Abstract
Background: The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection. Methods: This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted. Results: The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS. Conclusion: Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.
| Original language | English |
|---|---|
| Article number | 116309 |
| Journal | Diagnostic Microbiology and Infectious Disease |
| Volume | 109 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 2024 |
Keywords
- Artificial Intelligence Models
- Breath analysis
- COVID-19
- Pandemic
- RT-PCR
- Volatile organic compounds
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