Article Text
Abstract
Background Childhood tuberculosis (TB) accounts for 12% of the 10.6 million incident cases of TB globally, and 16% of all TB-related mortality. The majority of childhood TB cases and deaths occur in TB-endemic countries where difficulties with confirming TB diagnosis with conventional sputum-based approaches contribute to poor outcomes. We present the methodological approaches and progress report from a study investigating the added value of non-sputum-based approaches for the diagnosis of TB in children in West Africa.
Methods This is a multi-country study recruiting children (age <15 years) with presumptive pulmonary TB at study sites in The Gambia, Ghana, and Benin. Participants undergo standardised conventional clinical, radiologic and microbiological investigations for TB diagnosis. In addition, early morning stool samples are simultaneously collected for testing with Xpert Ultra (‘stool Xpert’), while Computer-aided Detection for TB-version 7 (’CAD4TBv7’; Delft Imaging, Netherlands) abnormality score are derived for their digital chest radiographs (CXR). Bayesian latent class analysis will be used to determine the added value of the non-sputum-based tests in term of relative increases in sensitivity and specificity by combining CAD4TBv7 and stool Xpert results with conventional methods.
Results Recruitment and investigation of eligible study participant have commenced at the three study sites, with more than 100 children enrolled from January 2023 till date. The CAD4TBv7 system has been set up at the Gambia study site. Digital CXR from the two other study sites are de-identified and transferred electronically to The Gambia, using an encrypted internet-based file transfer software, for CAD4TBv7 scoring. A blinded senior radiologist also provides independent assessment of the likelihood of TB on each CXR.
Conclusion This study presents an opportunity to objectively determine how many additional childhood TB cases can be detected if CAD4TBv7 abnormality score and stool Xpert are combined with conventional diagnostic tests.
Funding: EDCTP-TALENT PhD Fellowship (Ref: PSIA2020AGDG-3317-TALENT)