Molecular Epidemiology and Genetic Diversity of Multidrug-Resistant Mycobacterium tuberculosis Isolates in Bangladesh.

Although the number of multidrug-resistant (MDR) tuberculosis (TB) cases is high overall, a major gap exists in our understanding of the molecular characteristics and transmission dynamics of the MDR Mycobacterium tuberculosis isolates circulating in Bangladesh. The present study aims to characterize the MDR-TB isolates of Bangladesh and to investigate the mode of transmission. A total of 544 MDR-TB isolates were obtained from a nationwide drug-resistant TB surveillance study conducted between October 2011 and March 2017 covering all geographic divisions of Bangladesh. The isolates were characterized using TbD1 deletion analysis, spoligotyping, and mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) typing. Deletion analysis showed that 440 (80.9%) isolates were the modern type, while the remainder were the ancestral type. The largest circulating lineage was the Beijing type, comprising 208 isolates (38.2%), followed by T, EAI, and LAM with 93 (17.1%), 58 (10.7%), and 52 (9.5%) isolates, respectively. Combined MIRU-VNTR and spoligotyping analysis demonstrated that the majority of the clustered isolates were of the Beijing and T1 lineages. The overall rate of recent transmission was estimated at 33.8%. In conclusion, the MDR M. tuberculosis isolates circulating in Bangladesh are mostly of the modern virulent type. The Beijing and T lineages are the predominant types and most of the transmission of MDR-TB can be attributed to them. The findings also suggest that, along with the remarkable transmission, the emergence of MDR-TB in Bangladesh is largely due to acquired resistance. Rapid and accurate diagnosis and successful treatment will be crucial for controlling MDR-TB in Bangladesh. IMPORTANCE Multidrug-resistant TB is considered to be the major threat to tuberculosis control activities worldwide, including in Bangladesh. Despite the fact that the number of MDR-TB cases is high, a major gap exists in our understanding of the molecular epidemiology of the MDR-TB isolates in Bangladesh. In our study, we characterized and classified the MDR-TB isolates circulating in Bangladesh and investigated their mode of transmission. Our results demonstrated that the MDR M. tuberculosis isolates circulating in Bangladesh are mostly of the modern virulent type. The Beijing and T lineages are the predominant types and are implicated in the majority of MDR-TB transmission. Our findings reveal that, along with the remarkable transmission, the emergence of MDR-TB in Bangladesh is largely due to acquired resistance, which may be due to nonadherence to treatment or inadequate treatment of TB patients. Rapid diagnosis and adherence to an appropriate treatment regimen are therefore crucial to controlling MDR-TB in Bangladesh.

Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis.

Computer-aided detection (CAD) was recently recommended by the WHO for TB screening and triage based on several evaluations, but unlike traditional diagnostic tests, software versions are updated frequently and require constant evaluation. Since then, newer versions of two of the evaluated products have already been released. We used a case control sample of 12,890 chest X-rays to compare performance and model the programmatic effect of upgrading to newer versions of CAD4TB and qXR. We compared the area under the receiver operating characteristic curve (AUC), overall, and with data stratified by age, TB history, gender, and patient source. All versions were compared against radiologist readings and WHO's Target Product Profile (TPP) for a TB triage test. Both newer versions significantly outperformed their predecessors in terms of AUC: CAD4TB version 6 (0.823 [0.816-0.830]), version 7 (0.903 [0.897-0.908]) and qXR version 2 (0.872 [0.866-0.878]), version 3 (0.906 [0.901-0.911]). Newer versions met WHO TPP values, older versions did not. All products equalled or surpassed the human radiologist performance with improvements in triage ability in newer versions. Humans and CAD performed worse in older age groups and among those with TB history. New versions of CAD outperform their predecessors. Prior to implementation CAD should be evaluated using local data because underlying neural networks can differ significantly. An independent rapid evaluation centre is necessitated to provide implementers with performance data on new versions of CAD products as they are developed.

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.

BACKGROUND: Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance compares with each other and with radiologists. We aimed to evaluate five commercial AI algorithms for triaging tuberculosis using a large dataset that had not previously been used to train any AI algorithms. METHODS: Individuals aged 15 years or older presenting or referred to three tuberculosis screening centres in Dhaka, Bangladesh, between May 15, 2014, and Oct 4, 2016, were recruited consecutively. Every participant was verbally screened for symptoms and received a digital posterior-anterior chest x-ray and an Xpert MTB/RIF (Xpert) test. All chest x-rays were read independently by a group of three registered radiologists and five commercial AI algorithms: CAD4TB (version 7), InferRead DR (version 2), Lunit INSIGHT CXR (version 4.9.0), JF CXR-1 (version 2), and qXR (version 3). We compared the performance of the AI algorithms with each other, with the radiologists, and with the WHO's Target Product Profile (TPP) of triage tests (≥90% sensitivity and ≥70% specificity). We used a new evaluation framework that simultaneously evaluates sensitivity, proportion of Xpert tests avoided, and number needed to test to inform implementers' choice of software and selection of threshold abnormality scores. FINDINGS: Chest x-rays from 23 954 individuals were included in the analysis. All five AI algorithms significantly outperformed the radiologists. The areas under the receiver operating characteristic curve were 90·81% (95% CI 90·33-91·29) for qXR, 90·34% (89·81-90·87) for CAD4TB, 88·61% (88·03-89·20) for Lunit INSIGHT CXR, 84·90% (84·27-85·54) for InferRead DR, and 84·89% (84·26-85·53) for JF CXR-1. Only qXR (74·3% specificity [95% CI 73·3-74·9]) and CAD4TB (72·9% specificity [72·3-73·5]) met the TPP at 90% sensitivity. All five AI algorithms reduced the number of Xpert tests required by 50% while maintaining a sensitivity above 90%. All AI algorithms performed worse among older age groups (>60 years) and people with a history of tuberculosis. INTERPRETATION: AI algorithms can be highly accurate and useful triage tools for tuberculosis detection in high-burden regions, and outperform human readers. FUNDING: Government of Canada.