Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 336-346 |
| Number of pages | 11 |
| Journal | The Lancet Child & Adolescent Health |
| Volume | 7 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2023 |
Access to Document
Other files and links
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis : an individual participant data meta-analysis. / Gunasekera, Kenneth S; Marcy, Olivier; Muñoz, Johanna et al.
In: The Lancet Child & Adolescent Health, Vol. 7, No. 5, 05.2023, p. 336-346.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis
T2 - an individual participant data meta-analysis
AU - Gunasekera, Kenneth S
AU - Marcy, Olivier
AU - Muñoz, Johanna
AU - Lopez-Varela, Elisa
AU - Sekadde, Moorine P
AU - Franke, Molly F
AU - Bonnet, Maryline
AU - Ahmed, Shakil
AU - Amanullah, Farhana
AU - Anwar, Aliya
AU - Augusto, Orvalho
AU - Aurilio, Rafaela Baroni
AU - Banu, Sayera
AU - Batool, Iraj
AU - Brands, Annemieke
AU - Cain, Kevin P
AU - Carratalá-Castro, Lucía
AU - Caws, Maxine
AU - Click, Eleanor S
AU - Cranmer, Lisa M
AU - García-Basteiro, Alberto L
AU - Hesseling, Anneke C
AU - Huynh, Julie
AU - Kabir, Senjuti
AU - Lecca, Leonid
AU - Mandalakas, Anna
AU - Mavhunga, Farai
AU - Myint, Aye Aye
AU - Myo, Kyaw
AU - Nampijja, Dorah
AU - Nicol, Mark P
AU - Orikiriza, Patrick
AU - Palmer, Megan
AU - Sant'Anna, Clemax Couto
AU - Siddiqui, Sara Ahmed
AU - Smith, Jonathan P
AU - Song, Rinn
AU - Thuong Thuong, Nguyen Thuy
AU - Ung, Vibol
AU - van der Zalm, Marieke M
AU - Verkuijl, Sabine
AU - Viney, Kerri
AU - Walters, Elisabetta G
AU - Warren, Joshua L
AU - Zar, Heather J
AU - Marais, Ben J
AU - Graham, Stephen M
AU - Debray, Thomas P A
AU - Cohen, Ted
AU - Seddon, James A
N1 - Funding Information: This work was supported by the WHO Global Tuberculosis Programme (GTB) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the US National Institutes of Health (NIH). It was used to inform WHO consolidated guidelines on tuberculosis management in children and adolescents and the accompanying operational handbook, both published by WHO in March, 2022. This research was also supported in part by the President's Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention (CDC). Staff from the WHO GTB and from the CDC are co-authors of this paper, offered feedback on the methodology and investigation, reviewed the manuscript, and made the decision to submit. The authors alone are responsible for the views expressed in this Article, and they do not necessarily represent the views, decisions or policies of the NIH, WHO, CDC, and PEPFAR. KSG was supported by the NIH through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (F30HD105440) as well as the Yale Medical Scientist Training Program (T32GM007205). EL-V has received funding from the European Respiratory Society and the European Union's H2020 research and innovation program under Marie Sklodowska-Curie grant agreement number 847462. ISGlobal receives support from the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa 2019–2023 Program (CEX2018-000806-S). SA, SB, and SK received funding support from the US Agency for International Development through Research for Decision Makers' Activity, and this Article was prepared through the Structured Operational Research and Training Initiative, a global partnership led by the Special Program for Research and Training in Tropical Diseases at WHO. LMC was supported by the NIH through the National Institute of Allergy and Infectious Disease (K23 AI143479). RS was supported by the NIH through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K23HD072802). MMvdZ is supported by a career development grant from the EDCTP2 programme supported by the EU (TMA2019SFP-2836 TB lung-FACT2), by the NIH Fogarty International Center (award number K43TW011028), and by funding from the South African Medical Research Council. EGW was supported by a scholarship for doctoral studies from the Medical Research Council (MRC) of South Africa under MRC Clinician Researcher Programme, the Faculty of Medicine and Health Sciences at Stellenbosch University (Early Career Grant and Temporary Research Assistantship grant), the Harry Crossley Foundation, and the South African National Research Foundation (Thuthuka programme funding for doctoral students). HJZ and MPN received funding from the NIH (R01HD058971) and the Regional Prospective Observational Research in Tuberculosis Consortium, co-funded by MRC of South Africa and the NIH. TC and JLW were supported by the NIH through the National Institute of Allergy and Infectious Disease (R01AI147854 to TC and R01AI137093 to JLW). JAS was supported by a Clinician Scientist Fellowship jointly funded by the UK MRC and the UK Department for International Development (DFID) under the Concordat agreement (MR/R007942/1). We acknowledge the patients and their caregivers for their willingness to participate in each individual study. We hope that their contribution will benefit other families affected by tuberculosis. We thank the following individuals who contributed to execution of this work: Albert Okumu, Andrea T Cruz, Carlos M Perez-Velez, Chad Heilig, Colleen Wright, Elisha Okeyo, Ha Thi Minh Đang, Hendrik Simon Schaaf, James Orwa, Lazarus Odeny, Lesley Workman, Maria