Development of tuberculosis treatment decision algorithms in children below 5 years hospitalised with severe acute malnutrition in Zambia and Uganda: a prospective diagnostic cohort study

Background In children with severe acute malnutrition (SAM) tuberculosis is common, challenging to diagnose, and often fatal. We developed tuberculosis treatment decision algorithms (TDAs) for children under the age of 5 years with SAM. Methods In this prospective diagnostic study, we enrolled and followed up children aged Findings Of 1906 children hospitalised with SAM during the study period, 1230 were screened, 1152 were eligible and 603 were enrolled. Of the 603 children enrolled-median age 15 (inter-quartile range (IQR): 11-20) months and 65 (11.0%) living with HIV-114 (18.9%) were diagnosed with tuberculosis, including 51 (8.5%) with microbiological confirmation and 104 (17.2%) initiated treatment at a median of 6(IQR: 2-10) days after inclusion. 108 children were retrospectively classified as having tuberculosis resulting in a prevalence of 17.9% (95% confidence intervals (CI): 15.1; 21.2). 75 (69.4%) children with tuberculosis reported cough of any duration, 32 (29.6%) cough ≥2 weeks and 11 (10.2%) tuberculosis contact history. 535 children had complete data and were included in the diagnostic prediction model. The one-step diagnostic model had 15 predictors, including Ultra, clinical, radiographic, and abdominal features, an area under the receiving operating curve (AUROC) of 0.910, and derived TDA sensitivity of 86.14% (95% CI: 78.07-91.56) and specificity of 80.88% (95% CI: 76.91-84.30). The two-step model had AUROCs of 0.750 and 0.912 for screening and diagnosis, respectively, and derived combined TDA sensitivity of 79.21% (95% CI: 70.30-85.98) and a specificity of 83.64% (95% CI: 79.87-86.82). Interpretation Tuberculosis prevalence was high among hospitalised children with SAM, with atypical clinical features. TDAs achieved satisfactory diagnostic accuracy and could be used to improve diagnosis in this vulnerable group. Funding Unitaid.