Literature DB >> 9934542

LCx: a diagnostic alternative for the early detection of Mycobacterium tuberculosis complex.

M J Ruiz-Serrano1, J Albadalejo, L Martínez-Sánchez, E Bouza.   

Abstract

This study aims to evaluate the performance of a new diagnostic method (LCx Tuberculosis Assay, Abbott Laboratories) based on Ligase Chain Reaction (LCR) technology, for the detection of Mycobacterium tuberculosis in respiratory and non-respiratory specimens and compare it with standard microbiological data and the clinical diagnosis of tuberculosis. Nine hundred specimens were collected from patients with a high suspicion of tuberculosis (740 respiratory samples and 160 non-respiratory specimens). The study was divided into two separate groups: samples washed and distilled water (207 samples) and unwashed samples that were directly resuspended in phosphate buffer (693 samples). The overall sensitivity, specificity, positive and negative predictive values of samples washed with distilled water after decontamination with SDS-NaOH were: 54%, 100%, 100%, and 94%, respectively. If these results were divided according to origin of specimens, the sensitivity, specificity, positive and negative predictive values in respiratory and non-respiratory samples were 54.5%, 100%, 100%, 94% and 50 100%, 100%, 93%, respectively. In contrast, for the non-washed samples, values were 85%, 95%, 80% and 98%, respectively. Respiratory and non-respiratory samples gave values of 84%, 96%, 77%, and 97.5% versus 89%, 99%, 94%, and 98%. The LCx M. tuberculosis assay is a novel, semi-automated assay and a rapid and highly specific technique for screening all forms of tuberculosis, including non-respiratory forms.

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Year:  1998        PMID: 9934542     DOI: 10.1016/s0732-8893(98)00079-0

Source DB:  PubMed          Journal:  Diagn Microbiol Infect Dis        ISSN: 0732-8893            Impact factor:   2.803


  1 in total

1.  Commercial nucleic-acid amplification tests for diagnosis of pulmonary tuberculosis in respiratory specimens: meta-analysis and meta-regression.

Authors:  Daphne I Ling; Laura L Flores; Lee W Riley; Madhukar Pai
Journal:  PLoS One       Date:  2008-02-06       Impact factor: 3.240

  1 in total

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