A clinical study to detect COVID-19 infection from a non-invasive breath sample

Abstract Objectives Rapid diagnosis of coronavirus disease 2019 (COVID-19) is important to the control of SARS-CoV-2. The objective of this study was to assess the feasibility of diagnosing COVID-19 infection from a breath sample using a rapid, non-invasive point-of-care breath test that does not require off-site analysis. This could increase the accessibility of testing and reduce the discomfort of current swabbing techniques. Methods In this prospective observational study, samples of expired air from adults diagnosed with COVID-19 and controls were collected and analyzed with gas chromatography combined with ion mobility spectrometry (BreathSpec© GC-IMS, G.A.S mbH) and also with machine learning (ML) biomarker analysis (MLBA, Ancon Technologies Ltd.). Key findings A total of 330 participants, who tested negative or positive for COVID-19 through RT-PCR, were enrolled in the study. In an ML analysis of data collected, the MLBA algorithms enable distinction between COVID-19 and non-COVID-19 subjects with an accuracy of 94.1%. Conclusions This study indicates that patients with COVID-19 can be quickly identified at the point of use. The development and validation of this method may allow for a rapid, circa 10-min diagnosis of COVID-19 both now and in future seasons. It may also offer an alternative tool for the detection of other viral and microbial infections.