Passive Detection- SARS-CoV-2 (COVID-19) A&M Breathalyzer (PROTECT Kiosk) for Operational Medicine — COVID-19  

COVID-19 Clinical Trial

Official title: A Prospective, Non-interventional, Observational Clinical Validation Study to Analytically Validate the A&M Breathalyzer PROTECT Kiosk Breath Capture System.

Status Recruiting

Clinical Trial Summary

The primary objective of this effort will be to optimize and operationalize innovative passive surveillance systems and in parallel, the effort will identify, evaluate, and transition groundbreaking new technologies in diagnostics for operationalization.

To meet the objective and execute the deliverables for this program of effort, the A&M Breathalyzer PROTECT Kiosk will be tested, modified and validated at Brooke Army Medical Center (BAMC). The collaborative efforts between the PI, Dr. Michael Morris at BAMC and Co-Investigator Dr. Tony Yuan at USU- Center for Biotechnology (4D Bio3) will assess the passive detection technology and provide a capability survey of use-case scenarios for different operational settings.

Goals:

1. Optimization and operationalize the A&M Breathalyzer PROTECT Kiosk, portable mass spectrometer (MS) Detector for Deployment in Military Operational Medicine Environments. The Breathalyzer will be deployed to BAMC to test its detection capabilities of COVID-19 among symptomatic and asymptomatic COVID-19 carrier vs. those not infected compared to gold standard RT-PCR.

2. Evaluate the passive sensing, breath capture system, built within the A&M Breathalyzer PROTECT Kiosk. The conversion of the active breath capture system, currently requires a straw that the subject breaths into, where then a series of sensors built in the Breathalyzer would automatically sample the exhaled breath within proximity for recent COVID-19 exposure. This task would conclude with a set of sensors and sensor inputs that would be analyzed by the Atomic AI platform built in the device. Field testing at BAMC is planned to determine the level of detection and discrimination for sensor combinations to SARS-CoV2 components and biomarkers detected. This testing would update the Atomic AI algorithm, within the device, to understand the accuracy of positive detection and the resulting sensitivities.

Clinical Trial Description

Emerging acute respiratory diseases (ARDs) pose a significant threat for the US military, especially among those in training environments where crowded living conditions and demanding multi-factorial stresses exacerbate infection exposure and suppress immunity, respectively. Consequently, ARD rates are routinely reported higher in recruits than older military personnel, which have a detrimental effect on operational readiness. Although significant steps, such as surveillance and vaccine programs, have been taken to minimize the impact that ARDs have on military recruits and newly mobilized troops, hospitalizations among recruits still exceeds that of comparable civilian population in the United States by at least 3- to 4-folds, accounting for almost 30% of all infectious disease associated hospitalizations. In 2018, respiratory infections like respiratory syncytial virus (RSV), accounted for an estimated 50,000 medical encounters affecting about ~35,000 recruits that resulted in 1,000 hospital bed days leading to significant loss in training time and cost. In addition to annual respiratory infections, the on-going COVID-19 numbers rising again, continues to threaten to further degrade operational readiness. Thus, inexpensive, rapid, and more reliable diagnostics are continually required to better treat and prevent ARDs to preserve military readiness and decrease disability adjusted life years.

Current CLIA laboratory diagnostic procedures, such Enzyme Linked Immunosorbent Assay (ELISA), Reverse Transcriptase Polymerase Chain Reaction (RT-PCR), and bacterial cultures, are costly, time-consuming, and operator sensitive. It has become apparent that during the COVID-19 pandemic, these approaches were and continue to be insufficient in meeting diagnostic needs as they are difficult to scale-up and lack logistical flexibility. Furthermore, due to the invasive nature of active clinical sampling, there is a critical need for accurate and rapid passive surveillance as to screen for SARS-CoV-19 as well as other hazardous chemical and biological agents. To address this capability gap, the current project will: 1) modify and operationalize existing innovative passive surveillance systems that can be deployed in the near-term; 2) leverage revolutionary technologies that will enhance current diagnostic systems to meet mid- and far-term gaps in CBRNE gaps. ;

Related Conditions & MeSH terms

• Acute Lung Injury
• COVID-19
• COVID-19 Pneumonia
• Respiratory Distress Syndrome
• Respiratory Distress Syndrome, Newborn
• Respiratory Tract Infections

• NCT number: NCT06402318
• Study type: Observational
• Source: The Geneva Foundation
• Contact: Tony Yuan, PhD
• Phone: 214-292-0508
• Email: tony.yuan@usuhs.edu
• Status: Recruiting
• Start date: December 27, 2023
• Completion date: January 31, 2025