Temporal Fluctuations of Electronic Nose Signatures from breath in Patients with Asthma and Healthy Controls before and after challenge with Rhinovirus.
Temporal Fluctuations of Electronic Nose Signatures from breath in Patients with Asthma and Healthy Controls before and after challenge with Rhinovirus.
Sinha1,2,3, R. De Vries1, J. W. F. Dagelet1, A. H. Maitland-van der Zee,1 R. Lutter1,2,*, U. Frey3, P. J. Sterk1
1 Amsterdam UMC, University of Amsterdam, Department of Respiratory Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; 2 Amsterdam UMC, University of Amsterdam, Department of Experimental Immunology, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; 3 University of Basel, University Children’s Hospital (UKBB), Spitalstrasse 33, Postfach, 4031 Basel, Switzerland. *) presenting author
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Abstract:
Rationale: Point-of-care breath analysis is useful for diagnosing and phenotyping patients with chronic airway diseases [De Vries et al. ERJ, 2018]. Integration of electronic nose (eNose) to a spirometer (Spironose) at clinic has been shown to be effective for exhaled breath analysis [De Vries et al. JBR, 2015]. Fluctuations in time series of eNose signals from exhaled breath, in stable and unstable disease, might provide insights into underlying pathophysiological processes, which is especially relevant in asthma with episodic loss of control predominantly due to viral exposures. Aim: Comparing temporal fluctuations of eNose signatures in asthmatic and healthy subjects before and after Rhinovirus (RV) challenge. Methods: This was a prospective, observational, 3-months follow-up study (33+ visits) in 12 asthmatic and 12 healthy subjects. First 2-months was the stable phase followed by RV challenge and 1-month follow-up. Exhaled breathprints collected in duplicate consisted of signals from 7 different metal oxide semiconductor sensors (SpiroNose). Data analysis involved signal processing, ambient correction and conventional statistics and non-linear time-series analysis. Results: Variation (CV) and Non-linear measures (Fractal Dimension, Sample entropy and Time-lag) were significantly lower in healthy than asthmatic subjects (p<0.05, Sensors 1, 5 & 6). Phase Space Analysis of sensors 1, 5 were significantly higher in asthmatic subjects after RV challenge (lower stability and adaptive capacity) than in healthy subjects. Conclusion: Temporal fluctuation patterns of eNose signals differ between healthy and asthmatic subjects before and after RV challenge aiding in disease monitoring and prediction. Such variations of exhaled biomarkers provide their own physiological fingerprints.
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