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Meta-Analysis
. 2021 Apr 10;21(1):336.
doi: 10.1186/s12879-021-06022-w.

MPT64 assays for the rapid detection of Mycobacterium tuberculosis

Xun-Jie Cao #  1   2 , Ya-Ping Li #  1   2   3 , Jia-Ying Wang  1   2 , Jie Zhou  1   2 , Xu-Guang Guo  4   5   6   7
Affiliations
Meta-Analysis

MPT64 assays for the rapid detection of Mycobacterium tuberculosis

Xun-Jie Cao et al. BMC Infect Dis. .

Abstract

Background: Tuberculosis (TB) is a serious infectious disease caused by Mycobacterium tuberculosis (MTB). An estimated 1.7 billion people worldwide are infected with Mycobacterium tuberculosis (LTBI) during the incubation period without any obvious symptoms. Because of MTB's high infection and mortality rates, there is an urgent need to develop a fast, portable, and sensitive diagnostic technology for its detection.

Methods: We included research from PubMed, Cochrane Library, Web of Science, and Embase and extracted the data. MetaDisc and STATA were used to build forest plots, Deek's funnel plot, Fagan plot, and bivariate boxplot for analysis.

Results: Forty-six articles were analyzed, the results of which are as follows: sensitivity and specificity were 0.92 (0.91-0.93) and 0.95 (0.94-0.95) respectively. The NLR and PLR were 0.04 (95% CI 0.03-0.07) and 25.32 (95% CI 12.38-51.78) respectively. DOR was 639.60 (243.04-1683.18). The area under the SROC curve (AUC) was 0.99.

Conclusions: MPT64 exhibits good diagnostic efficiency for MTB. There is no obvious heterogeneity between the three commercial kits.

Keywords: Commercial kits; MPT64; MTB; Mycobacterium tuberculosis; Tuberculosis.

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Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Fig. 1
Fig. 1
Flow diagram of study identification and inclusion
Fig. 2
Fig. 2
Quality assessment of the included studies. a. Overall quality assessment of the included studies, b. Quality assessment of the individual studies
Fig. 3
Fig. 3
Forest plots of sensitivity and specificity. a. sensitivity, b. specificity
Fig. 4
Fig. 4
Forest plots of positive LR and negative LR. a. positive LR, b. negative LR
Fig. 5
Fig. 5
Overall diagnostic efficacy of MPT64 assays for Mycobacterium tuberculosis. a. diagnostic OR for the diagnosis of Mycobacterium tuberculosis infection, b. SROC curve
Fig. 6
Fig. 6
Fagan plot of disease probabilities based on Bayes’ theorem
Fig. 7
Fig. 7
The results of subgroup analysis between “three commercial kits group” and other detection methods. a. the result of “three commercial kits group”, b. the result of other detection methods group
Fig. 8
Fig. 8
The results of subgroup analysis for the three commercial kits. a. the result of BD MGIT TBcID kit, b. the result of Capilia TB kit, c. the result of SD Bioline kit
Fig. 9
Fig. 9
Publication bias for MPT64 detection for MTB. a. Deeks’ funnel plot asymmetry test to assess the publication bias for MPT64 detection for MTB; b. Bivariate boxplot
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