Wintjens, 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.
Applying the electronic nose for pre-operative SARS-CoV-2 screening
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Medientyp:
E-Artikel
Titel:
Applying the electronic nose for pre-operative SARS-CoV-2 screening
Beteiligte:
Wintjens, 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.
Erschienen:
Springer Science and Business Media LLC, 2021
Erschienen in:Surgical Endoscopy
Sprache:
Englisch
DOI:
10.1007/s00464-020-08169-0
ISSN:
1432-2218;
0930-2794
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>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.</jats:p>
</jats:sec>