Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research. Broger, T., Marx, F. M, Theron, G., Marais, B. J, Nicol, M. P, Kerkhoff, A. D, Nathavitharana, R., Huerga, H., Gupta-Wright, A., Kohli, M., Nichols, B. E, Muyoyeta, M., Meintjes, G. A, Ruhwald, M., Peeling, R. W, Pai, N. P., Pollock, N. R, Pai, M., Cattamanchi, A., Dowdy, D. W, Dewan, P., & Denkinger, C. M The Lancet Global Health, 12(7):e1184–e1191, Elsevier, jul, 2024.
Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research [link]Paper  doi  abstract   bibtex   
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.
@article{Broger2024,
abstract = {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{\textperiodcentered}6 million people who developed tuberculosis globally in 2022, more than 3{\textperiodcentered}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.},
author = {Broger, Tobias and Marx, Florian M and Theron, Grant and Marais, Ben J and Nicol, Mark P and Kerkhoff, Andrew D and Nathavitharana, Ruvandhi and Huerga, Helena and Gupta-Wright, Ankur and Kohli, Mikashmi and Nichols, Brooke E and Muyoyeta, Monde and Meintjes, Graeme A and Ruhwald, Morten and Peeling, Rosanna W and Pai, Nitika Pant and Pollock, Nira R and Pai, Madhukar and Cattamanchi, Adithya and Dowdy, David W and Dewan, Puneet and Denkinger, Claudia M},
doi = {10.1016/S2214-109X(24)00148-7},
file = {:C$\backslash$:/Users/01462563/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Broger et al. - 2024 - Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests rationale and guidance for.pdf:pdf},
issn = {2214-109X},
journal = {The Lancet Global Health},
keywords = {OA,fund{\_}not{\_}ack,original},
mendeley-tags = {OA,fund{\_}not{\_}ack,original},
month = {jul},
number = {7},
pages = {e1184--e1191},
pmid = {38876764},
publisher = {Elsevier},
title = {{Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2214109X24001487},
volume = {12},
year = {2024}
}

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