Bertin C Bisimwa1,2, Jean B Nachega3,4,5, Robin M Warren6, Grant Theron6, John Z Metcalfe7, Maunank Shah8, Andreas H Diacon9, Nadia A Sam-Agudu10,11, Marcel Yotebieng12, André N H Bulabula13,14, Patrick D M C Katoto15,16, Jean-Paul Chirambiza17, Rosette Nyota17, Freddy M Birembano17, Eric M Musafiri17, Sifa Byadunia2, Esto Bahizire18,19,20, Michel K Kaswa21, Steven Callens22, Zacharie M Kashongwe1,2,23. 1. Laboratoire de Recherche Biomédicale Professeur André Lurhuma, Université Catholique de Bukavu, Bukavu, Democratic Republic of Congo. 2. Institut Supérieur des Techniques Médicales, Bukavu, Democratic Republic of Congo. 3. Departments of Epidemiology, Infectious Diseases, and Microbiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA. 4. Department of Medicine and Center for Infectious Diseases, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. 5. Departments of Epidemiology and International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. 6. Division of Science and Technology (DST) Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa. 7. Division of Pulmonary and Critical Care Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California, San Francisco, San Francisco, California, USA. 8. Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. 9. Task Foundation and Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. 10. International Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, Nigeria. 11. Division of Epidemiology and Prevention, Institute of Human Virology, University of Maryland School of Medicine, Baltimore, Maryland, USA. 12. Department of Medicine, Albert Einstein College of Medicine, New York, New York, USA. 13. Department of Pediatrics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. 14. Infection Control Africa Network, Cape Town, South Africa. 15. Centre for Environment and Health, Department of Public Health and Primary Care, Laboratory of Pneumology, Katholieke Universiteit Leuven, Leuven, Belgium. 16. Department of Internal Medicine, Faculty of Medicine, Université Catholique de Bukavu, Bukavu, Democratic Republic of Congo. 17. National TB Program, Provincial Anti-Leprosy and TB Coordination, Bukavu, Democratic Republic of Congo. 18. Center for Tropical Diseases and Global Health, Catholic University of Bukavu, Bukavu, Democratic Republic of the Congo. 19. Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya. 20. Centre of Research in Epidemiology, Biostatistics, and Clinical Research, Université Libre de Bruxelles, Brussels, Belgium. 21. National Tuberculosis Program, Ministry of Health, Kinshasa, Democratic Republic of Congo. 22. Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium. 23. Cliniques Universitaire de Kinshasa, Université Nationale de Kinshasa, Kinshasa, Democratic Republic of Congo.
Abstract
BACKGROUND: Rifampicin (RIF) resistance is highly correlated with isoniazid (INH) resistance and used as proxy for multidrug-resistant tuberculosis (MDR-TB). Using MTBDRplus as a comparator, we evaluated the predictive value of Xpert MTB/RIF (Xpert)-detected RIF resistance for MDR-TB in eastern Democratic Republic of the Congo (DRC). METHODS: We conducted a cross-sectional study involving data from new or retreatment pulmonary adult TB cases evaluated between July 2013 and December 2016. Separate, paired sputa for smear microscopy and MTBDRplus were collected. Xpert testing was performed subject to the availability of Xpert cartridges on sample remnants after microscopy. RESULTS: Among 353 patients, 193 (54.7%) were previously treated and 224 (63.5%) were MTBDRplus TB positive. Of the 224, 43 (19.2%) were RIF monoresistant, 11 (4.9%) were INH monoresistant, 53 (23.7%) had MDR-TB, and 117 (52.2%) were RIF and INH susceptible. Overall, among the 96 samples detected by MTBDRplus as RIF resistant, 53 (55.2%) had MDR-TB. Xpert testing was performed in 179 (50.7%) specimens; among these, 163 (91.1%) were TB positive and 73 (44.8%) RIF resistant. Only 45/73 (61.6%) Xpert-identified RIF-resistant isolates had concomitant MTBDRplus-detected INH resistance. Xpert had a sensitivity of 100.0% (95% CI, 92.1-100.0) for detecting RIF resistance but a positive-predictive value of only 61.6% (95% CI, 49.5-72.8) for MDR-TB. The most frequent mutations associated with RIF and INH resistance were S531L and S315T1, respectively. CONCLUSIONS: In this high-risk MDR-TB study population, Xpert had low positive-predictive value for the presence of MDR-TB. Comprehensive resistance testing for both INH and RIF should be performed in this setting.
BACKGROUND:Rifampicin (RIF) resistance is highly correlated with isoniazid (INH) resistance and used as proxy for multidrug-resistant tuberculosis (MDR-TB). Using MTBDRplus as a comparator, we evaluated the predictive value of Xpert MTB/RIF (Xpert)-detected RIF resistance for MDR-TB in eastern Democratic Republic of the Congo (DRC). METHODS: We conducted a cross-sectional study involving data from new or retreatment pulmonary adult TB cases evaluated between July 2013 and December 2016. Separate, paired sputa for smear microscopy and MTBDRplus were collected. Xpert testing was performed subject to the availability of Xpert cartridges on sample remnants after microscopy. RESULTS: Among 353 patients, 193 (54.7%) were previously treated and 224 (63.5%) were MTBDRplus TB positive. Of the 224, 43 (19.2%) were RIF monoresistant, 11 (4.9%) were INH monoresistant, 53 (23.7%) had MDR-TB, and 117 (52.2%) were RIF and INH susceptible. Overall, among the 96 samples detected by MTBDRplus as RIF resistant, 53 (55.2%) had MDR-TB. Xpert testing was performed in 179 (50.7%) specimens; among these, 163 (91.1%) were TB positive and 73 (44.8%) RIF resistant. Only 45/73 (61.6%) Xpert-identified RIF-resistant isolates had concomitant MTBDRplus-detected INH resistance. Xpert had a sensitivity of 100.0% (95% CI, 92.1-100.0) for detecting RIF resistance but a positive-predictive value of only 61.6% (95% CI, 49.5-72.8) for MDR-TB. The most frequent mutations associated with RIF and INH resistance were S531L and S315T1, respectively. CONCLUSIONS: In this high-risk MDR-TB study population, Xpert had low positive-predictive value for the presence of MDR-TB. Comprehensive resistance testing for both INH and RIF should be performed in this setting.
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