dbGaP Study Accession: phs002563
NIH Institute/Center: NCATS
RADx Data Program: RADx-rad
DOI: 10.60773/gr4a-3m47
Release Date: 06/07/2022
Study Description: The COVID-19 pandemic has caused unprecedented societal suffering and economic disruption. Although current COVID-19 diagnostic testing technologies are critical for curbing the spread of the virus and preventing future outbreaks, they are not practical for field use. Current diagnostic tests are cumbersome to perform because they use aqueous solutions, require multiple steps, and hours-to-days to obtain results. Therefore, there is an urgent need to develop a diagnostic approach that is non-invasive, portable, and can rapidly provide test results. The overall goal of the project was to develop a mobile breath analysis technology for rapid screening for COVID-19 using a handheld breath collection tool and a portable GC with a photoionization detector (PID). The handheld tool was a closed system for trapping target volatile organic compounds (VOCs) on a microfabricated chip. The captured VOCs were eluted with a solvent and then analyzed using a portable GC-PID instrument. Artificial intelligence (AI) and machine learning algorithms were applied to recognize the VOC pattern that correlates with COVID-19 infection. The central innovation was the microfabricated chip that captured VOCs in exhaled breath and thus served as a preconcentrator, which enabled analysis of the captured VOCs by the portable GC-PID. The hypothesis was that VOC metabolome in exhaled breath was directly related to the body's reaction to tSARS-CoV-2 infection, and the changes of VOC profile in exhaled breath relative to healthy controls were used to detect both symptomatic and asymptomatic COVID-19 patients. The University of Louisville was uniquely suited to rapidly transition the microchip technology to field use because of the PI and Co-PI's experience in breath analysis and translational research, and the project team's experience in virology, infectious diseases, biostatistics, and artificial intelligence as well as the state-of-the-art facilities that include a MicroNano Technology Center, Biosafety Level 3 Regional Biocontainment Lab, and an NIH-funded REACH program.
Updated Date: 01/18/2024
Principal Investigator: Fu, Xiao-An
Has Data Files: No
Study Domain: Artificial Intelligence or Machine Learning; Medical Device or Tool Development; Novel Biosensing or VOC; Rapid Diagnostic Test (RDT); Virological Testing
Data Collection Method: Breath Analysis or Airborne Detection Device
Keywords: Bioengineering; Coronaviruses Diagnostics and Prognostics; MicroNano Technology; Networking and Information Technology Research and Development (NITRD); Photoionization Detector; Prevention
Study Design: Longitudinal Cohort
Multi-Center Study: No
Data Types: Chemosensor; Immunological
Study Start Date: 12/21/2020
Study End Date: 05/31/2022
Species: Human Data
Estimated Cohort Size: 200
Study Population Focus: N/A
Publication URL: https://pubmed.ncbi.nlm.nih.gov/34304585/; https://pubmed.ncbi.nlm.nih.gov/36815760/; https://pubmed.ncbi.nlm.nih.gov/37421280/
Acknowledgement Statement: This study was supported through funding, 4U18TR003787-02, for the National Center for Advancing Translational Sciences (NCATS) as part of the RADx-rad program. The team of Breath Analysis for Detection of COVID-19 at the University of Louisville gratefully acknowledge the support of this project the national center for advancing translational sciences through the grant number. The PI is especially grateful for the generous support of the UHPLC-MS instrument by the LC-MS group from Thermo-Fisher Scientific. Approved users should acknowledge the provision of data access by dbGaP for accession phs002563.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.
Funding Opportunity Announcement (FOA) Number: RFA-OD-20-017
NIH Grant or Contract Number(s): 4U18TR003787-02
Consent/Data Use Limitations: General Research Use