Diagnostic accuracy of the NOVA Tuberculosis Total Antibody Rapid test for detection of pulmonary tuberculosis and infection with Mycobacterium tuberculosis

oleh: Gideon Nsubuga, Samuel Kennedy, Yasha Rani, Zibran Hafiz, Soyeon Kim, Morten Ruhwald, David Alland, Jerrold Ellner, Moses Joloba, Susan E. Dorman, Adam Penn-Nicholson, Lydia Nakiyingi

Format: Article
Diterbitkan: Elsevier 2023-05-01

Deskripsi

Background: The NOVA Tuberculosis Total Antibody Rapid Test is a commercially available lateral flow serological assay that is intended to be used as an aid in the diagnosis of tuberculosis. We conducted a study to estimate diagnostic accuracy of this assay for diagnosis of active pulmonary tuberculosis disease and for detection of M. tuberculosis infection. Methods: This study used existing frozen plasma specimens that had been obtained previously from consenting HIV-negative adults in Cambodia, South Africa, and Vietnam whose tuberculosis status was rigorously characterized using sputum mycobacterial cultures and blood interferon gamma release assay. The investigational assay was performed in a single laboratory by laboratory staff specifically trained to conduct the assays according to the manufacturer’s procedures. In addition, intensity of the test band was subjectively assessed. Results: Plasma specimens from 150 participants were tested. All testing attempts yielded a determinate result of either positive or negative. For diagnosis of active pulmonary tuberculosis disease, test sensitivity and specificity were 40.0 % (20/50, 95 % confidence interval [CI] 27.6 % to 53.8 %) and 85.0 % (95 % CI 76.7 % to 90.7 %), respectively. For detection of M. tuberculosis infection, test sensitivity and specificity were 28.0 % (95 % CI 20.5 % to 37.2 %) and 86.0 % (95 % CI 73.8 % to 93.0 %), respectively. Among the 35 positive tests, no statistically significant band intensity trend was found across participant groups (p = 0.17). Conclusion: Study findings do not support a role for the NOVA Tuberculosis Test in current tuberculosis diagnostic algorithms.