Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries.

Autor: Codlin AJ; Friends for International TB Relief, Hanoi, Viet Nam.; Karolinska Institutet, Stockholm, Sweden., Vo LNQ; Friends for International TB Relief, Hanoi, Viet Nam.; Karolinska Institutet, Stockholm, Sweden., Garg T; Stop TB Partnership, Geneva, Switzerland., Banu S; icddr,b, Dhaka, Bangladesh., Ahmed S; icddr,b, Dhaka, Bangladesh., John S; Janna Health Foundation, Yola, Nigeria., Abdulkarim S; Janna Health Foundation, Yola, Nigeria., Muyoyeta M; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia., Sanjase N; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia., Wingfield T; Karolinska Institutet, Stockholm, Sweden.; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.; Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom., Iem V; Liverpool School of Tropical Medicine, Liverpool, United Kingdom., Squire B; Liverpool School of Tropical Medicine, Liverpool, United Kingdom., Creswell J; Stop TB Partnership, Geneva, Switzerland.
Jazyk: angličtina
Zdroj: BMC global and public health [BMC Glob Public Health] 2024; Vol. 2 (1), pp. 52. Date of Electronic Publication: 2024 Aug 01.
DOI: 10.1186/s44263-024-00081-2
Abstrakt: Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.
Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.
Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.
Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.
Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-024-00081-2.
Competing Interests: Competing interestsThe authors declare no competing interests.
(© The Author(s) 2024.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje