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BACKGROUND: Pulmonary TB (PTB) predominantly affects individuals of working age. We sought to characterise the occupations of people newly diagnosed with PTB in Karachi, Pakistan, by type and physical intensity. DESIGN/METHODS: We did a secondary analysis of data from a study evaluating the diagnostic accuracy of artificial intelligence-based chest X-ray (CXR) analysis software, where individuals had been evaluated for active PTB using sputum cultures and had provided information on occupation. We used an accelerometer-validated US National Health and Nutrition Examination Survey-based job categorisation to assign physical activity levels to participant-reported occupations as High, Intermediate, or Low. RESULTS: Among 272 participants with microbiologically confirmed PTB (women: 130/272, 48%; median age: 29 years, IQR 22–45), 78% (211/272) had smear-positive disease, and 96% (260/272) had data on occupation. Unemployment was common (women: 70/122, 57%; men: 23/138, 17%). Most women reporting an occupation were homemakers (21/52, 40%), and 54% (28/52) had an intermediate- or a high physical activity occupation. Among men reporting an occupation, 35% (40/115) were labourers, and 79% (91/115) had an intermediate- or high-physical activity occupation. CONCLUSION: The majority of individuals with PTB were in their working age, had extensive disease, and had intermediate or high physical activity occupations, suggesting economic vulnerability due to physical impairment.

Immune checkpoint inhibitors are the most important new medications in oncology and include inhibitors of programmed cell death protein-1 (PD-1) such as Pembrolizumab, Nivolumab, and Cemiplimab. These anticancer agents prevent tumour immune evasion and have been associated with a range of immune-related adverse events (irAEs) including those involving the nervous system. In this case report and literature review, we present the first case of inflammatory myeloradiculitis secondary to Pembrolizumab. We also summarise the characteristics, treatment, and outcomes of other cases reported in the literature which include a component of myelitis. Finally, we make general recommendations on management.

