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 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.

Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers.

SETTING: A busy urban health centre in Lusaka, Zambia. OBJECTIVE: To compare the accuracy of automated reading (CAD4TB) with the interpretation of digital chest radiograph (CXR) by clinical officers for the detection of tuberculosis (TB). DESIGN: A retrospective analysis was performed on 161 subjects enrolled in a TB specimen bank study. CXRs were analysed using CAD4TB, which computed an image abnormality score (0-100). Four clinical officers scored the CXRs for abnormalities consistent with TB. We compared the automated readings and the readings by clinical officers against the bacteriological and radiological results used as reference. We report here the area under the receiver operating characteristic curve (AUC) and kappa (κ) statistics. RESULTS: Of 161 enrolled subjects, 97 had bacteriologically confirmed TB and 120 had abnormal CXR. The AUCs for CAD4TB and the clinical officers were respectively 0.73 and 0.65-0.75 in comparison with the bacteriological reference, and 0.91 and 0.89-0.94 in comparison with the radiological reference. P values indicated no significant differences, except for one clinical officer who performed significantly worse than CAD4TB (P < 0.05) using the bacteriological reference. κ values for CAD4TB and clinical officers with radiological reference were respectively 0.61 and 0.49-0.67. CONCLUSION: CXR assessment using CAD4TB and by clinical officers is comparable. CAD4TB has potential as a point-of-care test and for the automated identification of subjects who require further examinations.