Imperial College London
Portrait Picture

Professor James Seddon

Faculty of Medicine, Department of Infectious Disease

Professor of Global Child Health
 
 
 
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Contact

 

+44 (0)20 7594 3179james.seddon

 
 
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Location

 

235Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gunasekera:2023:10.1016/S2352-4642(23)00004-4,
author = {Gunasekera, K and Seddon, J},
doi = {10.1016/S2352-4642(23)00004-4},
journal = {The Lancet Child & Adolescent Health},
pages = {336--346},
title = {Development and validation of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis},
url = {http://dx.doi.org/10.1016/S2352-4642(23)00004-4},
volume = {7},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundMany 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.MethodsFor 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.FindingsOf 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having
AU  - Gunasekera,K
AU  - Seddon,J
DO  - 10.1016/S2352-4642(23)00004-4
EP  - 346
PY  - 2023///
SN  - 2352-4642
SP  - 336
TI  - Development and validation of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis
T2  - The Lancet Child & Adolescent Health
UR  - http://dx.doi.org/10.1016/S2352-4642(23)00004-4
UR  - https://www.thelancet.com/journals/lanchi/article/PIIS2352-4642(23)00004-4/fulltext
UR  - http://hdl.handle.net/10044/1/102731
VL  - 7
ER  -
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