R. Jaswal, Mariaem Andres, Mark Fajans, Moe Zaw, Parisa Hariri, Patrice Ahenda, Prisca Rabuogi, Rose Abwunza, Rumana Nasrin, Sabrina Choudhury, Scott Lee, Shaikh Shumail, Shoaib Ahmed, Susan Musau, Syed Mohammad Mazidur Rahman, Walter Mchembere, Yi Kyaw, Leonardo Martinez, Vivian Cox, Bryan Vonasek, and Alexander Kay. Finally, we acknowledge the members of the WHO Guideline Development Group who considered this evidence to inform the 2022 WHO consolidated guidelines on the management of tuberculosis in children and adolescents. Funding Information: This work was supported by the WHO Global Tuberculosis Programme (GTB) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the US National Institutes of Health (NIH). It was used to inform WHO consolidated guidelines on tuberculosis management in children and adolescents and the accompanying operational handbook, both published by WHO in March, 2022. This research was also supported in part by the President's Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention (CDC). Staff from the WHO GTB and from the CDC are co-authors of this paper, offered feedback on the methodology and investigation, reviewed the manuscript, and made the decision to submit. The authors alone are responsible for the views expressed in this Article, and they do not necessarily represent the views, decisions or policies of the NIH, WHO, CDC, and PEPFAR. KSG was supported by the NIH through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (F30HD105440) as well as the Yale Medical Scientist Training Program (T32GM007205). EL-V has received funding from the European Respiratory Society and the European Union's H2020 research and innovation program under Marie Sklodowska-Curie grant agreement number 847462. ISGlobal receives support from the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa 2019–2023 Program (CEX2018-000806-S). SA, SB, and SK received funding support from the US Agency for International Development through Research for Decision Makers' Activity, and this Article was prepared through the Structured Operational Research and Training Initiative, a global partnership led by the Special Program for Research and Training in Tropical Diseases at WHO. LMC was supported by the NIH through the National Institute of Allergy and Infectious Disease (K23 AI143479). RS was supported by the NIH through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K23HD072802). MMvdZ is supported by a career development grant from the EDCTP2 programme supported by the EU (TMA2019SFP-2836 TB lung-FACT2), by the NIH Fogarty International Center (award number K43TW011028), and by funding from the South African Medical Research Council. EGW was supported by a scholarship for doctoral studies from the Medical Research Council (MRC) of South Africa under MRC Clinician Researcher Programme, the Faculty of Medicine and Health Sciences at Stellenbosch University (Early Career Grant and Temporary Research Assistantship grant), the Harry Crossley Foundation, and the South African National Research Foundation (Thuthuka programme funding for doctoral students). HJZ and MPN received funding from the NIH (R01HD058971) and the Regional Prospective Observational Research in Tuberculosis Consortium, co-funded by MRC of South Africa and the NIH. TC and JLW were supported by the NIH through the National Institute of Allergy and Infectious Disease (R01AI147854 to TC and R01AI137093 to JLW). JAS was supported by a Clinician Scientist Fellowship jointly funded by the UK MRC and the UK Department for International Development (DFID) under the Concordat agreement (MR/R007942/1). We acknowledge the patients and their caregivers for their willingness to participate in each individual study. We hope that their contribution will benefit other families affected by tuberculosis. We thank the following individuals who contributed to execution of this work: Albert Okumu, Andrea T Cruz, Carlos M Perez-Velez, Chad Heilig, Colleen Wright, Elisha Okeyo, Ha Thi Minh Đang, Hendrik Simon Schaaf, James Orwa, Lazarus Odeny, Lesley Workman, Maria R. Jaswal, Mariaem Andres, Mark Fajans, Moe Zaw, Parisa Hariri, Patrice Ahenda, Prisca Rabuogi, Rose Abwunza, Rumana Nasrin, Sabrina Choudhury, Scott Lee, Shaikh Shumail, Shoaib Ahmed, Susan Musau, Syed Mohammad Mazidur Rahman, Walter Mchembere, Yi Kyaw, Leonardo Martinez, Vivian Cox, Bryan Vonasek, and Alexander Kay. Finally, we acknowledge the members of the WHO Guideline Development Group who considered this evidence to inform the 2022 WHO consolidated guidelines on the management of tuberculosis in children and adolescents. Publisher Copyright: © 2023 World Health Organization
PY - 2023/5
Y1 - 2023/5
N2 - 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 (38·4%) 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.
AB - 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 (38·4%) 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.
UR - http://www.scopus.com/inward/record.url?scp=85151564895&partnerID=8YFLogxK
U2 - 10.1016/S2352-4642(23)00004-4
DO - 10.1016/S2352-4642(23)00004-4
M3 - Article
C2 - 36924781
VL - 7
SP - 336
EP - 346
JO - The Lancet Child & Adolescent Health
JF - The Lancet Child & Adolescent Health
SN - 2352-4642
IS - 5
ER -