Browsing by Author "Nathavitharana R"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.
    (2022-Apr-28) Tavaziva G; Harris M; Abidi SK; Geric C; Breuninger M; Dheda K; Esmail A; Muyoyeta M; Reither K; Majidulla A; Khan AJ; Campbell JR; David PM; Denkinger C; Miller C; Nathavitharana R; Pai M; Benedetti A; Ahmad Khan F; Swiss Tropical and Public Health Institute, Basel, Switzerland.; IRD Global, Singapore.; World Health Organization, Geneva, Switzerland.; Département des Médicaments et Santé des Populations, Faculty of Pharmacy, Université de Montréal, Montreal, Canada.; Centre for Lung Infection and Immunity Unit, Division of Pulmonology and UCT Lung Institute, University of Cape Town, Cape Town, South Africa.; Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada.; Division of Infectious Diseases, Department I of Internal Medicine, University of Cologne, Cologne, Germany.; Interactive Research & Development (IRD) Pakistan, Karachi, Pakistan.; Division of Tropical Medicine, Center of Infectious Diseases, University Hospital Heidelberg, Heidelberg, Germany.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; University of Basel, Basel, Switzerland.; Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.; Faculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.; Departments of Medicine & Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.; Zambart, Lusaka, Zambia.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    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.
  • Thumbnail Image
    Item
    Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research.
    (2024-Jul) Broger T; Marx FM; Theron G; Marais BJ; Nicol MP; Kerkhoff AD; Nathavitharana R; Huerga H; Gupta-Wright A; Kohli M; Nichols BE; Muyoyeta M; Meintjes G; Ruhwald M; Peeling RW; Pai NP; Pollock NR; Pai M; Cattamanchi A; Dowdy DW; Dewan P; Denkinger CM; Centre for Infectious Diseases Research in Zambia, Lusaka, Zambia.; Boston Children's Hospital, Boston, MA, USA.; The University of Sydney Infectious Diseases Institute, Sydney, NSW, Australia; Children's Hospital at Westmead, Sydney, NSW, Australia.; Bill & Melinda Gates Foundation, Seattle, WA, USA.; Department of Epidemiology, Epicentre, Paris, France.; McGill International TB Centre, McGill University, Montreal, QC, Canada.; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Department of Medicine, Division of Pulmonary Diseases and Critical Care Medicine, University of California Irvine, Irvine, CA, USA.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Faculty of Science, Stellenbosch University, Stellenbosch, South Africa.; London School of Hygiene & Tropical Medicine, London, UK.; DSI-NRF 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, Cape Town, South Africa.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; German Center for Infection Research, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: claudia.denkinger@uni-heidelberg.de.; Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Medicine, University of Cape Town, Cape Town, South Africa; Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.; Division of HIV, Infectious Diseases, and Global Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA.; Division of Infection and Immunity, School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia.; Department of Medicine, Centre for Outcomes Research & Evaluation, McGill University, Montreal, QC, Canada.; FIND, Geneva, Switzerland.; Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, MA, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    Better access to tuberculosis testing is a key priority for fighting tuberculosis, the leading cause of infectious disease deaths in people. Despite the roll-out of molecular WHO-recommended rapid diagnostics to replace sputum smear microscopy over the past decade, a large diagnostic gap remains. Of the estimated 10·6 million people who developed tuberculosis globally in 2022, more than 3·1 million were not diagnosed. An exclusive focus on improving tuberculosis test accuracy alone will not be sufficient to close the diagnostic gap for tuberculosis. Diagnostic yield, which we define as the proportion of people in whom a diagnostic test identifies tuberculosis among all people we attempt to test for tuberculosis, is an important metric not adequately explored. Diagnostic yield is particularly relevant for subpopulations unable to produce sputum such as young children, people living with HIV, and people with subclinical tuberculosis. As more accessible non-sputum specimens (eg, urine, oral swabs, saliva, capillary blood, and breath) are being explored for point-of-care tuberculosis testing, the concept of yield will be of growing importance. Using the example of urine lipoarabinomannan testing, we illustrate how even tests with limited sensitivity can diagnose more people with tuberculosis if they enable increased diagnostic yield. Using tongue swab-based molecular tuberculosis testing as another example, we provide definitions and guidance for the design and conduct of pragmatic studies that assess diagnostic yield. Lastly, we show how diagnostic yield and other important test characteristics, such as cost and implementation feasibility, are essential for increased effective population coverage, which is required for optimal clinical care and transmission impact. We are calling for diagnostic yield to be incorporated into tuberculosis test evaluation processes, including the WHO Grading of Recommendations, Assessment, Development, and Evaluations process, providing a crucial real-life implementation metric that complements traditional accuracy measures.