Background 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.

Aim 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.

Methods 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.

Results 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.

Conclusion 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.