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

Abstract

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 <60 months hospitalised with SAM at three tertiary hospitals in Zambia and Uganda from 4 November 2019 to 20 June 2022. We included children aged 2-59 months with SAM as defined by WHO and hospitalised following the WHO clinical criteria. We excluded children with current or history of antituberculosis treatment within the preceding 3 months. They underwent tuberculosis symptom screening, clinical assessment, chest X-ray, abdominal ultrasound, Xpert MTB/RIF Ultra (Ultra) and culture on respiratory and stool samples with 6 months follow-up. Tuberculosis was retrospectively defined using the 2015 standard case definition for childhood tuberculosis. We used logistic regression to develop diagnostic prediction models for a one-step diagnosis and a two-step screening and diagnostic approaches. We derived scores from models using WHO-recommended thresholds for sensitivity and proposed TDAs. This study is registered with ClinicalTrials.gov, NCT04240990.

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.

Keywords: Children; Diagnosis; Severe acute malnutrition; Treatment decision algorithms; Tuberculosis.

Fig. 1

Flow chart of the study. The figures represent the flow of children from admission to enrolment in the study, the tuberculosis diagnosis classification by the clinician and the reclassification by the Endpoint Review Committee. ERC, endpoint review committee; TB, tuberculosis; ∗Common reasons were: the child conditions worsened requiring urgent medical management, died before completion of informed consent process, languages barriers, consent decision awaiting another family member (usually father) who was not available.

Fig. 2

Receiver operating characteristic (ROC) curve and proposed diagnostic threshold of the one-step TB diagnostic prediction model. The final one-step diagnostic prediction model had an AUROC of 0.910. A cut-off giving a sensitivity of 86.14% and a specificity of 80.88% on the ROC curve was selected for the detection of tuberculosis. Se, sensitivity; Sp, specificity; AUC, area under the curve.

Fig. 3

Receiver operating characteristic (ROC) curves and proposed decision thresholds for the (A) First-step screening prediction model (B) Second-step diagnostic prediction model. In the two-step approach, in the final screening model (A) we selected a cut-off giving a sensitivity of 89.11% and a specificity of 34.79%, in the second step diagnostic prediction model we selected a predicted probability cut-off with a sensitivity of 88.89% and a specificity of 74.91%. Se, sensitivity; Sp, specificity; AUC, area under the curve.

Fig. 4

Proposed treatment decision algorithms: (A) ‘one step’ and (B) ‘two steps’ diagnostic approaches. TB, tuberculosis; SAM, Severe acute malnutrition; NPA, Nasopharyngeal aspirate; GA, gastric aspirate. The total score in each figure is the sum of each individual score from history, microbiological tests, Chest x-ray and ultrasound.

Fig. 4

Proposed treatment decision algorithms: (A) ‘one step’ and (B) ‘two steps’ diagnostic approaches. TB, tuberculosis; SAM, Severe acute malnutrition; NPA, Nasopharyngeal aspirate; GA, gastric aspirate. The total score in each figure is the sum of each individual score from history, microbiological tests, Chest x-ray and ultrasound.