Performance of the Xpert MTB/RIF assay for the diagnosis of pulmonary tuberculosis and rifampin resistance in a low-incidence, high-resource setting

Abstract

<div><p>Performance of the Xpert MTB/RIF assay, designed to simultaneously detect <i>Mycobacterium tuberculosis</i> complex (MTBC) and rifampin (RIF) resistance, has been well documented in low-resource settings with high TB-incidence. However, few studies have assessed its accuracy in low TB incidence settings. We evaluated the performance of Xpert MTB/RIF using clinical sputum specimens routinely collected from suspect pulmonary TB patients over a 4-year time period in San Diego County, California. Xpert MTB/RIF results were compared to acid-fast bacilli (AFB) smear microscopy, mycobacterial culture, and phenotypic drug susceptibility testing (DST). Of 751 sputum specimens, 134 (17.8%) were MTBC culture-positive and 2 (1.5%) were multidrug-resistant (MDR). For the detection of MTBC, Xpert MTB/RIF sensitivity was 89.6% (97.7% and 74.5% in smear-positive and -negative sputa, respectively) and specificity was 97.2%; while AFB smear sensitivity and specificity were 64.9% and 77.8%, respectively. Xpert MTB/RIF detected 35 of 47 smear-negative culture-positive specimens, and excluded 124 of 137 smear-positive culture-negative specimens. Xpert MTB/RIF also correctly excluded 99.2% (121/122) of nontuberculous mycobacteria (NTM) specimens, including all 33 NTM false-positives by smear microscopy. For the detection of RIF resistance, Xpert MTB/RIF sensitivity and specificity were 100% and 98.3%, respectively. Our findings demonstrate that Xpert MTB/RIF is able to accurately detect MTBC and RIF resistance in routinely collected respiratory specimens in a low TB-incidence setting, with comparable performance to that achieved in high-incidence settings; and suggest that under these conditions the assay has particular utility in detecting smear-negative TB cases, excluding smear-positive patients without MTBC disease, and differentiating MTBC from NTM.</p></div

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Last time updated on 12/02/2018

This paper was published in FigShare.

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