Diagnostic value of SIFT-MS and GC-ToF-MS analysis of exhaled breath for asthma diagnosis in school-aged children

<bold>Background</bold> Asthma is a common chronic condition in childhood with a high rate of misdiagnosis. Development of non-invasive, reliable tests is crucial to enhance the current diagnostic approaches for asthma detection. Volatile organic compounds (VOC) in exhaled breath are promising potential biomarkers for asthma. <bold>Aim</bold> Comparison of the diagnostic accuracy of Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) and Gas Chromatography-Time of Flight-Mass Spectrometry (GC-ToF-MS) for distinguishing asthmatic from healthy school-aged children. <bold>Methods</bold> A cross-sectional study was performed in asthmatic and healthy school-aged children. Breath samples were analysed using SIFT-MS and GC-ToF-MS. Principal component analysis and unsupervised random forest models were used for exploratory data analysis. Classification models were built to evaluate the discriminatory power of both techniques. <bold>Results</bold> 51 asthmatic and 68 healthy school-aged children were included. The classification model built using SIFT-MS data resulted in an Area Under the Receiver Operating Characteristic (AUROC) curve value equal to 0.85 and in a sensitivity and specificity of 72.5% and 80.9%, respectively. Twenty-nine m/z ratios were identified showing the highest discrimination power. The classification model built using GC-ToF-MS data resulted in an AUROC curve value equal to 0.89 and in a sensitivity and specificity of 92.2% and 65.5%. Twenty-one discriminative variables were identified as those exhibiting the highest discrimination power. <bold>Conclusion</bold> SIFT-MS can be a reliable, user-friendly alternative to GC-ToF-MS, possibly paving the way for clinical applications of breath-based diagnosis in paediatric asthma.