| Title: | Applying the electronic nose for pre-operative SARS-CoV-2 screening |
| Author(s): | Wintjens A; Hintzen KFH; Engelen SME; Lubbers T; Savelkoul PHM; Wesseling G; van der Palen JAM; Bouvy ND; |
| Address: | "NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands. Department of Surgery, Maastricht University Medical Center, PO Box 5800, 6202 AZ, Maastricht, The Netherlands. Department of Medical Microbiology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands. Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, The Netherlands. Department of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, The Netherlands. Department of Epidemiology, Medisch Spectrum Twente, Enschede, The Netherlands. Department of Surgery, Maastricht University Medical Center, PO Box 5800, 6202 AZ, Maastricht, The Netherlands. n.bouvy@mumc.nl" |
| DOI: | 10.1007/s00464-020-08169-0 |
| ISSN/ISBN: | 1432-2218 (Electronic) 0930-2794 (Print) 0930-2794 (Linking) |
| Abstract: | "BACKGROUND: Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. METHODS: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability. RESULTS: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. CONCLUSIONS: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery" |
| Keywords: | *covid-19 Electronic Nose Humans Mass Screening Predictive Value of Tests *SARS-CoV-2 Covid-19 Exhaled air Innovative diagnostics Volatile organic compounds; |
| Notes: | "MedlineWintjens, Anne G W E Hintzen, Kim F H Engelen, Sanne M E Lubbers, Tim Savelkoul, Paul H M Wesseling, Geertjan van der Palen, Job A M Bouvy, Nicole D eng Research Support, Non-U.S. Gov't Germany 2020/12/04 Surg Endosc. 2021 Dec; 35(12):6671-6678. doi: 10.1007/s00464-020-08169-0. Epub 2020 Dec 2" |
