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The Accuracy of the Uganda National Tuberculosis and Leprosy Program diagnostic algorithm and the World Health
Peter J Kitonsa1,2, Bernard Kikaire2, Peter Wambi1
1Uganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Insights
Diagnosing childhood tuberculosis (TB) is difficult. This study compared the Uganda NTLP algorithm with WHO TB diagnostic algorithms, finding WHO
Area of Science:
- Pediatrics
- Infectious Diseases
- Public Health
Background:
- Childhood tuberculosis (TB) diagnosis presents significant challenges.
- The Uganda National Tuberculosis and Leprosy Program (NTLP) developed a clinical treatment decision algorithm (TDA) for pediatric TB.
- Limited data exists on the accuracy of the Uganda NTLP TDA and its comparison to newer World Health Organization (WHO) TDAs for children.
Purpose of the Study:
- To evaluate and compare the diagnostic accuracy of the 2017 Uganda NTLP TDA against the 2022 WHO TDAs for pediatric TB.
- To assess the performance of these algorithms using clinical data from children in Uganda.
- To inform potential improvements in TB diagnosis algorithms for children.
Main Methods:
- Analysis of four years of clinical data from children under 15 years old in Kampala, Uganda.
- Classification of children based on National Institutes of Health (NIH) consensus definitions: Confirmed, Unconfirmed, or Unlikely TB.
- Application of the 2017 Uganda NTLP algorithm and the 2022 WHO TDAs (with and without chest x-ray [CXR]) to determine TB treatment decisions.
- Calculation of sensitivity, specificity, and predictive values using microbiological and composite reference standards.
Main Results:
- The Uganda NTLP algorithm demonstrated high sensitivity (97.9%) but low specificity (25.9% with CXR, 28.1% without CXR).
- WHO TDAs showed similar sensitivity but varied specificity: Algorithm A (with CXR) had 32.2% specificity, while Algorithm B (without CXR) had 15.4% specificity.
- WHO TDAs offered improved specificity compared to the NTLP algorithm when CXR was utilized, but were less specific when CXR was unavailable.
Conclusions:
- Further optimization of both the Uganda NTLP and WHO TB diagnostic algorithms is necessary.
- Improving algorithm specificity is crucial to reduce the over-treatment of TB in children.
- Enhanced diagnostic tools are needed to accurately identify childhood pulmonary TB.
Abstract:
Diagnosing childhood pulmonary tuberculosis (TB) is a challenge. This led the Uganda National Tuberculosis and Leprosy Program (NTLP) to develop a clinical treatment decision algorithm (TDA) for children. However, there is limited data on its accuracy, and how it compares to new World Health Organization (WHO) TB TDAs for children. This study aimed to evaluate and compare the accuracy of the 2017 Uganda NTLP diagnostic algorithm with the 2022 WHO TDAs for TB among children. We analyzed four years of clinical data from children <15 years old in Kampala, Uganda. Children were classified as per National Institutes of Health (NIH) consensus definitions (Confirmed, Unconfirmed or Unlikely TB). We applied the 2017 Uganda NTLP and 2022 WHO algorithms (A with chest x-ray [CXR], B without CXR) to make a decision to treat for TB or not, and calculated the sensitivity, specificity and predictive values in reference to Confirmed vs. Unlikely TB, as well as a microbiological and composite reference standard. Of the 699 children included in this analysis, 64% (451/699) were under 5 years, 53% (373/669) were male, 12% (85/699) were Xpert Ultra positive, 11% (74/669) were HIV positive and 6% had severe acute malnutrition (SAM). The Uganda NTLP algorithm had a sensitivity of 97.9% (95% CI: 96.4-99.4) and specificity of 25.9% (95% CI: 21.2-30.7). If CXR was considered unavailable, sensitivity was 97.9% (95% CI: 96.4-99.4) and specificity 28.1% (95% CI: 23.2-33.0). In comparison, WHO TDAs had similar sensitivity to the Uganda NTLP, but algorithm A was more specific (32.2%, 95% CI: 26.9-37.5) and algorithm B was less specific (15.4%, 95% CI: 11.3-19.5). The WHO TDAs had better specificity than the NTLP algorithm with CXR, and worse specificity without CXR. Further optimization of the algorithms is needed to improve specificity and reduce over-treatment of TB in children.
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