Bax, C.

Bonetti, S.

Capelli, L.

Dellaca, Raffaele.

Di Marco, Fabiano

Novelli, Luca

Raimondi, Federico

Ratti, Christian

Robbiani, S.

Zannin, E.

English

Archivio istituzionale della ricerca - Politecnico di Milano

Breath Summit 2022    12|15 June 2022       Pisa - Italy   Title An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients Authors Presenting: Author Carmen Bax (1) Co-Authors: Stefano Robbiani (2), Emanuela Zannin (2), Laura Capelli (1), Christian Ratti (1), Simone Bonetti (3,4), Luca Novelli (3), Federico Raimondi (3), Fabiano Di Marco (3,4) and Raffaele L. Dellacà (2)  Affiliation (1) Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” (DCMC), 20133, Milano, Italy;  (2) Politecnico di Milano, TechRes Lab, Department of Electronics Information and Bioengineering (DEIB), 20133 Milano, Italy;  (3) Azienda Ospedaliera Socio Sanitaria Territoriale Papa Giovanni XXIII, Unit of Pneumology, 24127, Bergamo, Italy;  (4) Università   degli Studi di Milano, Department of Health Sciences, University of Milan, 20142 Milan, Italy  Email address of presenting author carmen.bax@polimi.it    Abstract  Max 2000 characters  Background: Nowadays, a key in the personalization of respiratory failure patients’ management is providing bedside diagnostic tools [1]. Electronic noses (EN) represent an emerging tool for this purpose. By analysing endogenous Volatile Organic Compounds (VOCs) in breath samples, EN can phenotype respiratory disorders and improve diagnosis [2]–[5]. As EN application in respiratory failure patients is challenging, this work proposes a novel apparatus for exhaled breath sampling [6], [7].  Methods: It uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen, prevent contamination of exhaled gas from ambient VOCs and minimise the respiratory load imposed on patients. A commercial EN with Breath Summit 2022    12|15 June 2022       Pisa - Italy   custom MOS sensors was used to assess breath odour fingerprints. To collect data on tolerability and for a preliminary assessment of sensitivity and specificity, a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls was carried out. Boruta algorithm [8], was used to discriminate the most significant features to identify respiratory failure patients and controls breath odour fingerprints. Then, a classification model based on Support Vector Machine (SVM)[9] was implemented on selected features.  Results: All the patients well tolerated the proposed sampling system. The SVM model differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve), and a sensitivity and specificity of 0.92 and 0.68, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p<0.001 and 0.046, respectively).  Conclusions: The developed breath sampling apparatus proved suitable for respiratory failure patients, and results achieved within the feasibility study highlighted the potentialities of EN VOCs analysis for assessing lung disease severity and aetiology.  References [1] F. of I. R. Societies., The Global Impact of Respiratory Disease- 2a ed. 2017. [2] S. Dragonieri, G. Pennazza, P. Carratu, and O. Resta, “Electronic Nose Technology in Respiratory Diseases,” Lung, vol. 195, no. 2, pp. 157–165, 2017, doi: 10.1007/s00408-017-9987-3. [3] G. Rocco et al., Breathprinting and Early Diagnosis of Lung Cancer, vol. 13, no. 7. International Association for the Study of Lung Cancer, 2018. [4] B. Buszewski, D. Grzywinski, T. Ligor, T. Stacewicz, Z. Bielecki, and J. Wojtas, “Detection of volatile organic compounds as biomarkers in breath analysis by different analytical techniques,” Bioanalysis, vol. 5, no. 18, pp. 2287–2306, 2013, doi: 10.4155/bio.13.183. [5] G. Song et al., “Quantitative breath analysis of volatile organic compounds of lung cancer patients,” Lung Cancer, vol. 67, no. 2, pp. 227–231, 2010, doi: 10.1016/j.lungcan.2009.03.029. [6] J. Kwak and G. Preti, “Volatile Disease Biomarkers in Breath: A Critique,” Curr. Pharm. Biotechnol., vol. 12, no. 7, pp. 1067–1074, 2011, doi: 10.2174/138920111795909050. [7] N. Fens, M. P. van der Schee, P. Brinkman, and P. J. Sterk, “Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions,” Clin. Exp. Allergy, vol. 43, no. 7, pp. 705–715, 2013, doi: 10.1111/cea.12052. [8] M. B. Kursa, A. Jankowski, and W. R. Rudnicki, “Boruta - A system for feature selection,” Fundam. Informaticae, vol. 101, no. 4, 2010, doi: 10.3233/FI-2010-288. [9] M. Sun, “Support Vector Machine Models for Classification,” in Encyclopedia of Business Analytics and Optimization, IGI Global, 2014, pp. 2395–2409.  

An experimental apparatus for e-nose breath analysis in respiratory failure patients

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An experimental apparatus for e-nose breath analysis in respiratory failure patients

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