Comparison of Analytical Sensitivity (Limit of Detection) of Xpert MTB/RIF and Xpert MTB/RIF Ultra for Non-Sputum Specimens.
Marisa C NielsenPaula ClarnerRuchi ParohaSunhee LeePhyu M ThwePing RenPublished in: Pathogens (Basel, Switzerland) (2023)
Tuberculosis (TB) is a significant public health threat and has remained a leading cause of death in many parts of the world. Rapid and accurate testing and timely diagnosis can improve treatment efficacy and reduce new exposures. The Cepheid Xpert® MTB/RIF tests have two marketed products (US-IVD and Ultra) that are widely accepted for diagnosis of TB but have not yet been approved for non-sputum specimens. Despite numerous studies in the literature, no data for the analytical sensitivity of these two products on the non-sputum samples are available to date. This is the first study that systematically determined the analytical sensitivities of both US-IVD and Ultra tests on cerebrospinal fluid (CSF), tissue, and bronchoalveolar lavage (BAL). The limits of detection (LoDs) on the US-IVD test for both Mycobacterium tuberculosis and rifampin resistance in CFU/mL, respectively, were as follows: CSF (3.3 and 4.6), tissue (15 and 23), and bronchoalveolar lavage (BAL) (45 and 60), and on the Ultra test: CSF (0.16 and 2.7), tissue (0.11 and 12), and BAL (0.65, and 7.5). Overall, the analytical sensitivities of the Ultra test were substantially better than US-IVD for all sample types tested. This study provided a foundation for using either the US-IVD or Ultra test for the early detection of both pulmonary and extrapulmonary (EP) TB. Furthermore, using Ultra could result in higher TB case detection rates in subjects with paucibacillary TB and EP TB, positively impacting WHO goals to eradicate TB.
Keyphrases
- mycobacterium tuberculosis
- pulmonary tuberculosis
- high resolution
- public health
- cerebrospinal fluid
- loop mediated isothermal amplification
- systematic review
- pulmonary hypertension
- human immunodeficiency virus
- mass spectrometry
- hepatitis c virus
- machine learning
- big data
- electronic health record
- smoking cessation
- combination therapy