Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis

Background Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres.Methods For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings.Findings Of 4718 children from 13 studies from 12 countries, 1811 (38 center dot 4%) were classified as having pulmonary tuberculosis: 541 (29 center dot 9%) bacteriologically confirmed and 1270 (70 center dot 1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0 center dot 86 [95% CI 0 center dot 68-0 center dot 94] and specificity of 0 center dot 37 [0 center dot 15-0 center dot 66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0 center dot 84 [95% CI 0 center dot 66-0 center dot 93] and specificity of 0 center dot 30 [0 center dot 13-0 center dot 56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms.Interpretation We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance.Funding WHO, US National Institutes of Health.Copyright (c) 2023 World Health Organization. Published by Elsevier Ltd. All rights reserved. This is an Open Access article published under the CC BY 3.0 IGO license which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any use of this article, there should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.

Automated summary

This study evaluated the performance of current diagnostic algorithms for pediatric tuberculosis and developed evidence-based algorithms using prediction modeling to assist in treatment decision-making. The existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical and chest x-ray features had higher sensitivity and lower specificity, while the scoring system derived from clinical features only also accurately diagnosed tuberculosis to some extent.