- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04475562
Can the Electronic Nose Smell COVID-19?
Can the Electronic Nose Smell COVID-19? A Proof-of-principle Study
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 investigates 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, 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.
Study Overview
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Maastricht, Netherlands, 6229 HX
- Maastricht University Medical Center
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Participants of whom an oropharyngeal or nasopharyngeal swab was collected to perform RT-PCR on.
Exclusion Criteria:
- Participants who were experiencing dyspnea or needed supplemental oxygen.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: DIAGNOSTIC
- Allocation: NA
- Interventional Model: SINGLE_GROUP
- Masking: NONE
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
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OTHER: COVID-19 suspected
Participants were recruited at the outpatient clinic for MUMC+ employees with COVID-19 symptoms or at the nursing unit where a SARS-CoV-2 patient was admitted.
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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.
A nose clip was placed on the nose of each participant to avoid entry of non-filtered air in the device.
Before measuring, the Aeonose was flushed with room air, guided through a carbon filter as well.
During each measurement, a video was displayed to distract the participant and to reduce the chance of hyperventilation.
Failed breath tests were excluded from analysis; the reason for failure was documented.
Four similar Aeonose devices were used for breath analysis.
A full-measurement procedure required sixteen minutes.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
COVID 19 positive vs negative
Time Frame: 3 months
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Ability of the eNose to distinguish COVID-19 positive from COVID-19 negative persons based on VOC patterns.
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3 months
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Collaborators and Investigators
Publications and helpful links
General Publications
- Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020 Apr 7;323(13):1239-1242. doi: 10.1001/jama.2020.2648. No abstract available.
- de Lacy Costello B, Amann A, Al-Kateb H, Flynn C, Filipiak W, Khalid T, Osborne D, Ratcliffe NM. A review of the volatiles from the healthy human body. J Breath Res. 2014 Mar;8(1):014001. doi: 10.1088/1752-7155/8/1/014001. Epub 2014 Jan 13.
- Schuermans VNE, Li Z, Jongen ACHM, Wu Z, Shi J, Ji J, Bouvy ND. Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients. Surg Innov. 2018 Oct;25(5):429-434. doi: 10.1177/1553350618781267. Epub 2018 Jun 18.
- Bikov A, Lazar Z, Horvath I. Established methodological issues in electronic nose research: how far are we from using these instruments in clinical settings of breath analysis? J Breath Res. 2015 Jun 9;9(3):034001. doi: 10.1088/1752-7155/9/3/034001.
- Bijland LR, Bomers MK, Smulders YM. Smelling the diagnosis: a review on the use of scent in diagnosing disease. Neth J Med. 2013 Jul-Aug;71(6):300-7.
- van Geffen WH, Bruins M, Kerstjens HA. Diagnosing viral and bacterial respiratory infections in acute COPD exacerbations by an electronic nose: a pilot study. J Breath Res. 2016 Jun 16;10(3):036001. doi: 10.1088/1752-7155/10/3/036001.
Study record dates
Study Major Dates
Study Start (ACTUAL)
Primary Completion (ACTUAL)
Study Completion (ACTUAL)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- eNoseCOVID
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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