Ruling out SARS-CoV-2 infection using exhaled breath analysis by electronic nose in a public health setting
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Background
Rapid 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.
Methods
This 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 .
Findings
For 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 (CI:0.928-0.967). These results were confirmed in the validation set (0.957; CI:0.942-0.971, n=904) and externally validated in the replication set (0.937; CI:0.926-0.947, n=1948) and the asymptomatic set (0.909; CI:0.879-0.938, 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.
Interpretation
eNose 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.
Funding
Ministry of Health, Welfare and Sport
Research in context
Evidence before this study
Electronic 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 study
We 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 evidence
The 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.
Article activity feed
-
SciScore for 10.1101/2021.02.14.21251712: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study protocol was approved by the Medical Ethics Committee of Leiden The Hague Delft (P20.033) and all participants provided written informed consent.
Consent: The study protocol was approved by the Medical Ethics Committee of Leiden The Hague Delft (P20.033) and all participants provided written informed consent.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources SPSS (IBM, version 23) was used for statistical analysis. SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect …
SciScore for 10.1101/2021.02.14.21251712: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study protocol was approved by the Medical Ethics Committee of Leiden The Hague Delft (P20.033) and all participants provided written informed consent.
Consent: The study protocol was approved by the Medical Ethics Committee of Leiden The Hague Delft (P20.033) and all participants provided written informed consent.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources SPSS (IBM, version 23) was used for statistical analysis. SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One of the limitations of the study could be that a SARS-CoV-2 infection was merely based on the results of molecular amplification tests. This could explain some of the false positives found by the eNose as there is concern about false negative RT-PCR results for detecting SARS-CoV-2 (33). In our validation set, including follow-up (≤ 7 days) TMA or RT-PCR results to our reference standard, we were able to add some initial false negatives to the positive group, only partially mitigating the suboptimal sensitivity of the initial SARS-CoV-2 RNA detection test. During this study in the Netherlands, very few other commonly known viruses were circulating, besides rhinovirus. Due to the real-life setting and timing of the study, we cannot yet provide an answer about the accuracy of the eNose for distinguishing SARS-CoV-2 from other respiratory viruses. Given that these viruses harbor unique viral proteins, different receptor preferences, and distinct interactions with hosts on a cellular level, specific VOC patterns could possibly be identified for various viral respiratory pathogens. Finally, the present population mostly comprised young ambulant adults that visit public test facilities but that may not be representative of older and in-hospital populations. In conclusion, eNose technology can 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 ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
