Evaluation of the Xpert MTB/XDR test for detection of isoniazid, fluoroquinolones, and second-line injectable drugs resistance to Mycobacterium tuberculosis—Anhui Province, China
Abstract
Introduction The emergence of drug-resistant tuberculosis (DR-TB) has posed significant challenges to TB control. This study assessed the diagnostic performance of the Xpert MTB/XDR test for detecting drug resistance in TB patients. Methods This study analyzed 276 samples collected from clinically suspected MDR-TB patients in Auhui Chest Hospital from 01/03/2022–01/03/2023. The Xpert MTB/XDR test was evaluated for its ability to detect resistance to isoniazid (INH), ethionamide (ETH), fluoroquinolones (FLQ), and second-line injectable drugs (SLIDs) compared with phenotypic drug susceptibility testing (pDST). Specimens were investigated by Sanger sequencing, where the MTB/XDR test and pDST results were inconsistent. Afterward, the clinical performance of the Xpert MTB/XDR test was also evaluated with the composite reference test (pDST + sequencing). Results The sensitivity of the Xpert MTB/XDR test against pDST in detecting resistance to INH and FLQ using 276 samples was 95.77% (95% CI: 91.83–98.16) and 93.83% (95% CI: 86.18–97.97), respectively. In contrast, a lower sensitivity of the MTB XDR test in predicting SLIDs and ETH resistance (sensitivity < 75%) compared with pDST was demonstrated in this study. The specificity for detecting all drugs was greater than 90%. Thirty-three samples were retested by sequencing, which identified mutations predicting INH and FLQ resistance, determining whether resistant or not by combining pDST and sequencing results. When considering pDST + sequencing, the sensitivity and specificity of the MTB/XDR assay for INH and FLQ drug targets increased, especially the detection specificity of FLQ has reached 100% (95% CI: 97.95–100). Conclusion The Xpert MTB/XDR has high sensitivity and specificity in drug-resistant tuberculosis patients, making it better suited to meet the needs of rapid, sensitive, and accurate detection for drug-resistant tuberculosis in resource-limited settings, and serving as a critical tool for achieving personalized treatment and TB control.
Article Details
Authors (12)
Ruiqing Zhang
Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology
Xundi Bao
Fangjin Bao
Chong Teng
Dongfang Xu
Zhou Liu
Institute of Condensed Matter and Material Physics, School of Physics
Yue Li
Bing Zhao
Hui Xia
School of Materials Science and Engineering
Ruida Xing
Xichao Ou
Yanlin Zhao