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Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis
BACKGROUND: In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)–based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, th...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671604/ https://www.ncbi.nlm.nih.gov/pubmed/34917694 http://dx.doi.org/10.1093/ofid/ofab567 |
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author | Nsengiyumva, Ntwali Placide Hussain, Hamidah Oxlade, Olivia Majidulla, Arman Nazish, Ahsana Khan, Aamir J Menzies, Dick Ahmad Khan, Faiz Schwartzman, Kevin |
author_facet | Nsengiyumva, Ntwali Placide Hussain, Hamidah Oxlade, Olivia Majidulla, Arman Nazish, Ahsana Khan, Aamir J Menzies, Dick Ahmad Khan, Faiz Schwartzman, Kevin |
author_sort | Nsengiyumva, Ntwali Placide |
collection | PubMed |
description | BACKGROUND: In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)–based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool. METHODS: A decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars. RESULTS: Compared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%–4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective. CONCLUSIONS: In lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection. |
format | Online Article Text |
id | pubmed-8671604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86716042021-12-15 Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis Nsengiyumva, Ntwali Placide Hussain, Hamidah Oxlade, Olivia Majidulla, Arman Nazish, Ahsana Khan, Aamir J Menzies, Dick Ahmad Khan, Faiz Schwartzman, Kevin Open Forum Infect Dis Major Articles BACKGROUND: In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)–based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool. METHODS: A decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars. RESULTS: Compared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%–4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective. CONCLUSIONS: In lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection. Oxford University Press 2021-12-15 /pmc/articles/PMC8671604/ /pubmed/34917694 http://dx.doi.org/10.1093/ofid/ofab567 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Major Articles Nsengiyumva, Ntwali Placide Hussain, Hamidah Oxlade, Olivia Majidulla, Arman Nazish, Ahsana Khan, Aamir J Menzies, Dick Ahmad Khan, Faiz Schwartzman, Kevin Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title | Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title_full | Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title_fullStr | Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title_full_unstemmed | Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title_short | Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis |
title_sort | triage of persons with tuberculosis symptoms using artificial intelligence–based chest radiograph interpretation: a cost-effectiveness analysis |
topic | Major Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671604/ https://www.ncbi.nlm.nih.gov/pubmed/34917694 http://dx.doi.org/10.1093/ofid/ofab567 |
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