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Analysis of exhaled breath offers a noninvasive approach to understanding the metabolic state of the body. This study focuses on the efficacy of an innovative Electrochemical Hand-held Breathalyzer COVID-19 Sensing Technology (E.Co.Tech) for predicting COVID-19 infection, specifically in populations of never or former light smokers. The electrochemical nose technology used in this device aims to discriminate changes in exhaled nitric oxide levels, which are associated with COVID-19-linked respiratory inflammation. The methodology combines the device with a machine learning-based algorithm trained on a diverse data set of breath profiles from both infected and noninfected individuals. A cohort of 46 participants, consisting of never or former light smokers, was recruited. Each participant was tested using the E.Co.Tech prototype device and an iHealth COVID-19 antigen rapid test. The performance of the device was assessed by calculating sensitivity, specificity, positive predictive value, and negative predictive value (NPV). The results demonstrated high specificity (91.11%) and NPV (97.62%) for the device in this demographic group. This case study underscores the potential of E.Co.Tech as a valuable tool for point-of-care COVID-19 diagnosis, particularly in populations with unique smoking histories. The technology's high sensitivity and specificity, along with its rapid results, make it a promising candidate for deployment in resource-limited settings and situations where timely detection is crucial for effective public health management. Further large-scale clinical trials and real-world validations are necessary to establish the device's utility across diverse population groups.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487758 | PMC |
| http://dx.doi.org/10.1021/acsmeasuresciau.4c00020 | DOI Listing |
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