Symptomatic screening and computer-aided radiography for active-case finding of tuberculosis: a prediction model for TB case detection

Background: Scale-up of rapid tuberculosis (TB) diagnostics through GeneXpert MTB/Rif has supported active-case finding for TB in high burden countries and is being utilized to increase case-notification as part of the TB Reach initiative. However, the high cost per test necessitates investigation of screening approaches that can better rationalize the use of GeneXpert. The aim of this study was to investigate predictive accuracy and validation of a prediction model based on symptomatic screening and computer-aided detection (CAD) radiography compared with GeneXpert MTB/Rif for TB case detection in a high TB burden setting. Methods: Screening for TB was carried out at private Family Practitioner clinics in three low-income towns of Karachi, Pakistan. Suspects for TB were identified on the basis of the presence of cough, fever, hemoptysis, weight loss and night-sweats and were referred for chest X-ray (CXR). All CXRs were analyzed by CAD4TB v3.07 (Diagnostic Image Analysis Group, Nijmegen, The Netherlands), a CAD system developed for TB diagnosis. This system computes an abnormality score (0-100) by analyzing the shape, symmetry and texture of the lung fields. GeneXpert testing was carried out on all cases where good quality sputum samples could be obtained. Results: 324 consecutive cases referred for CXR and with sputum samples were recruited into the study. Prediction models were constructed using logistic regressions with TB detection as a binary outcome variable and sequentially adding CAD4TB scores, demographics and symp-tomatic screening as explanatory variables. The final prediction model was constructed using backwards stepwise Akaike's Information Criteria multiple logistic regression and included CAD4TB scores (OR 1.08, 95% CI: 1.04 - 1.12), cough >2 weeks (OR 3.10, 95% CI: 1.09 AC/a,!aEUR? 5.51), age (OR 0.97, 95% CI: 0.95 AC/a,!aEUR? 0.98) and gender (OR 1.01, 95% CI: 0.73 AC/a,!aEUR? 1.38). The area under receiver operator curve (AUC) for the model was 0.87 (95% CI: 0.83 AC/a,!aEUR? 0.91). The AUC of a split-set cross validation model for assessing internal validation was 0.84 (95% CI: 0.78 AC/a,!aEUR?0.89). The model was appropriately calibrated (Hosmer-Lemeshow X' 8.99, p-value 0.45). Conclusion: Combining CAD4TB scores with patient demographics and symptomatic screening data offers high predictive accuracy for TB. Multi-center studies are required for external validation of the model in order to provide appropriate evidence for its use in screening in high TB burden settings.