Diagnosis of COVID-19 with enhanced real-time mass spectrometry breath analysis

Abstract

Introduction: Although rapid screening for and diagnosis of COVID-19 are still urgently needed, standard testing methods are long and costly. Real-time mass spectrometry (MS) breath analysis is a non-invasive, point-of-care technique based on the detection of volatile organic compounds which can be a tool of interest. Aims and objectives: To determine whether artificial-intelligence-enhanced real-time MS breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19. Methods: In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight MS to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata. Results: We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After processing breath analysis data and further enhancing the machine learning model by adding patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants (sensitivity: 98%, specificity: 74%, negative predictive value: 98%, positive predictive value: 72%, area under the ROC curve: 0.961). The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by the COVID-19 vaccination status. Conclusions: Real-time, non-invasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.