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

dc.rights.licenseopenen_US
dc.contributor.authorGUNASEKERA, Kenneth S.
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierGlobal Health in the Global South [GHiGS]
dc.contributor.authorMARCY, Olivier
dc.contributor.authorMUNOZ, Johanna
dc.contributor.authorLOPEZ-VARELA, Elisa
dc.contributor.authorSEKADDE, Moorine P.
dc.contributor.authorFRANKE, Molly F.
dc.contributor.authorBONNET, Maryline
dc.contributor.authorAHMED, Shakil
dc.contributor.authorAMANULLAH, Farhana
dc.contributor.authorANWAR, Aliya
dc.contributor.authorAUGUSTO, Orvalho
dc.contributor.authorAURILIO, Rafaela Baroni
dc.contributor.authorBANU, Sayera
dc.contributor.authorBATOOL, Iraj
dc.contributor.authorBRANDS, Annemieke
dc.contributor.authorCAIN, Kevin P.
dc.contributor.authorCARRATALA-CASTRO, Lucia
dc.contributor.authorCAWS, Maxine
dc.contributor.authorCLICK, Eleanor S.
dc.contributor.authorCRANMER, Lisa M.
dc.contributor.authorGARCIA-BASTEIRO, Alberto L.
dc.contributor.authorHESSELING, Anneke C.
dc.contributor.authorHUYNH, Julie
dc.contributor.authorKABIR, Senjuti
dc.contributor.authorLECCA, Leonid
dc.contributor.authorMANDALAKAS, Anna
dc.contributor.authorMAVHUNGA, Farai
dc.contributor.authorMYINT, Aye Aye
dc.contributor.authorMYO, Kyaw
dc.contributor.authorNAMPIJJA, Dorah
dc.contributor.authorNICOL, Mark P.
dc.contributor.authorORIKIRIZA, Patrick
dc.contributor.authorPALMER, Megan
dc.contributor.authorSANT'ANNA, Clemax Couto
dc.contributor.authorSIDDIQUI, Sara Ahmed
dc.contributor.authorSMITH, Jonathan P.
dc.contributor.authorSONG, Rinn
dc.contributor.authorTHUONG THUONG, Nguyen Thuy
dc.contributor.authorUNG, Vibol
dc.contributor.authorVAN DER ZALM, Marieke M.
dc.contributor.authorVERKUIJL, Sabine
dc.contributor.authorVINEY, Kerri
dc.contributor.authorWALTERS, Elisabetta G.
dc.contributor.authorWARREN, Joshua L.
dc.contributor.authorZAR, Heather J.
dc.contributor.authorMARAIS, Ben J.
dc.contributor.authorGRAHAM, Stephen M.
dc.contributor.authorDEBRAY, Thomas P. A.
dc.contributor.authorCOHEN, Ted
dc.contributor.authorSEDDON, James A.
dc.date.accessioned2023-04-18T08:15:29Z
dc.date.available2023-04-18T08:15:29Z
dc.date.issued2023-05
dc.identifier.issn2352-4650 (Electronic) 2352-4642 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/173040
dc.description.abstractEnBACKGROUND: 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 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) 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 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] 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.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.title.enDevelopment of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis
dc.title.alternativeLancet Child Adolesc Healthen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/s2352-4642(23)00004-4en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed36924781en_US
bordeaux.journalThe Lancet Child & Adolescent Healthen_US
bordeaux.page336-346en_US
bordeaux.volume7en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue5en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamGHIGS_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04072463
hal.version1
hal.date.transferred2023-07-04T09:59:28Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
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