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Sci Rep. 2015 Jul 27;5:12215. doi: 10.1038/srep12215.

Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs.

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Diagnostic Image Analysis Group, Radboud university medical center, Department of Radiology and Nuclear Medicine Internal postal code 766, 6500 HB Nijmegen, The Netherlands.
Department of Radiology and Nuclear Medicine, Radboud university medical center, Department of Radiology, Internal postal code 766 6500 HB Nijmegen, The Netherlands.
Lung Infection and Immunity Unit and Centre for Tuberculosis Research Innovation, Division of Pulmonology, Department of Medicine, University of Cape Town Lung Institute Cape Town, South Africa.


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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