Hyperspectral Analysis of Sweat Metabolite Biometrics for Real-Time Detection of COVID-19

Sponsor

National Cancer Institute (NCI) (NIH)

Overall Status

Recruiting

CT.gov ID

NCT05044780

Collaborator

(none)

100

Enrollment

Location

12.1

Anticipated Duration (Months)

8.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Background:

The COVID-19 pandemic has challenged the health systems worldwide. Many tools have been developed in response to the pandemic, but there is no current way to quickly screen multiple people for the disease. Research has shown that people with COVID-19 have higher levels of some proteins involved in the immune response and inflammation. These proteins can be detected in sweat using a special camera. Researchers want to see if analysis of sweat from fingerprints could be used to detect COVID-19 infection in people.

Objective:

To test a new technology to detect COVID-19 infection based on an analysis of sweat from fingerprints.

Eligibility:

Adults ages 18 and older who tested positive or negative for COVID-19 within the last 7 days.

Design:

Participants will visit the NIH Clinical Center for one day within 7 days from COVID-19 testing. The visit will last for 3 to 4 hours.

Participants who show symptoms for COVID-19 with a positive test will give blood samples to correlate with the sweat markers. About 1/2 tablespoon of blood will be drawn.

For sweat markers, 10 fingers will be imaged by a camera using a touchless system. This will be repeated 3 times. It will take about 15 minutes. Participants will use the device. They will get instructions and watch a short video on how to use the device.

Condition or Disease	Intervention/Treatment	Phase
COVID-19

Detailed Description

Background

The Coronavirus Disease 19 (COVID19) pandemic has challenged healthcare systems worldwide. Massive testing, contact tracing and social distancing proved to be the most effective tools to fight the pandemic prior to the development of vaccines.

Despite the effort to develop rapid diagnostic testing, we still don t have an available large population screening modality. Analysis of sweat metabolites from hyperspectral images of fingertips has the potential to be a valid clinic strategy to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)infected individuals.

COVID19 has shown higher levels of inflammatory proteins like IL6, LDH, CRP, and d-dimer which have been implicated with severe COVID-19 induced pneumonitis and coagulopathy. These molecules can be detected as sweat metabolites and used as a biomarker for viral infection detection.

Objective

Identify a pattern classifier to distinguish between SARS-CoV-2 positive and SARS-CoV-2 negative human subjects by analysis of sweat metabolites from hyperspectral images of fingertips.

Eligibility

Individuals must all be >=18 years old

Must have standard of care molecular testing (either antigen or PCR) for SARS-CoV-2 within 7 days from study enrollment. Those individuals who tested positive will be enrolled in cohort 1 and those who tested negative will be enrolled in cohort 2

Study Design

This is an exploratory multisite study to evaluate the use of biometric analysis of sweat metabolites from hyperspectral images of fingertips to detect SARS-CoV-2 infection. Center for Cancer research (CCR), NCI will be the coordinating center.

All adult subjects that have available testing for SARS-CoV-2 completed within 7 days from the study enrollment are eligible for this study. The study will have two cohorts, cohort 1 (SARS-CoV-2 positive), and cohort 2 (SARS-CoV-2 negative). Fifty participants will be enrolled in each cohort to have hyperspectral imaging of the fingertips.

Every participant will have the right and left index fingers imaged by the camera with a touchless system. The imaging will be repeated three times. This imaging will take about 10 minutes.

The data obtained by the digital analysis will be compared to the result of the standard SARS-CoV-2 tests in use at the enrolling sites.

Study Design

Study Type:

Observational

Anticipated Enrollment :

100 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Hyperspectral Analysis of Sweat Metabolite Biometrics for Real-Time Detection of COVID-19

Anticipated Study Start Date :

Aug 30, 2022

Anticipated Primary Completion Date :

Sep 1, 2023

Anticipated Study Completion Date :

Sep 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
Cohort 1/SARS-CoV-2 positive Participants with molecular testing positive for SARS-CoV-2
Cohort 2/SARS-CoV-2 negative Participants with molecular testing negative for SARS-CoV-2

Outcome Measures

Primary Outcome Measures

Hyperspectal Analysis [One day]
Identify a pattern classifier to distinguish between SARS-CoV-2 positive (cohort 1) and SARS-CoV-2 negative (cohort 2) human subjects by hyperspectral analysis of sweat metabolites.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

INCLUSION CRITERIA:

Eligible subjects must meet the following inclusion criteria:

Age >=18 years.
Eligible for one of the following cohorts:
Cohort 1: Participants who sought medical attention for symptoms and tested positive for SARS-CoV-2 via standard of care molecular testing within 7 days of enrollment. Either antigen or PCR testing is acceptable.
Cohort 2: Participants must have a standard of care molecular testing negative for SARS-CoV-2 done within 7 days of enrollment. Either antigen or PCR testing is acceptable for enrollment.
Ability of subject or Legally Authorized Representative (LAR) or Durable Power of Attorney (DPA) to understand and the willingness to sign a written informed consent document

EXCLUSION CRITERIA:

Subjects with the following characteristics will be excluded from the study:

-Participants who have received remdesivir and/or dexamethasone for longer than 48 hours prior to hyperspectral imaging for the treatment of COVID19. Participants who have received up to 48 hours of treatment will be eligible.