Abstract
Rationale: The Xpert MTB/RIF is an automated molecular test for Mycobacterium tuberculosis that estimates bacterial burden by measuring the threshold-cycle (Ct) of its M. tuberculosis-specific real-time polymerase chain reaction. Bacterialburden is an important biomarker for disease severity, infection control risk, and response to therapy. Objectives: Evaluate bacterial load quantitation by Xpert MTB/RIF compared with conventional quantitative methods. Methods: Xpert MTB/RIF results we recompared with smear-microscopy, semiquantiative solid culture, and time-to-detection in liquid culture for 741 patients and 2,008 samples tested in a multisite clinical trial. An internal control real-time polymerase chain reaction was evaluated for its ability to identify inaccurate quantitative Xpert MTB/RIF results. Measurements and Main Results: Assays with an internal control Ct greater than 34 were likely to be inaccurately quantitated; this represented 15% of M. tuberculosis-positive tests. Excluding these, decreasing M. tuberculosis Ct was associated with increasing smear microscopy grade for smears of concentrated sputum pellets (r s = 20.77) and directly from sputum (r s = -0.71). A Ct cutoff of approximately 27.7 best predicted smear-positive status. The association between M. tuberculosis Ct and time-to-detection in liquid culture (r s = 0.68) andsemiquantitative colony counts (r s = -0.56)wasweaker than smear. Tests of paired same-patient sputum showed that highviscosity sputum samples contained x32 more M. tuberculosis than nonviscous samples. Comparisons between the grade of the acid-fast bacilli smear and Xpert MTB/RIF quantitative data across study sites enabled us to identify a site outlier inmicroscopy. Conclusions: Xpert MTB/RIF quantitation offers a new, standardized approach to measuring bacterial burden in the sputum of patients with tuberculosis.
| Original language | English |
|---|---|
| Pages (from-to) | 1076-1084 |
| Number of pages | 9 |
| Journal | American Journal of Respiratory and Critical Care Medicine |
| Volume | 184 |
| Issue number | 9 |
| DOIs | |
| State | Published - 1 Nov 2011 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Clinical trial
- Diagnosis
- Diagnostic techniques and procedures
- Molecular diagnostics
- Tuberculosis
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