Development and validation 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. Diagnostic challenges in children include low bacterial burden, challenges around specimen collection, and limited access to diagnostic expertise. Algorithms that guide decisions to initiate tuberculosis treatment in resource-limited settings could help to close the persistent childhood tuberculosis treatment gap. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies conducted to date have been small and localised, with limited generalizability. Methods We collated individual participant data including clinical, bacteriological, and radiologic information from prospective diagnostic studies in high-tuberculosis incidence settings enrolling children <10 years with presumptive pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms and then developed multivariable prediction models, investigating model generalisability using internal-external cross-validation. A team of experts provided input to adapt the models into a pragmatic treatment-decision algorithm with a pre-determined sensitivity threshold of 85% for use in resource-limited, primary healthcare settings. Findings Of 4,718 children from 13 studies from 12 countries, 1,811 (38·4%) were classified as having pulmonary tuberculosis; 541 (29·9%) bacteriologically confirmed and 1,270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. Our prediction model had a combined sensitivity of 86% [95% confidence interval (CI): 0·68-0·94] and specificity of 37% [95% CI: 0·15-0·66] against a composite reference standard. 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 resource-limited, primary healthcare settings to initiate tuberculosis treatment in children in order 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. Funding World Health Organization, US National Institutes of Health Evidence before the study Treatment-decision algorithms relate information gained in the evaluation of children into an assessment of tuberculosis disease risk and empower healthcare workers to make appropriate treatment decisions. Studies in primary healthcare centres have demonstrated that use of treatment-decision algorithms can improve childhood pulmonary tuberculosis case-detection and treatment initiation in settings with high-tuberculosis incidence. To identify primary research studies on treatment-decision algorithm performance evaluation and/or development for childhood pulmonary tuberculosis, we carried out a PubMed search using the terms (‘child*’ OR ‘paediatr*’ OR ‘pediatr*’) AND (‘tuberculosis’ OR ‘TB’) AND (‘treatment-decision’ OR ‘algorithm’ OR ‘diagnos*’) to identify primary research published in any language prior to 29 June 2022. We additionally consulted multiple experts in childhood pulmonary tuberculosis diagnosis and management, and we referred to existing, published reviews of treatment-decision algorithms. With respect to treatment-decision algorithm performance, several studies have retrospectively estimated the performance of treatment-decision algorithms in a single geographic setting; a subset of these studies have also compared the performance of multiple algorithms using data from a single geographic setting. With respect to treatment-decision algorithm development, many existing algorithms have been developed without explicit analysis of data from children with presumptive pulmonary tuberculosis, often developed from expert consensus. Gunasekera et al. used model-based approaches to analyse diagnostic evaluations data (e.g., clinical history, physical examination, chest radiograph, and results from rapid molecular and culture testing for Mycobacterium tuberculosis ) collected from children with presumptive pulmonary tuberculosis in a single geographic setting to inform the development of a diagnostic algorithm while Marcy et al. and Fourie et al analysed data from multiple geographic settings. However, these studies were relatively small with limited assessment of generalisability. Added value of this study We collated individual participant data from 13 prospective diagnostic studies from 12 countries including 4,718 children with presumptive pulmonary tuberculosis from geographically diverse settings with a high incidence of tuberculosis in order to 1) evaluate the performance of existing treatment-decision algorithms and 2) develop multivariable logistic regression models to quantify the contribution of individual features to discriminate tuberculosis from non-tuberculosis. A panel of child tuberculosis experts provided input into performance targets and advised on how to incorporate scores derived from these models into pragmatic treatment-decision algorithms to assist in the evaluation of children presenting with presumptive pulmonary tuberculosis in primary healthcare centres. Implications of all the available evidence Our findings suggest that evidence-based, pragmatic treatment-decision algorithms can be developed to make sensitive and clinically appropriate decisions to treat a child with pulmonary tuberculosis. Although the specificity does not reach optimal targets for childhood tuberculosis diagnosis, pragmatic treatment-decision algorithms provide clinically relevant guidance that can empower health workers to start children on tuberculosis treatment at the primary healthcare setting and will likely contribute to reducing the case-detection gap in childhood tuberculosis. External, prospective evaluation of these novel algorithms in diverse settings is required, including assessment of their accuracy, feasibility, acceptability, impact, and cost-effectiveness. This work led to a new interim WHO recommendation to support the use of treatment-decision algorithms in the evaluation of children with presumptive tuberculosis in the 2022 updated consolidated guidelines on the management of tuberculosis in children. Two algorithms developed from this work have been included in the WHO operational handbook accompanying these guidelines. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the WHO Global Tuberculosis Programme (GTB) as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the United States National Institutes of Health (NIH). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Health Research Ethics Committee of Stellenbosch University (Ref No. X21/02/003) and and the Institutional Review Board of Yale University (Ref No. 2000028046) gave ethical approval for this work. All collaborating investigators confirmed institutional ethical approval for their original data collection. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data is available upon written request to the authors.