Accuracy of cobas MTB and MTB-RIF/INH for Detection of Mycobacterium tuberculosis and Drug Resistance
Year: 2024
Type: article
Study design: Observational Study
Abstract: This study evaluated the performance of cobas MTB and cobas MTB-RIF/INH for the diagnosis of tuberculosis and detection of rifampicin (RIF) and isoniazid (INH) resistance. Adults presenting with pulmonary tuberculosis symptoms were recruited in South Africa, Moldova, and India. Performance of cobas MTB was assessed against culture, whereas cobas MTB-RIF/INH was assessed using phenotypic drug susceptibility testing and whole-genome sequencing as composite reference standards. Xpert MTB/RIF (Xpert) or Xpert MTB/RIF Ultra (Ultra) was used as a comparator. The overall sensitivity and specificity of cobas MTB were 95% (95% CI, 93%-96%) and 96% (95% CI, 95%-97%). Among smear-negatives, the sensitivity of cobas MTB was 75% (95% CI, 66%-83%). Among participants tested with both cobas MTB and Xpert, sensitivity was 96% (95% CI, 94%-97%) for cobas MTB and 95% (95% CI, 93%-97%) for Xpert. Among participants tested with both cobas MTB and Ultra, sensitivity was 88% (95% CI, 81%-92%) for cobas MTB and 89% (95% CI, 83%-93%) for Ultra. Sensitivity and specificity of cobas MTB-RIF/INH for RIF and INH detection were 90% (95% CI, 84%-94%) and 100% (95% CI, 99%-100%), and 89% (95% CI, 84%-93%) and 99.5% (95% CI, 98%-100%), respectively. The cobas MTB and cobas MTB-RIF/INH assays exhibited high performance in a diverse population and present a suitable option for molecular detection of tuberculosis and RIF and INH resistance.
Source: Journal of Molecular Diagnostics
Authors: Margaretha de Vos, Anura David, Karthickeyan Duraisamy, Darshaalini Nadarajan, Ecaterina Noroc, Adam Penn-Nicholson, Valeriu Crudu, Sidhartha Giri, Florian P. Maurer, Sanghamitra Pati, Wendy Stevens, Lesley Scott, Jyotimayee Turuk, Samuel G. Schumacher, Morten Ruhwald, Nelly Ciobanu, Sarabijt Singh Chadha, Alexandru Codreanu, Dasarathi Das, Trish Kahamba, Itishree Kar, Aurélien Macé, Jyoti Mohanty, Pamela Nabeta, Katherina Kranzer, Stefano Ongarello, Archana Pattanaik, Elena Romancenco, Sanjay Sarin, Lyndel Singh, Sunita Singh, Nadia Turcan
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W4399447235