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Updated: Nov 21, 2025
An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis
Published on: August 16, 2021
Development of a Treatment-decision Algorithm for Human Immunodeficiency Virus-uninfected Children Evaluated for
Kenneth S Gunasekera1, Elisabetta Walters2, Marieke M van der Zalm2
1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.
Insights
Developing a diagnostic algorithm using clinical evidence and chest X-rays can improve childhood tuberculosis diagnosis. This approach aids in rapid treatment initiation for more children with tuberculosis, reducing mortality.
Area of Science:
- Pediatrics
- Infectious Diseases
- Diagnostic Medicine
Background:
- Childhood tuberculosis diagnosis is hindered by insensitive and inaccessible tools, creating a gap between estimated and notified cases.
- Accurate and rapid diagnostic tools are crucial for timely antituberculosis treatment initiation in children.
Purpose of the Study:
- To develop and validate a diagnostic algorithm for childhood pulmonary tuberculosis.
- To improve the sensitivity and accessibility of tuberculosis diagnosis in children.
Main Methods:
- Analysis of a prospective cohort of 478 children (<13 years) evaluated for pulmonary tuberculosis in Cape Town, South Africa (2012-2017).
- Development of a regression model incorporating clinical evaluation, chest radiography, and Xpert MTB/RIF assay results.
- Standardized, retrospective case definitions were used to classify tuberculosis status.
Main Results:
- The final algorithm achieved 90.1% sensitivity and 52.1% specificity for diagnosing tuberculosis.
- Clinical evidence alone identified 71.4% of cases; adding chest radiography increased this to 89.3%.
- High sensitivity (>90%) was maintained in children <2 years old and those with low weight-for-age.
Conclusions:
- Clinical evidence is often sufficient for initiating antituberculosis treatment decisions in children.
- Evidence-based diagnostic algorithms can enhance decentralized and rapid treatment initiation.
- Improved diagnostic strategies are essential for reducing the global burden of childhood tuberculosis mortality.
Background:
Limitations in the sensitivity and accessibility of diagnostic tools for childhood tuberculosis contribute to the substantial gap between estimated cases and cases notified to national tuberculosis programs. Thus, tools to make accurate and rapid clinical diagnoses are necessary to initiate antituberculosis treatment in more children.
Methods:
We analyzed data from a prospective cohort of children <13 years old being routinely evaluated for pulmonary tuberculosis in Cape Town, South Africa, from March 2012 to November 2017. We developed a regression model to describe the contributions of baseline clinical evaluation to the diagnosis of tuberculosis using standardized, retrospective case definitions. We included baseline chest radiographic and Xpert MTB/RIF assay results to the model to develop an algorithm with ≥90% sensitivity in predicting tuberculosis.
Results:
Data from 478 children being evaluated for pulmonary tuberculosis were analyzed (median age, 16.2 months; interquartile range, 9.8-30.9 months); 242 (50.6%) were retrospectively classified with tuberculosis, bacteriologically confirmed in 104 (43.0%). The area under the receiver operating characteristic curve for the final model was 0.87. Clinical evidence identified 71.4% of all tuberculosis cases in this cohort, and inclusion of baseline chest radiographic results increased the proportion to 89.3%. The algorithm was 90.1% sensitive and 52.1% specific, and maintained a sensitivity of >90% among children <2 years old or with low weight for age.
Conclusions:
Clinical evidence alone was sufficient to make most clinical antituberculosis treatment decisions. The use of evidence-based algorithms may improve decentralized, rapid treatment initiation, reducing the global burden of childhood mortality.
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