Moniek van Aarle  at BBCon 2023

Exhaled Breath Analysis for Asthma Detection in Children: Leveraging and comparing SIFT-MS and GC-ToF-MS


00:00 ‘Exhaled Breath Analysis for Asthma Detection in Children: Leveraging and comparing SIFT-MS and GC-ToF-MS’
19:38 Question and Answer Session


Talk Abstract:
Asthma is a common chronic condition in childhood, with a high misdiagnosis rate. Current diagnostic algorithms for asthma are complex, and there is a need for non-invasive, reliable tests. Volatile organic compounds (VOCs) in exhaled breath have shown promise as potential biomarkers for asthma. However, the lack of a standardized breath test hinders its clinical implementation. This study aims at comparing the diagnostic accuracy of two breath analysis techniques, ‘Selected Ion Flow Tube Mass Spectrometry’ (SIFT-MS) and ‘Gas Chromatography Time of Flight Mass Spectrometry’ (GC-ToF-MS), for distinguishing between healthy and doctor-diagnosed asthmatic school-aged children.
The analytical observational cross-sectional study presented here includes 51 asthmatic school-aged children and 68 healthy controls. Breath samples were collected using a tailored and child-friendly sampling system. SIFT-MS and GC-ToF-MS were used to analyse the exhaled breath samples. Principal component analysis (PCA) and unsupervised random forest models were employed for exploratory data analysis. Classification models were built to evaluate the discrimination power of different VOCs detected by both techniques. The analysis of exhaled breath samples using SIFT-MS and GC-ToF-MS revealed a diverse set of volatile organic compounds (VOCs). While the PCA scores plots did not show clear groupings, the classification models based on discriminatory VOCs exhibited promising sensitivity and specificity values. The build classification model for SIFT-MS identified 47 discriminatory variables and achieved a sensitivity and specificity of both 70%. The GC-ToF-MS analysis identified 17 discriminatory variables and demonstrated a sensitivity and specificity of 76% and 70%, respectively. The combination of both techniques might improve the diagnostic model even further. Our study highlighted the potential of SIFT-MS as a user-friendly and reliable breath diagnostic tool for paediatric asthma. The discriminatory VOCs identified by SIFT-MS and its comparable diagnostic accuracy to GC-ToF-MS underscore its feasibility for real-world clinical implementation. This brings us closer to developing a quick and accessible breath test for asthma diagnosis in children, enabling the utilisation of precision medicine in asthma management. We acknowledge, though, the potential influence of inhaled corticosteroid (ICS) medication on breath VOC profiles of asthmatic participants. To address this limitation, future studies should specifically explore the effect of ICS on VOC patterns to ensure accurate diagnostic performance. In conclusion, SIFT-MS holds promise as an effective and user-friendly alternative to GC-ToF-MS, advancing breath diagnosis towards practical clinical application in paediatric asthma.
Authors: Kienhorst S (1)* & van Aarle MHD (1)* Vitale R (2) Smolinska A (3)# and Dompeling E (1)# *shared first author #shared last author


Speaker Biography:

Meet Drs. Sophie Kienhorst and Drs. Moniek van Aarle, esteemed professionals at the paediatric-pulmonology department of MosaKids Children’s Hospital. They are passionate researchers dedicated to advancing children’s health. Currently, they are leading the ADEM2 study alongside a team of clinicians and researchers. This study, a multi-center Randomized Controlled Trial (RCT) and cohort study, focuses on diagnosing asthma in preschool children based on a breath test.

In their research, they employ cutting-edge techniques such as GC-ToF-MS and SIFT-MS in full scan mode. Their work involves optimizing sampling and analysis methods, contributing significantly to the field of paediatric pulmonology.

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