Commercial MPT64-based tests for rapid identification of Mycobacterium tuberculosis complex: a meta-analysis.

J Infect

Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, No. 1838, North of Guangzhou Road, Baiyun District, Guangzhou 510515, PR China.

Published: November 2013

Objective: We did a systematic review and meta-analysis of published studies to evaluate the accuracy of commercial MPT64-based immunochromatographic tests for rapid identification of Mycobacterium tuberculosis complex.

Methods: We identified studies by searching Pubmed, BIOSIS Previews and Web of Science, and included studies using predetermined inclusion criteria. The data were pooled using the DerSimonian-Laird random effects model.

Results: A total of 28 studies were included in the final analysis. Pooled estimates were 97% (confidence interval [CI] 96-97%) for sensitivity and 98% (CI 98-99%) for specificity. The summary receiver operating characteristic curve showed an area of 0.9968 and a Q* of 0.98. Subgroup analysis showed that test accuracy did not depend on commercial kit, reference test and medium.

Conclusions: Commercial MPT64-based immunochromatographic tests are highly sensitive and specific for rapid identification of M. tuberculosis complex. They are good alternatives to biochemical test and molecular assays. Nevertheless, additional studies are required in setting with high prevalence of mpt64 mutations or high contamination of cultures.

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http://dx.doi.org/10.1016/j.jinf.2013.06.009DOI Listing

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Commercial MPT64-based tests for rapid identification of Mycobacterium tuberculosis complex: a meta-analysis.

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Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, No. 1838, North of Guangzhou Road, Baiyun District, Guangzhou 510515, PR China.

Objective: We did a systematic review and meta-analysis of published studies to evaluate the accuracy of commercial MPT64-based immunochromatographic tests for rapid identification of Mycobacterium tuberculosis complex.

Methods: We identified studies by searching Pubmed, BIOSIS Previews and Web of Science, and included studies using predetermined inclusion criteria. The data were pooled using the DerSimonian-Laird random effects model.

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