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The Xpert tuberculosis assay resulted in only marginally improved sensitivity and time to diagnosis over standard smear- and culture-based diagnostic algorithms in a high-resource, ambulatory, tertiary-care setting, where clinician expertise is high and patients present early in their disease.Background. Xpert MTB/RIF, the first automated molecular test for tuberculosis, is transforming the diagnostic landscape in low-income countries. However, little information is available on its performance in low-incidence, high-resource countries.Methods. We evaluated the accuracy of Xpert in a university hospital tuberculosis clinic in Montreal, Canada, for the detection of pulmonary tuberculosis on induced sputum samples, using mycobacterial cultures as the reference standard. We also assessed the potential reduction in time to diagnosis and treatment initiation.Results. We enrolled 502 consecutive patients who presented for evaluation of possible active tuberculosis (most with abnormal chest radiographs, only 18% symptomatic). Twenty-five subjects were identified to have active tuberculosis by culture. Xpert had a sensitivity of 46% (95% confidence interval [CI], 26%–67%) and specificity of 100% (95% CI, 99%–100%) for detection of Mycobacterium tuberculosis. Sensitivity was 86% (95% CI, 42%–100%) in the 7 subjects with smear-positive results, and 28% (95% CI, 10%–56%) in the remaining subjects with smear-negative, culture-positive results; in this latter group, positive Xpert results were obtained a median 12 days before culture results. Subjects with positive cultures but negative Xpert results had minimal disease: 11 of 13 had no symptoms on presentation, and mean time to positive liquid culture results was 28 days (95% CI, 25–47 days) compared with 14 days (95% CI, 8–21 days) in Xpert/culture-positive cases.Conclusions. Our findings suggest limited potential impact of Xpert testing in high-resource, low-incidence ambulatory settings due to lower sensitivity in the context of less extensive disease, and limited potential to expedite diagnosis beyond what is achieved with the existing, well-performing diagnostic algorithm. [ABSTRACT FROM AUTHOR]