95% CI, 0.96–1.00), with a sensitivity of 78.7% (95% CI, 0.68–0.88), which was between that of the IGRA and TST. Subgroup analysis found that population categories and reference standards, among other factors, may be attributed to heterogeneity. In addition, the TST and IGRA add-on TBST can significantly improve diagnostic accuracy. In our study, the IGRA showed higher sensitivity, whereas the TBST showed higher specificity. Interestingly, under certain conditions, TST add-on TBST and IGRA add-on TBST showed better accuracy than TST and IGRA alone and could provide more effective guidance for clinical practice (PROSPERO CRD42023420136).
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spelling doaj-art-656f8cf715f8494e88fdd6730a0429a72025-08-20T02:57:17ZengMDPI AGPathogens2076-08172024-11-011312105010.3390/pathogens13121050Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-AnalysisLi Peng0Weijie Ma1Lei Zhong2Jiaru Yang3Hanxin Wu4Liangyu Zhu5Xun Huang6Rui Yang7Bingxue Li8Weijiang Ma9Xinya Wu10Jieqin Song11Suyi Luo12Fukai Bao13Aihua Liu14Yunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaYunnan Province Key Laboratory of Children’s Major Diseases Research, School of Basic Medical Sciences, Kunming Medical University, Kunming 650500, ChinaThe aim of this study was to evaluate the diagnostic accuracy of the IGRA, TST, and TBST by combining diagnostic test accuracy (DTA) analysis and network meta-analysis (NMA) to increase the reliability and accuracy of diagnostic methods and promote the eradication of TB. An electronic search of the PubMed, Embase, and Cochrane databases was conducted, from the date of establishment to September 30, 2024. Data were synthesized with frequentist random-effects network meta-analyses, a single-group rate meta-analysis algorithm, and a bivariate mixed-effects logistic regression model. Summarized receiver operating characteristic curves and Fagan nomograms were used to assess diagnostic accuracy and clinical utility. Deeks’ funnel plots and the Quality Assessment of Diagnostic Accuracy Studies 2 tools were used to assess publication bias and risk of bias. Sources of heterogeneity were investigated using subgroup analyses. Forty-nine studies were identified. The diagnostic performance of the three diagnostic methods for TB infection is summarized as follows: the pooled sensitivity was 77.9% (95% confidence interval [CI], 0.69–0.856), and the pooled specificity was 80.3% (95% CI, 0.75–0.86). The sensitivity and specificity of the IGRA were 82.1% (95% CI, 0.78–0.86) and 81.1% (95% CI, 0.75–0.86), respectively, both higher than the TST. However, the TBST exhibited the highest specificity, at 98.5% (95% CI, 0.96–1.00), with a sensitivity of 78.7% (95% CI, 0.68–0.88), which was between that of the IGRA and TST. Subgroup analysis found that population categories and reference standards, among other factors, may be attributed to heterogeneity. In addition, the TST and IGRA add-on TBST can significantly improve diagnostic accuracy. In our study, the IGRA showed higher sensitivity, whereas the TBST showed higher specificity. Interestingly, under certain conditions, TST add-on TBST and IGRA add-on TBST showed better accuracy than TST and IGRA alone and could provide more effective guidance for clinical practice (PROSPERO CRD42023420136).https://www.mdpi.com/2076-0817/13/12/1050tuberculosisdiagnosisTSTIGRAthe <i>Mycobacterium tuberculosis</i> antigen-based skin testmeta-analysis
spellingShingle Li Peng
Weijie Ma
Lei Zhong
Jiaru Yang
Hanxin Wu
Liangyu Zhu
Xun Huang
Rui Yang
Bingxue Li
Weijiang Ma
Xinya Wu
Jieqin Song
Suyi Luo
Fukai Bao
Aihua Liu
Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
Pathogens
tuberculosis
diagnosis
TST
IGRA
the <i>Mycobacterium tuberculosis</i> antigen-based skin test
meta-analysis
title Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
title_full Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
title_fullStr Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
title_full_unstemmed Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
title_short Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis
title_sort diagnostic accuracy of i mycobacterium tuberculosis i antigen based skin tests tbsts for tuberculosis infection compared with tst and igra a network meta analysis
topic tuberculosis
diagnosis
TST
IGRA
the <i>Mycobacterium tuberculosis</i> antigen-based skin test
meta-analysis
url https://www.mdpi.com/2076-0817/13/12/1050
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