<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04475562</url>
  </required_header>
  <id_info>
    <org_study_id>eNoseCOVID</org_study_id>
    <nct_id>NCT04475562</nct_id>
  </id_info>
  <brief_title>Can the Electronic Nose Smell COVID-19?</brief_title>
  <official_title>Can the Electronic Nose Smell COVID-19? A Proof-of-principle Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Maastricht University Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Maastricht University Medical Center</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Infection with SARS-CoV-2 causes Corona Virus Disease (COVID-19). The most standard&#xD;
      diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a&#xD;
      nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results&#xD;
      due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling&#xD;
      method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle&#xD;
      study investigates the possibility to train machine-learning classifiers with an electronic&#xD;
      nose (Aeonose) to differentiate between COVID-19 positive- and negative persons based on&#xD;
      volatile organic compounds (VOCs) analysis.&#xD;
&#xD;
      Methods: between April and June 2020, participants were invited for breath analysis when a&#xD;
      swab for RT-PCR was collected. If the RT-PCR resulted negative, presence of SARS-CoV-2&#xD;
      specific antibodies was checked to confirm the negative result. All participants breathed&#xD;
      through the Aeonose for five minutes. This device contains metal-oxide sensors that change in&#xD;
      conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input&#xD;
      data for machine-learning and used for pattern recognition. The result is a value between -1&#xD;
      and +1, indicating the infection probability.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">April 6, 2020</start_date>
  <completion_date type="Actual">July 1, 2020</completion_date>
  <primary_completion_date type="Actual">May 6, 2020</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>COVID 19 positive vs negative</measure>
    <time_frame>3 months</time_frame>
    <description>Ability of the eNose to distinguish COVID-19 positive from COVID-19 negative persons based on VOC patterns.</description>
  </primary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">219</enrollment>
  <condition>SARS-CoV Infection</condition>
  <condition>Covid19</condition>
  <arm_group>
    <arm_group_label>COVID-19 suspected</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>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.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Aeonose</intervention_name>
    <description>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.</description>
    <arm_group_label>COVID-19 suspected</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Participants of whom an oropharyngeal or nasopharyngeal swab was collected to perform&#xD;
             RT-PCR on.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Participants who were experiencing dyspnea or needed supplemental oxygen.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Maastricht University Medical Center</name>
      <address>
        <city>Maastricht</city>
        <zip>6229 HX</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Netherlands</country>
  </location_countries>
  <reference>
    <citation>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.</citation>
    <PMID>32091533</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>24421258</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>29909757</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>26056127</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>23956311</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>27310311</PMID>
  </reference>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 15, 2020</study_first_submitted>
  <study_first_submitted_qc>July 16, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 17, 2020</study_first_posted>
  <last_update_submitted>July 16, 2020</last_update_submitted>
  <last_update_submitted_qc>July 16, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 17, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Maastricht University Medical Center</investigator_affiliation>
    <investigator_full_name>Nicole Bouvy</investigator_full_name>
    <investigator_title>Prof. Dr. Nicole D. Bouvy</investigator_title>
  </responsible_party>
  <keyword>Electronic nose</keyword>
  <keyword>Volatile organic compounds</keyword>
  <keyword>Diagnosis</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
    <mesh_term>Severe Acute Respiratory Syndrome</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04475562
org study id: eNoseCOVID
nct id: NCT04475562
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
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.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: COVID 19 positive vs negative
time frame: 3 months
description: Ability of the eNose to distinguish COVID-19 positive from COVID-19 negative persons based on VOC patterns.
arm group label: COVID-19 suspected
arm group type: Other
description: 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.
intervention type: Device
intervention name: Aeonose
description: 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.
arm group label: COVID-19 suspected
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: Netherlands
citation: 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.
PMID: 32091533
citation: 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.
PMID: 24421258
citation: 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.
PMID: 29909757
citation: 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.
PMID: 26056127
citation: 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.
PMID: 23956311
citation: 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.
PMID: 27310311
responsible party type: Principal Investigator
investigator affiliation: Maastricht University Medical Center
investigator full name: Nicole Bouvy
investigator title: Prof. Dr. Nicole D. Bouvy
mesh term: COVID-19
mesh term: Severe Acute Respiratory Syndrome
sharing ipd: Yes