A method for the identification of COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry. (December 2021)

• Record Type: Journal Article
• Authors: Liangou, Aikaterini; Tasoglou, Antonios; Huber, Heinz J.; Wistrom, Christopher; Brody, Kevin; Menon, Prahlad G; Bebekoski, Thomas; Menschel, Kevin; Davidson-Fiedler, Marlise; DeMarco, Karl; Salphale, Harshad; Wistrom, Jonathan; Wistrom, Skyler; Lee, Richard J.
• Is Part Of: EClinicalMedicine. Volume 42(2021)
• Publication Date: 2021-12
• Subjects: Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2021.101207
• Languages: English
• ISSNs: 2589-5370

Abstract

Abstract: Background: COVID-19 has caused a worldwide pandemic, making the early detection of the virus crucial. We present an approach for the determination of COVID-19 infection based on breath analysis. Methods: A high sensitivity mass spectrometer was combined with artificial intelligence and used to develop a method for the identification of COVID-19 in human breath within seconds. A set of 1137 positive and negative subjects from different age groups, collected in two periods from two hospitals in the USA, from 26 August, 2020 until 15 September, 2020 and from 11 September, 2020 until 11 November, 2020, was used for the method development. The subjects exhaled in a Tedlar bag, and the exhaled breath samples were subsequently analyzed using a Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). The produced mass spectra were introduced to a series of machine learning models. 70% of the data was used for these sub-models' training and 30% was used for testing. Findings: A set of 340 samples, 95 positives and 245 negatives, was used for the testing. The combined models successfully predicted 77 out of the 95 samples as positives and 199 out of the 245 samples as negatives. The overall accuracy of the model was 81.2%. Since over 50% of the total positive samples belonged to the age group of over 55 years old, the performance of the model in this category was also separately evaluated on 339 subjects (170 negative and 169 positive). The model correctly identified 166 out of the 170 negatives and 164 out of the 169 positives. The model accuracy in this case was 97.3%. Interpretation: The results showed that this method for the identification of COVID-19 infection is a promising tool, which can give fast and accurate results. Funding: This study was funded by RJ Lee Group Inc. in Monroeville, PA.