Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs
Molecular tests hold great potential for tuberculosis (TB) diagnosis, but are costly, time consuming and HIV-infected patients are often sputum scarce. Therefore, alternative approaches are needed. We evaluated automated digital chest radiography (ACR) as a rapid and cheap pre-screen test prior to X...
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Published in | Scientific reports Vol. 5; no. 1; p. 12215 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
27.07.2015
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Summary: | Molecular tests hold great potential for tuberculosis (TB) diagnosis, but are costly, time consuming and HIV-infected patients are often sputum scarce. Therefore, alternative approaches are needed. We evaluated automated digital chest radiography (ACR) as a rapid and cheap pre-screen test prior to Xpert MTB/RIF (Xpert). 388 suspected TB subjects underwent chest radiography, Xpert and sputum culture testing. Radiographs were analysed by computer software (CAD4TB) and specialist readers and abnormality scores were allocated. A triage algorithm was simulated in which subjects with a score above a threshold underwent Xpert. We computed sensitivity, specificity, cost per screened subject (CSS), cost per notified TB case (CNTBC) and throughput for different diagnostic thresholds. 18.3% of subjects had culture positive TB. For Xpert alone, sensitivity was 78.9%, specificity 98.1%, CSS $13.09 and CNTBC $90.70. In a pre-screening setting where 40% of subjects would undergo Xpert, CSS decreased to $6.72 and CNTBC to $54.34, with eight TB cases missed and throughput increased from 45 to 113 patients/day. Specialists, on average, read 57% of radiographs as abnormal, reducing CSS ($8.95) and CNTBC ($64.84). ACR pre-screening could substantially reduce costs and increase daily throughput with few TB cases missed. These data inform public health policy in resource-constrained settings. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 2045-2322 2045-2322 |
DOI: | 10.1038/srep12215 |