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Background <p>The Xpert MTB/RIF (Xpert) assay is a cornerstone for the rapid detection of rifampicin resistance. However, discordant results between Xpert and phenotypic drug susceptibility testing (pDST) present diagnostic challenges, particularly in regions with genetically diverse <i>Mycobacterium tuberculosis</i> populations. This study aimed to evaluate the performance of Xpert at the probe level to resolve such discrepancies and to inform an optimized diagnostic workflow.</p> Methods <p>Using a retrospective cohort of 3,360 clinical specimens from a high-burden setting in China, we compared Xpert results with pDST as the reference standard. We then performed a stratified analysis based on specific probe mutation patterns to determine their predictive value for phenotypic resistance.</p> Results <p>Overall, Xpert demonstrated substantial agreement with pDST (<i>κ</i> = 0.722, 95% CI: 0.685–0.759), exhibiting a sensitivity of 80.4% and a specificity of 95.8%. However, concordance was highly dependent on the specific probe mutation. Mutations in Probe E were strongly predictive of phenotypic resistance (Positive Predictive Value: PPV: 91.4%). In contrast, The mutations detected by Probe ΔCt and Probe A exhibited a low PPV of only 28.0% and 14.3%, respectively. Based on these findings, we propose a diagnostic triage strategy: results indicating Probe E mutations can reliably guide immediate treatment adjustments, whereas isolated ProbeΔCt resistance, or Probe A mutations, warrant confirmatory pDST before initiating second-line regimens.</p> Conclusion <p>The clinical interpretation of Xpert results requires a nuanced understanding of probe-specific mutation patterns. Our probe-stratified analysis and the resulting triage algorithm provide a practical framework for optimizing the management of rifampicin-resistant tuberculosis (RR-TB). This approach effectively balances the need for diagnostic speed with therapeutic accuracy in programmatic settings.</p>

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Probe-level performance of Xpert MTB/RIF versus phenotypic culture for rifampicin resistance detection and a triage algorithm based on 3,360 clinical specimens in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Yuliang Huang,
  • Shunda Luo,
  • Jinfeng Lu,
  • Chaoyong He,
  • Diyu Mai,
  • Qiuying Ma,
  • Wanhong Huang,
  • Hanzhen Su,
  • Zhengkang Li,
  • Liang Zhang,
  • Xiaocheng Luo

摘要

Background

The Xpert MTB/RIF (Xpert) assay is a cornerstone for the rapid detection of rifampicin resistance. However, discordant results between Xpert and phenotypic drug susceptibility testing (pDST) present diagnostic challenges, particularly in regions with genetically diverse Mycobacterium tuberculosis populations. This study aimed to evaluate the performance of Xpert at the probe level to resolve such discrepancies and to inform an optimized diagnostic workflow.

Methods

Using a retrospective cohort of 3,360 clinical specimens from a high-burden setting in China, we compared Xpert results with pDST as the reference standard. We then performed a stratified analysis based on specific probe mutation patterns to determine their predictive value for phenotypic resistance.

Results

Overall, Xpert demonstrated substantial agreement with pDST (κ = 0.722, 95% CI: 0.685–0.759), exhibiting a sensitivity of 80.4% and a specificity of 95.8%. However, concordance was highly dependent on the specific probe mutation. Mutations in Probe E were strongly predictive of phenotypic resistance (Positive Predictive Value: PPV: 91.4%). In contrast, The mutations detected by Probe ΔCt and Probe A exhibited a low PPV of only 28.0% and 14.3%, respectively. Based on these findings, we propose a diagnostic triage strategy: results indicating Probe E mutations can reliably guide immediate treatment adjustments, whereas isolated ProbeΔCt resistance, or Probe A mutations, warrant confirmatory pDST before initiating second-line regimens.

Conclusion

The clinical interpretation of Xpert results requires a nuanced understanding of probe-specific mutation patterns. Our probe-stratified analysis and the resulting triage algorithm provide a practical framework for optimizing the management of rifampicin-resistant tuberculosis (RR-TB). This approach effectively balances the need for diagnostic speed with therapeutic accuracy in programmatic settings.