Cargando…
Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children
BACKGROUND: In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To iden...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284336/ https://www.ncbi.nlm.nih.gov/pubmed/37351457 http://dx.doi.org/10.1093/ofid/ofad245 |
_version_ | 1785061379487563776 |
---|---|
author | Brooks, Meredith B Hussain, Hamidah Siddiqui, Sara Ahmed, Junaid F Jaswal, Maria Amanullah, Farhana Becerra, Mercedes Malik, Amyn A |
author_facet | Brooks, Meredith B Hussain, Hamidah Siddiqui, Sara Ahmed, Junaid F Jaswal, Maria Amanullah, Farhana Becerra, Mercedes Malik, Amyn A |
author_sort | Brooks, Meredith B |
collection | PubMed |
description | BACKGROUND: In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To identify children who would benefit from rapid initiation of TB treatment, we developed 2 clinical prediction tools. METHODS: We conducted a secondary analysis of a prospective intensified TB patient–finding intervention conducted in Pakistan in 2014–2016. TB disease was determined through either bacteriologic confirmation or a clinical diagnosis. We derived 2 tools: 1 uses classification and regression tree (CART) analysis to develop decision trees, while the second uses multivariable logistic regression to calculate a risk score. RESULTS: Of the 5162 and 5074 children included in the CART and prediction score, respectively, 1417 (27.5%) and 1365 (26.9%) were eligible for TB treatment. CART identified abnormal chest radiographs and family history of TB as the most important predictors (area under the receiver operating characteristic curve [AUC], 0.949). The final prediction score model included age group (0–4, 5–9, 10–14), weight <5th percentile, cough, fever, weight loss, chest radiograph suggestive of TB disease, and family history of TB; the identified best cutoff score was 9 (AUC, 0.985%). CONCLUSIONS: Use of clinical evidence was sufficient to accurately identify children who would benefit from treatment initiation. Our tools performed well compared with existing algorithms, though these results need to be externally validated before operationalization. |
format | Online Article Text |
id | pubmed-10284336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102843362023-06-22 Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children Brooks, Meredith B Hussain, Hamidah Siddiqui, Sara Ahmed, Junaid F Jaswal, Maria Amanullah, Farhana Becerra, Mercedes Malik, Amyn A Open Forum Infect Dis Major Article BACKGROUND: In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To identify children who would benefit from rapid initiation of TB treatment, we developed 2 clinical prediction tools. METHODS: We conducted a secondary analysis of a prospective intensified TB patient–finding intervention conducted in Pakistan in 2014–2016. TB disease was determined through either bacteriologic confirmation or a clinical diagnosis. We derived 2 tools: 1 uses classification and regression tree (CART) analysis to develop decision trees, while the second uses multivariable logistic regression to calculate a risk score. RESULTS: Of the 5162 and 5074 children included in the CART and prediction score, respectively, 1417 (27.5%) and 1365 (26.9%) were eligible for TB treatment. CART identified abnormal chest radiographs and family history of TB as the most important predictors (area under the receiver operating characteristic curve [AUC], 0.949). The final prediction score model included age group (0–4, 5–9, 10–14), weight <5th percentile, cough, fever, weight loss, chest radiograph suggestive of TB disease, and family history of TB; the identified best cutoff score was 9 (AUC, 0.985%). CONCLUSIONS: Use of clinical evidence was sufficient to accurately identify children who would benefit from treatment initiation. Our tools performed well compared with existing algorithms, though these results need to be externally validated before operationalization. Oxford University Press 2023-05-04 /pmc/articles/PMC10284336/ /pubmed/37351457 http://dx.doi.org/10.1093/ofid/ofad245 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Major Article Brooks, Meredith B Hussain, Hamidah Siddiqui, Sara Ahmed, Junaid F Jaswal, Maria Amanullah, Farhana Becerra, Mercedes Malik, Amyn A Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title | Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title_full | Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title_fullStr | Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title_full_unstemmed | Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title_short | Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children |
title_sort | two clinical prediction tools to inform rapid tuberculosis treatment decision-making in children |
topic | Major Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284336/ https://www.ncbi.nlm.nih.gov/pubmed/37351457 http://dx.doi.org/10.1093/ofid/ofad245 |
work_keys_str_mv | AT brooksmeredithb twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT hussainhamidah twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT siddiquisara twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT ahmedjunaidf twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT jaswalmaria twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT amanullahfarhana twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT becerramercedes twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren AT malikamyna twoclinicalpredictiontoolstoinformrapidtuberculosistreatmentdecisionmakinginchildren |