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Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants
Ruchi Sharma; Wenzhe Zang; Ali Tabartehfarahani; Andres Lam; Xiaheng Huang; Anjali D. Sivakumar; Chandrakalavathi Thota; Shuo Yang; Robert P. Dickson; Michael W. Sjoding; Erin Bisco; Carmen Colmenero Mahmood; Kristen Machado Diaz; Nicholas Sautter; Sardar Ansari; Kevin R. Ward; Xudong Fan.
Affiliation
  • Ruchi Sharma; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Wenzhe Zang; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Ali Tabartehfarahani; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Andres Lam; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Xiaheng Huang; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Anjali D. Sivakumar; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Chandrakalavathi Thota; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Shuo Yang; University of Michigan Ann Arbor, Department of Biomedical Engineering
  • Robert P. Dickson; University of Michigan Ann Arbor, Department of Internal Medicine, Division of Pulmonary Critical Care Medicine
  • Michael W. Sjoding; University of Michigan Ann Arbor, Department of Internal Medicine, Division of Pulmonary Critical Care Medicine
  • Erin Bisco; University of Michigan Ann Arbor, Department of Emergency Medicine
  • Carmen Colmenero Mahmood; University of Michigan Ann Arbor, Department of Emergency Medicine
  • Kristen Machado Diaz; University of Michigan Ann Arbor, Department of Emergency Medicine
  • Nicholas Sautter; University of Michigan Ann Arbor
  • Sardar Ansari; University of Michigan Ann Arbor, Department of Emergency Medicine
  • Kevin R. Ward; University of Michigan Ann Arbor, Department of Emergency Medicine
  • Xudong Fan; University of Michigan Ann Arbor, Department of Biomedical Engineering
Preprint in English | medRxiv | ID: ppmedrxiv-22279649
ABSTRACT
ImportanceBreath analysis has been explored as a non-invasive means to detect COVID-19. However, the impact of the emerging variants such as Omicron on the exhaled breath profile and hence the accuracy of breath analysis is unknown. ObjectiveTo evaluate the diagnostic accuracies of breath analysis on detecting COVID-19 patients in periods where Delta and Omicron were most prevalent. Design, Setting, and ParticipantsA convenience cohort of patients testing positive and negative for COVID-19 using reverse transcriptase polymerase chain reaction (RT-PCR) were studied and included 167 COVID and non-COVID patients recruited between April 2021 and May 2022, which covers the period when Delta (and other variants prior to Delta) was the dominant variant (April - December 2021) and when Omicron was the dominant variant (January - May 2022). The breath from those patients were collected and analyzed for volatile organic compounds (VOCs) with a newly developed portable gas chromatography-based breath analyzer. Diagnostic patterns and algorithms were developed. ResultsA total of 205 breath samples were analyzed from 167 COVID and non-COVID patients. The RT-PCR was conducted within 18 hours of the breath analysis to confirm the COVID status of the patients. Among 94 COVID positive samples, 41 samples were collected from the patients in 2021 who were assumed to be infected by the Delta variant (or other variants occurring in 2021) and 53 samples from the patients in 2022 who were assumed to be infected by the Omicron variant (BA.1 and BA.2). Breath analysis using a set of 4 VOC biomarkers was able to distinguish between COVID (Delta and other variants in 2021) and non-COVID with an overall accuracy of 94.7%. However, the accuracy dropped significantly to 82.1% when the same set of biomarkers were applied to the Omicron variant with and 21 out of 53 COVID positive being misidentified. A new set of 4 VOC biomarkers were found to distinguish the Omicron variant and non-COVID, which yielded an overall accuracy of 90.9%. Breath analysis was also found to be able to distinguish between COVID (for all the variants occurring between April 2021 and May 2022) and non-COVID with an overall accuracy of 90.2%, and between the Omicron variant and the earlier variants (Delta and other variants occurring in 2021) with an overall accuracy of 91.5%. Conclusions and RelevanceBreath analysis of VOCs using point of care gas chromatography may be a promising diagnostic modality for detection of COVID and similar diseases that result in VOC production. However, similar to other diagnostic modalities such as rapid antigen testing, challenges are posed by the dynamic emergence of viral variants. The results of this study warrant additional investment and evaluation on how to overcome these challenges and to exploit breath analysis to improve the diagnosis and care of patients. Key PointsO_ST_ABSQuestionC_ST_ABSCan volatile organic compounds (VOCs) in exhaled breath provide diagnostic information on COVID-19? Will variants such as Omicron B.1.1.529 and others affect the accuracy in breath analysis? FindingsA set of 4 VOC biomarkers were found to distinguish between Delta (and the variants occurring in 2021) from non-COVID. The Omicron variant (occurring in 2022) significantly affects VOC profiles requiring the search for a new set of VOC biomarkers to distinguish between Omicron and non-COVID. MeaninThese findings demonstrate the ability of breath analysis to distinguish between COVID and non-COVID, but also reveal the significant difference in the exhaled breath profile between COVID-19 patients during the period when Delta was most prevalent and when Omicron was most prevalent.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Diagnostic study / Experimental_studies / Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Diagnostic study / Experimental_studies / Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint
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