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Ruling out SARS-CoV-2 infection using exhaled breath analysis by electronic nose in a public health setting
Rianne de Vries; Rene M. Vigeveno; Simone Mulder; Niloufar Farzan; Demi R. Vintges; Jelle J. Goeman; Sylvia Bruisten; Bianca van der Corput; J. J. Miranda Geelhoed; Leo G. Visser; Mariken van der Lubben; Peter J. Sterk; Johannes C.C.M. in 't Veen; Geert H. Groeneveld.
  • Rianne de Vries; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands and Breathomix BV, Leiden, The Netherlands
  • Rene M. Vigeveno; Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
  • Simone Mulder; Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands.
  • Niloufar Farzan; Breathomix BV, Leiden, The Netherlands.
  • Demi R. Vintges; Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands.
  • Jelle J. Goeman; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
  • Sylvia Bruisten; Department of Infectious Diseases, Public Health Laboratory, Municipal Health Services (GGD) Amsterdam, The Netherlands.
  • Bianca van der Corput; Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands.
  • J. J. Miranda Geelhoed; Department of Respiratory Medicine, Leiden University Medical Center, Leiden, The Netherlands.
  • Leo G. Visser; Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands.
  • Mariken van der Lubben; Department of Infectious Diseases, Public Health Laboratory, Municipal Health Services (GGD) Amsterdam, The Netherlands.
  • Peter J. Sterk; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
  • Johannes C.C.M. in 't Veen; Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands and Department of Respiratory Medicine, Erasmus MC, Rotterdam, T
  • Geert H. Groeneveld; Department of Infectious Diseases and Acute Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands.
Preprint in English | medRxiv | ID: ppmedrxiv-21251712
ABSTRACT
BackgroundRapid and accurate detection of SARS-CoV-2 infected individuals is crucial for taking timely measures and minimizing the risk of further SARS-CoV-2 spread. We aimed to assess the accuracy of exhaled breath analysis by electronic nose (eNose) for the discrimination between individuals with and without a SARS-CoV-2 infection. MethodsThis was a prospective real-world study of individuals presenting to public test facility for SARS-CoV-2 detection by molecular amplification tests (TMA or RT-PCR). After sampling of a combined throat/nasopharyngeal swab, breath profiles were obtained using a cloud-connected eNose. Data-analysis involved advanced signal processing and statistics based on independent t-tests followed by linear discriminant and ROC analysis. Data from the training set were tested in a validation, a replication and an asymptomatic set. FindingsFor the analysis 4510 individuals were available. In the training set (35 individuals with; 869 without SARS-CoV-2), the eNose sensors were combined into a composite biomarker with a ROC-AUC of 0.947 (CI0.928-0.967). These results were confirmed in the validation set (0.957; CI0.942-0.971, n=904) and externally validated in the replication set (0.937; CI0.926-0.947, n=1948) and the asymptomatic set (0.909; CI0.879-0.938, n=754). Selecting a cut-off value of 0.30 in the training set resulted in a sensitivity/specificity of 100/78, >99/84, 98/82% in the validation, replication and asymptomatic set, respectively. InterpretationeNose represents a quick and non-invasive method to reliably rule out SARS-CoV-2 infection in public health test facilities and can be used as a screening test to define who needs an additional confirmation test. FundingMinistry of Health, Welfare and Sport Research in contextO_ST_ABSEvidence before this studyC_ST_ABSElectronic nose technology is an emerging diagnostic tool for diagnosis and phenotyping of a wide variety of diseases, including inflammatory respiratory diseases, lung cancer, and infections. As of Feb 13, 2021, our search of PubMed using keywords "COVID-19" OR "SARS-CoV-2" AND "eNose" OR "electronic nose" OR "exhaled breath analysis" yielded 4 articles (1-4) that have assessed test characteristics of electronic nose to diagnose COVID-19. In these small studies the obtained signals using sensor-based technologies, two-dimensional gas chromatography and time-of-flight mass spectrometry, or proton transfer reaction time-of-flight mass spectrometry, provided adequate discrimination between patients with and without COVID-19. Added value of this studyWe prospectively studied the accuracy of exhaled breath analysis by electronic nose (eNose) to diagnose or rule out a SARS-CoV-2 infection in individuals with and without symptoms presenting to a public test facility. In the training set with 904 individuals, the eNose sensors were combined into a composite biomarker with a ROC-AUC of 0.948. In three independent validation cohorts of 3606 individuals in total, eNose was able to reliably rule out SARS-CoV-2 infection in 70-75% of individuals, with a sensitivity ranging between 98-100%, and a specificity between 78-84%. No association was found between cycle thresholds values, as semi-quantitative measure of viral load, and eNose variables. Implications of all the available evidenceThe available findings, including those from our study, support the use of eNose technology to distinguish between individuals with and without a SARS-CoV-2 infection with high accuracy. Exhaled breath analysis by eNose represents a quick and non-invasive method to reliably rule out a SARS-CoV-2 infection in public health test facilities. The results can be made available within seconds and can therefore be used as screening instrument. The eNose can reliably rule out a SARS-CoV-2 infection, eliminating the need for additional time-consuming, stressful, and expensive diagnostic tests in the majority of individuals.
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document Type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document Type: Preprint