Development of a treatment-decision algorithm for HIV-uninfected children evaluated for pulmonary tuberculosis
File(s)
Accepted version
Accepted version
Author(s)
Type
Journal Article
Abstract
Background
Limitations in the sensitivity and accessibility of diagnostic tools for childhood tuberculosis contribute to the substantial gap between estimated cases and cases notified to national tuberculosis programs. Thus, tools to make accurate and rapid clinical diagnoses are necessary to initiate more children on antituberculosis treatment.
Methods
We analyzed data from a prospective cohort of children <13 years being routinely evaluated for pulmonary tuberculosis in Cape Town, South Africa from March 2012 to November 2017. We developed a regression model to describe the contributions of baseline clinical evaluation to the diagnosis of tuberculosis using standardized, retrospective case definitions. We included results from baseline chest radiography and Xpert MTB/RIF to the model to develop an algorithm with at least 90% sensitivity in predicting tuberculosis.
Results
Data from 478 children being evaluated for pulmonary tuberculosis were analyzed (median age: 16.2 months, interquartile range: 9.8-30.9); 242 (50.6%) were retrospectively classified with tuberculosis, of which 104 (43.0%) were bacteriologically-confirmed. The area under the receiver operating characteristic curve for the final model was 0.87. Clinical evidence identified 71.4% of all tuberculosis cases in this cohort, and inclusion of baseline chest radiography results increased the proportion to 89.3%. The algorithm was 90.1% sensitive and 52.1% specific, and maintained a sensitivity of above 90% among children <2 years or with low weight-for-age.
Conclusions
Clinical evidence alone was sufficient to make most clinical antituberculosis treatment decisions. The use of evidence-based algorithms may improve decentralized, rapid treatment-initiation, reducing the global burden of childhood mortality.
Limitations in the sensitivity and accessibility of diagnostic tools for childhood tuberculosis contribute to the substantial gap between estimated cases and cases notified to national tuberculosis programs. Thus, tools to make accurate and rapid clinical diagnoses are necessary to initiate more children on antituberculosis treatment.
Methods
We analyzed data from a prospective cohort of children <13 years being routinely evaluated for pulmonary tuberculosis in Cape Town, South Africa from March 2012 to November 2017. We developed a regression model to describe the contributions of baseline clinical evaluation to the diagnosis of tuberculosis using standardized, retrospective case definitions. We included results from baseline chest radiography and Xpert MTB/RIF to the model to develop an algorithm with at least 90% sensitivity in predicting tuberculosis.
Results
Data from 478 children being evaluated for pulmonary tuberculosis were analyzed (median age: 16.2 months, interquartile range: 9.8-30.9); 242 (50.6%) were retrospectively classified with tuberculosis, of which 104 (43.0%) were bacteriologically-confirmed. The area under the receiver operating characteristic curve for the final model was 0.87. Clinical evidence identified 71.4% of all tuberculosis cases in this cohort, and inclusion of baseline chest radiography results increased the proportion to 89.3%. The algorithm was 90.1% sensitive and 52.1% specific, and maintained a sensitivity of above 90% among children <2 years or with low weight-for-age.
Conclusions
Clinical evidence alone was sufficient to make most clinical antituberculosis treatment decisions. The use of evidence-based algorithms may improve decentralized, rapid treatment-initiation, reducing the global burden of childhood mortality.
Date Issued
2021-01-15
Date Acceptance
2021-01-12
Citation
Clinical Infectious Diseases, 2021, 73 (4), pp.e904-e912
ISSN
1058-4838
Publisher
Oxford University Press (OUP)
Start Page
e904
End Page
e912
Journal / Book Title
Clinical Infectious Diseases
Volume
73
Issue
4
Copyright Statement
© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This is a pre-copy-editing, author-produced version of an article accepted for publication in Clinical Infectious Diseases following peer review. The definitive publisher-authenticated version is available online at: https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciab018/6101512
Subjects
Diagnosis/* clinical decision-making
Diagnostic algorithm
clinical evidence
diagnostic system
pediatric
Microbiology
06 Biological Sciences
11 Medical and Health Sciences
Publication Status
Published
Date Publish Online
2021-01-15