Exhaled breath analysis: A promising triage test for tuberculosis in young children

Else M Bijker1, Jonathan P Smith2, Walter Mchembere3

  • 1Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK; Department of Paediatrics, Maastricht University Medical Centre, MosaKids Children's Hospital, Maastricht, the Netherlands.

PubMed

Insights

Diagnosing childhood tuberculosis in infants is challenging. New breath analysis technology shows potential as a screening tool for pediatric tuberculosis, aiding early detection in young children.

Area of Science:

  • Pediatric pulmonology
  • Infectious disease diagnostics
  • Biomarker discovery

Background:

  • Diagnosing pediatric pulmonary tuberculosis, particularly in non-expectorating infants, presents significant clinical challenges.
  • Exhaled breath analysis offers a non-invasive method for respiratory infection diagnosis, but its application in pediatric tuberculosis requires further investigation.
  • Limited data exists on the efficacy of breath testing for diagnosing tuberculosis in children under five years old.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of exhaled breath analysis using a novel device in children under five years with suspected tuberculosis.
  • To assess the potential of breath analysis as a triage tool for pediatric tuberculosis in a resource-limited setting.

Main Methods:

  • A prospective cross-sectional study was conducted in Kenya involving 118 children under five years presenting with tuberculosis symptoms.
  • Exhaled breath samples were collected using a portable, battery-powered device.
  • Machine learning algorithms were employed to analyze breath samples, classifying them as microbiologically confirmed tuberculosis (positive) or unlikely tuberculosis (negative).

Main Results:

  • The optimal machine learning model achieved an area under the curve (AUC) of 0.73.
  • The test demonstrated a sensitivity of 86% (95% CI 62-96%) and a specificity of 42% (95% CI 30-55%).
  • While promising, the performance metrics did not meet the World Health Organization's target product profile for tuberculosis diagnostics.

Conclusions:

  • Exhaled breath analysis, coupled with machine learning, shows potential as a non-invasive screening or triage test for pediatric tuberculosis.
  • Further research and technological refinement are necessary to improve specificity and meet established diagnostic targets for widespread clinical adoption.
  • This approach could facilitate earlier detection and management of tuberculosis in young children, especially in settings with limited diagnostic infrastructure.

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