Objectives: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling., Methods: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use., Results: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%])., Conclusion: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis., Competing Interests: Declarations of competing interest FAK currently holds a grant from CIHR to study CAD in Canada. FAK also reports salary support from the Fonds de Recherche du Quebec Santé. FAK reports that the following developers of computer-aided detection software provided his research group with either free or reduced pricing access to their software for evaluative research, governed by contracts with the Research Institute of the McGill University Health Centre that ensure that the groups did not have any role in the study design, analysis, result interpretation, or decision to publish previous research and the submitted work: Delft (Netherlands, makers of CAD4TB), qure.ai (India, makers of qXR), and Lunit (South Korea, makers of LUNIT INSIGHT). MB reports grants from European and Development Countries Clinical Trials Partnership during the conduct of the study., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Computer-aided detection (CAD) may be a useful screening tool for tuberculosis (TB). However, there are limited data about its utility in active case finding (ACF) in a community-based setting, and particularly in an HIV-endemic setting where performance may be compromised., Methods: We performed a systematic review and evaluated articles published between January 2012 and February 2023 that included CAD as a screening tool to detect pulmonary TB against a microbiological reference standard (sputum culture and/or nucleic acid amplification test [NAAT]). We collected and summarized data on study characteristics and diagnostic accuracy measures. Two reviewers independently extracted data and assessed methodological quality against Quality Assessment of Diagnostic Accuracy Studies-2 criteria. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines were followed., Results: Of 1748 articles reviewed, 5 met with the eligibility criteria and were included in this review. A meta-analysis revealed pooled sensitivity of 0.87 (95% CI, 0.78-0.96) and specificity of 0.74 (95% CI, 0.55-0.93), just below the World Health Organization (WHO)-recommended target product profile (TPP) for a screening test (sensitivity ≥0.90 and specificity ≥0.70). We found a high risk of bias and applicability concerns across all studies. Subgroup analyses, including the impact of HIV and previous TB, were not possible due to the nature of the reporting within the included studies., Conclusions: This review provides evidence, specifically in the context of ACF, for CAD as a potentially useful and cost-effective screening tool for TB in a resource-poor HIV-endemic African setting. However, given methodological concerns, caution is required with regards to applicability and generalizability., Competing Interests: Potential conflicts of interest. All authors: no reported conflicts., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objectives: We applied computer-aided detection (CAD) software for chest X-ray (CXR) analysis to determine if diabetes affects the radiographic presentation of tuberculosis., Methods: From March 2017-July 2018, we consecutively enrolled adults being evaluated for pulmonary tuberculosis in Karachi, Pakistan. Participants had same-day CXR, two sputum mycobacterial cultures, and random blood glucose measurement. We identified diabetes through self-report or glucose >11.1mMol/L. We included participants with culture-confirmed tuberculosis for this analysis. We used linear regression to estimate associations between CAD-reported tuberculosis abnormality score (range 0.00 to 1.00) and diabetes, adjusting for age, body mass index, sputum smear-status, and prior tuberculosis. We also compared radiographic abnormalities between participants with and without diabetes., Results: 63/272 (23%) of included participants had diabetes. After adjustment, diabetes was associated with higher CAD tuberculosis abnormality scores (p < 0.001). Diabetes was not associated with frequency of CAD-reported radiographic abnormalities apart from cavitary disease; participants with diabetes were more likely to have cavitary disease (74.6% vs 61.2% p = 0.07), particularly non-upper zone cavitary disease (17% vs 7.8%, p = 0.09)., Conclusions: CAD analysis of CXR suggests diabetes is associated with more extensive radiographic abnormalities and with greater likelihood of cavities outside upper lung zones., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: CG, AM, GT, AN, SS, and AB have no potential conflicts of interest to disclose. Aamir J. Khan has had financial interests in the company Alcela, of which qure.ai is a client, and has provided technical assistance to quire.ai data scientists on the development of an all-in-artificial intelligence algorithm for mass CXR screening in public health programs. Consulting started in Q4 2019 and ended in Q2 2020. Solution was not finalized, and the planned product was not made commercially available. Aamir J. Khan had helped conceive and design the included study from Pakistan in 2016 to 2017, but was never directly involved in data collection, analysis or reporting of that study and his relationship with Alcela arose after the completion of data collection for that study. Aamir J. Khan was not involved in the design, analysis, reporting, writing, editing, or decision to submit the work reported in the present manuscript. As Principal Investigator, Faiz Ahmad Khan received the grant from the Canadian Institutes of Health Research (CIHR) to undertake the original study of CAD diagnostic accuracy in Pakistan whose data are analyzed for the present work. The present work is partially funded by a peer-reviewed grant from l'Observatoire International sur les Impacts Sociétaux de l'IA et du Numérique, which is a publicly funded observatory of the Fonds de Recherche du Quebec, which itself is the provincial research funding agency in the province of Quebec. FAK currently holds a grant from CIHR to study CAD in Canada. FAK also reports salary support from the Fonds de Recherche du Quebec Santé. Qure.ai (India) the developer and owner of qXR, the software evaluated in this study, provide our lab with free access to their software. They have no access to data, and no role in collection, analysis and interpretation of data; nor in the writing of reports; nor in the decision to submit work for publication. Delft (Netherlands) the developer and owner of CAD4TB, provide ou rlab with reduced research pricing access to their software. They have no access to data, and no role in collection, analysis and interpretation of data; nor in the writing of reports; nor in the decision to submit work for publication., (© 2023 The Authors.)

Background: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH)., Methods: We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy., Results: We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7-61.9]; Lunit, 54.1% [95% CI: 44.6-63.3]; qXRv2, 60.5% [95% CI: 51.7-68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, -13.4% [-21.1, -6.9]; Lunit, +2.2% [-3.6, +6.3]; qXRv2: -13.4% [-21.5, -6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, -12.3% [-19.5, -6.1]; Lunit, -17.2% [-24.6, -10.5]; qXRv2, -16.6% [-24.4, -9.9]. Accuracy was similar to human readers., Conclusions: For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.)

We assessed the COVID-19 pandemic's impact on treatment of latent tuberculosis, and of active tuberculosis, at 3 centers in Montreal and Toronto, using data from 10 833 patients (8685 with latent tuberculosis infection, 2148 with active tuberculosis). Observation periods prior to declarations of COVID-19 public health emergencies ranged from 219 to 744 weeks, and after declarations, from 28 to 33 weeks. In the latter period, reductions in latent tuberculosis infection treatment initiation rates ranged from 30% to 66%. At 2 centers, active tuberculosis treatment rates fell by 16% and 29%. In Canada, cornerstone measures for tuberculosis elimination weakened during the COVID-19 pandemic., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)