Evaluating the diagnostic accuracy of WHO-recommended treatment decision algorithms for childhood tuberculosis using an individual person dataset: a study protocol.
Authors
Affiliations (26)
Affiliations (26)
- Institute of Infectious Diseases and Tropical Medicine, LMU University Hospital, Munchen, Germany.
- German Center for Infection Research Munich Site, Munchen, Germany.
- Fraunhofer Institute for Translational Medicine and Pharmacology, Immunology, Infection and Pandemic Research, Munich, Germany.
- Institute of Infectious Diseases and Tropical Medicine, LMU University Hospital, Munchen, Germany [email protected].
- School of Health and Related Research, The University of Sheffield, Sheffield, UK.
- Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Desmond Tutu TB Centre, Stellenbosch University, Stellenbosch, South Africa.
- Bordeaux Population Health, University of Bordeaux, Talence, France.
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.
- Unit on Child and Adolescent Health, South African Medical Research Council, Cape Town, South Africa.
- National Institute of Medical Research-Mbeya Medical Research Centre, Mbeya, United Republic of Tanzania.
- Instituto Nacional de Saude, Maputo, Mozambique.
- Helse Nord Tuberculosis Initiative, University of Malawi College of Medicine, Blantyre, Malawi.
- Department of Child Health, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India.
- Institut de Recherche pour le Développement (IRD), Université de Montpellier, Montpellier, France.
- Mulago National Referral Hospital, Kampala, Uganda.
- Arthur Davidson Children's Hospital, Ndola, Zambia.
- Programme PAC-CI, Abidjan, Lagunes, Côte d'Ivoire.
- Epicentre Mbarara Research Centre, Mbarara, Uganda.
- Ola During Children Hospital, Freetown, Sierra Leone.
- Solthis, Paris, France.
- Chantal Biya International Reference Centre for HIV/AIDS Research on Prevention and Treatment, Yaounde, Cameroon.
- Clinical Research Group, Epidemiology and Public Health Unit, Institut Pasteur in Cambodia, Phnom Penh, Cambodia.
- Center for Tuberculosis Research, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD20600, USA.
- Department of Paediatrics, University of Zambia, Lusaka, Zambia.
- Children's Hospital, University Teaching Hospital, Lusaka, Zambia.
- Imperial College London, London, UK.
Abstract
In 2022, the WHO conditionally recommended the use of treatment decision algorithms (TDAs) for treatment decision-making in children <10 years with presumptive tuberculosis (TB), aiming to decrease the substantial case detection gap and improve treatment access in high TB-incidence settings. WHO also called for external validation of these TDAs. Within the Decide-TB project (PACT ID: PACTR202407866544155, 23 July 2024), we aim to generate an individual-participant dataset (IPD) from prospective TB diagnostic accuracy cohorts (RaPaed-TB, UMOYA and two cohorts from TB-Speed). Using the IPD, we aim to: (1) assess the diagnostic accuracy of published TDAs using a set of consensus case definitions produced by the National Institute of Health as reference standard (confirmed and unconfirmed vs unlikely TB); (2) evaluate the added value of novel tools (including biomarkers and artificial intelligence-interpreted radiology) in the existing TDAs; (3) generate an artificial population, modelling the target population of children eligible for WHO-endorsed TDAs presenting at primary and secondary healthcare levels and assess the diagnostic accuracy of published TDAs and (4) identify clinical predictors of radiological disease severity in children from the study population of children with presumptive TB. This study will externally validate the first data-driven WHO TDAs in a large, well-characterised and diverse paediatric IPD derived from four large paediatric cohorts of children investigated for TB. The study has received ethical clearance for sharing secondary deidentified data from the ethics committees of the parent studies (RaPaed-TB, UMOYA and TB Speed) and as the aims of this study were part of the parent studies' protocols, a separate approval was not necessary. Study findings will be published in peer-reviewed journals and disseminated at local, regional and international scientific meetings and conferences. This database will serve as a catalyst for the assessment of the inclusion of novel tools and the generation of an artificial population to simulate the impact of novel diagnostic pathways for TB in children at lower levels of healthcare. TDAs have the potential to close the diagnostic gap in childhood TB. Further finetuning of the currently available algorithms will facilitate this and improve access to care.