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Evaluation of Xpert MTB/XDR and Deeplex Myc‐TB for the Rapid Detection of Drug Resistance in Mycobacterium tuberculosis

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Objectives Rapid identification of drug‐resistant tuberculosis remains critical for the initiation of appropriate treatment regimens, improved success rates and reduction in the development of drug resistance. In this study, we compared the diagnostic performance of two novel diagnostic tests, Xpert MTB/XDR and Deeplex Myc‐TB (both CE marked). Methods Twenty clinical isolates of Mycobacterium tuberculosis (MTB) with known drug resistance patterns were used to determine the concordance/discordance of the two rapid platforms when performing drug susceptibility testing. The limit of detection for MTB, the detection of coinfection/cross‐reactivity with Mycobacterium abscessus (Mab) and the detection of heteroresistance using mixtures of wild type MTB and drug‐resistant MTB were also evaluated. Results Xpert MTB/XDR had a total concordance of 68% and Deeplex Myc‐TB had a total concordance of 100% when compared to WGS and phenotypic drug susceptibility testing. Xpert TB/XDR had a lower limit of detection for identifying MTB at 10^3 CFU/mL, whereas Deeplex Myc‐TB required at least 10^4 CFU/mL. Both Xpert MTB/XDR and Deeplex Myc‐TB were able to detect the wild type MTB without any cross‐reactivity with the drug‐resistant Mab. For heteroresistance detection, Deeplex Myc‐TB was able to detect down to 1% admix of the resistant isolates (1 out of 4), whilst Xpert MTB/XDR was only able to detect down to 10% in 1 out of 4 mixtures of isolates tested. Conclusion Both platforms represent an attractive option for the rapid detection of drug resistance and heteroresistance contributing to the management of complex cases, but further studies are needed to assess their real clinical impact.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.

Titre Crossref
Evaluation of Xpert MTB/XDR and Deeplex Myc‐TB for the Rapid Detection of Drug Resistance in <i>Mycobacterium tuberculosis</i>
Date Crossref
01/01/2026
Éditeur
Wiley
Type
journal-article

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Institutions déclarées

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Sujets associés

Tuberculosis Research and EpidemiologyMycobacterium research and diagnosisImage Processing Techniques and Applications

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