dbGaP Study Accession: phs002747

NIH Institute/Center: NCATS

RADx Data Program: RADx-rad

DOI: 10.60773/n1eh-8t78

Release Date: 06/07/2022

Updated Date: 06/17/2023

Study Description: Recent studies, including this one, have suggested that breath may allow us to diagnose COVID-19 infection and even monitor its progress. As compared to immunological and genetic-based methods using sample media like blood, nasopharyngeal swab, and saliva, breath analysis is non-invasive, simple, safe, and inexpensive; it allows a nearly infinite amount of sample volume and can be used at the point-of-care for rapid detection. Fundamentally, breath also provides critical metabolomics information regarding how human body responds to virus infection and medical intervention (such as drug treatment and mechanical ventilation). The objectives of the SCENT project are: (1) to refine automated, portable, high-performance micro-gas chromatography (GC) device and related data analysis / biomarker identification algorithms for rapid (5-6 minutes), in-situ, and sensitive (down to ppt) breath analysis and (2) to conduct breath analysis on up to 760 patients, and identify and validate the COVID-19 biomarkers in breath. Thus, in coordination with the RADx-rad Data Coordination Center (DCC), this study completed the following specific aims. (1) Refine 5 automated micro-GC devices to achieve higher speed and better separation capability. This study constructed 5 new automated and portable one-dimensional micro-GC devices that required only ~6 minutes of assay time (improved from current 20 minutes) at the ppt level sensitivity (Sub-Aim 1a). Then the devices were upgraded to 2-dimensional micro-GC to significantly increase the separation capability (Sub-Aim 1b). In the meantime, the study optimized and automated existing data processing and biomarker identification algorithms and codes to streamline the workflow so that the GC device could automatically process and analyze the data without human intervention (Sub-Aim 1c). (2) Identify breath biomarkers that distinguish COVID-19 positive (symptomatic and asymptomatic) and negative patients. This study recruited a training cohort of 380 participants, including 190 COVID-19 positive patients (95 symptomatic and 95 asymptomatic) and 190 COVID-19 negative patients from two hospitals (Michigan Medicine – Ann Arbor and the Henry Ford Hospital – Detroit). This study conducted breath analysis using machine learning to identify VOC patterns that match each COVID-19 diagnostic status. (3) Validated the COVID-19 biomarkers using refined micro-GC devices. Using the refined 2-D micro-GC devices from Sub-Aim 1b, this study recruited a new validation cohort of 380 participants (190 COVID-19 positive patients and 190 COVID-19 negative patients) to validate the biomarkers identified in Aim 2. This study leveraged existing engineering, data science, clinical, regulatory, and commercialization resources throughout the project to hit milestones, ensuring a high likelihood of rapid patient impact. Upon completion of this work, this study had a portable micro-GC device and accompanying automated algorithms that detect and monitor COVID-19 status for people in a variety of clinical and community settings.

Principal Investigator: Fan, Xudong

Has Data Files: Yes

Study Domain: Medical Device/Tool Development; Virological Testing; Novel Biosensing and VOC

Data Collection Method: Breath Analysis Device / Airborne Detection Device

Keywords: Symptomatic Population; Asymptomatic Population; COVID-negative Patients; COVID-positive Patients; Metablomics; Acute Respiratory Distress Syndrome (ARDS)

Study Design: Case-Control

Multi-Center Study: TRUE

Study Sites: Henry Ford Hospital Centers - Detroit

Data Types: Individual Phenotype; Electronic Medical Records; Other

Data Types, Other: Breath - Volatile Organic Compounds (VOCs)

Study Start Date: 12/21/2020

Study End Date: 11/30/2022

Species: Human Data

Estimated Cohort Size: 760

Study Population Focus: N/A

Publication URL: https://pubmed.ncbi.nlm.nih.gov/36853606/

Acknowledgement Statement: This study was supported through funding, 4U18TR003812-02, for the National Center for Advancing Translational Sciences (NCATS) as part of the RADx-rad program. A special acknowledgment and gratitude to our collaborators and to the research participants that made this study possible. Approved users should acknowledge the provision of data access by dbGaP for accession phs002747.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): 4U18TR003812-02

Consent/Data Use Limitations: General Research Use