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AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation (AIDI)

Primary Purpose

Sars-CoV-2 Infection

Status
Withdrawn
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
Monitoring of vital signs
Sponsored by
AIDAR Health, Inc.
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Sars-CoV-2 Infection

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

  • Male or female 18 years of age or older
  • Unvaccinated individuals, or individuals who have received only 1 dose of an mRNA vaccine
  • Individuals who have received a positive SARS-CoV-2 result within 24-48 hours (lab-based PCR or antigen test)
  • Willing and able to provide informed consent
  • Ability to read, write, and comprehend English
  • Have no functional limitation that would impede the use of the MouthLab device
  • Willing to provide access to health information via electronic health records (EHR)

Exclusion Criteria:

  • Currently receiving hospice care
  • Have a left ventricular assist device
  • Left-sided hemiplegia or any other motor deficits that may restrict the use of the device.
  • individuals with cognitive deficits that impede their ability to comprehend and give informed consent.
  • Individuals who are enrolled in any other investigational research studies of SARS-CoV2 or COVID-19
  • Individuals who are treated with monoclonal antibody therapy prior to diagnosis
  • Individuals who are admitted to a hospital or acute care facility at the time of diagnosis
  • Individuals with pacemakers or implanted cardio-defibrillators (ICDs)
  • History of hemoptysis, pneumothorax, thoracic or abdominal aneurysm, pulmonary embolism, or stroke
  • History of unstable cardiovascular status, including recent myocardial infarction (MI within 30 days), unstable angina, or uncontrolled hypertension Color blindness
  • Chest, abdominal or eye surgery within the preceding 14 days
  • Any condition that in the judgment of the investigators would interfere with the subject's ability to provide informed consent, comply with study instructions, place the subject at increased risk, or which might confound interpretation of study results.

Sites / Locations

  • Maxis Llc

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Device Arm

Arm Description

Participants will use the MouthLab device for monitoring their vital signs

Outcomes

Primary Outcome Measures

Collect trends in physiological vital signs and symptom burden to identify individuals who are at risk of clinical decompensation
Accurately collect trends in physiological vital signs and symptom burden of individuals with COVID-19 infection or who test positive for SARS-CoV-2 captured with the MouthLab to identify individuals who are at risk of clinical decompensation. (Phase 1)
AIDI evaluation
Evaluate trends in physiological vital signs and symptom burden of individuals with COVID-19 infection or who test positive for SARS-CoV-2 which are captured with the MouthLab device to evaluate the ability of the Aidar COVID-19 Decompensation Index (AIDI) to accurately identify individuals at risk of clinical decompensation. (Phase 2)

Secondary Outcome Measures

Usability of MouthLab device
Usability of the MouthLab device will be assessed via a questionnaire at study exit
Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions
Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions

Full Information

First Posted
February 1, 2022
Last Updated
February 6, 2023
Sponsor
AIDAR Health, Inc.
Collaborators
MAXIS, LLC
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1. Study Identification

Unique Protocol Identification Number
NCT05220306
Brief Title
AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation
Acronym
AIDI
Official Title
AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation
Study Type
Interventional

2. Study Status

Record Verification Date
February 2022
Overall Recruitment Status
Withdrawn
Why Stopped
No eligible patients could be found and funding was lost
Study Start Date
January 27, 2022 (Actual)
Primary Completion Date
July 31, 2022 (Actual)
Study Completion Date
July 31, 2022 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
AIDAR Health, Inc.
Collaborators
MAXIS, LLC

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
Yes
Product Manufactured in and Exported from the U.S.
No

5. Study Description

Brief Summary
The AIDI study has to phases. It's purpose is to capture vital signs using a non-invasive, hand-held, home monitoring device (MouthLab Device) from individuals with COVID-19 or who test positive for SARS-CoV-2 (Phase I) and use an algorithm-based approach to identify individuals at risk of clinical decompensation (Phase II). Up to 500 unvaccinated and partially vaccinated subjects will be included (up to 100 in Phase I and up to 400 in Phase II).

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Sars-CoV-2 Infection

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Sequential Assignment
Model Description
Phase I/Derivation Cohort (100 patients): This phase will use the MouthLab to capture vital signs. This data will help create Aidar's algorithm-based decompensation index (AIDI) that utilizes changes in vital signs to identify individuals who test positive for SARS-CoV-2 or with COVID-19 infection and are at risk of developing clinical decompensation. Phase II/Validation Cohort (400 patients): This phase will use the MouthLab to capture vital signs and use Aidar's algorithm-based decompensation index (AIDI) to identify individuals at risk of clinical decompensation.
Masking
None (Open Label)
Allocation
N/A
Enrollment
0 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Device Arm
Arm Type
Experimental
Arm Description
Participants will use the MouthLab device for monitoring their vital signs
Intervention Type
Device
Intervention Name(s)
Monitoring of vital signs
Intervention Description
The MouthLab is a hand-held device. The user holds the unit in their left hand with the Mouthpiece between the teeth and lips and breathes normally into the device for 30 seconds.
Primary Outcome Measure Information:
Title
Collect trends in physiological vital signs and symptom burden to identify individuals who are at risk of clinical decompensation
Description
Accurately collect trends in physiological vital signs and symptom burden of individuals with COVID-19 infection or who test positive for SARS-CoV-2 captured with the MouthLab to identify individuals who are at risk of clinical decompensation. (Phase 1)
Time Frame
Daily for 60 days
Title
AIDI evaluation
Description
Evaluate trends in physiological vital signs and symptom burden of individuals with COVID-19 infection or who test positive for SARS-CoV-2 which are captured with the MouthLab device to evaluate the ability of the Aidar COVID-19 Decompensation Index (AIDI) to accurately identify individuals at risk of clinical decompensation. (Phase 2)
Time Frame
Daily for 60 days
Secondary Outcome Measure Information:
Title
Usability of MouthLab device
Description
Usability of the MouthLab device will be assessed via a questionnaire at study exit
Time Frame
At 60 days
Title
Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions
Description
Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions
Time Frame
Day 0 to Day 60

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Male or female 18 years of age or older Unvaccinated individuals, or individuals who have received only 1 dose of an mRNA vaccine Individuals who have received a positive SARS-CoV-2 result within 24-48 hours (lab-based PCR or antigen test) Willing and able to provide informed consent Ability to read, write, and comprehend English Have no functional limitation that would impede the use of the MouthLab device Willing to provide access to health information via electronic health records (EHR) Exclusion Criteria: Currently receiving hospice care Have a left ventricular assist device Left-sided hemiplegia or any other motor deficits that may restrict the use of the device. individuals with cognitive deficits that impede their ability to comprehend and give informed consent. Individuals who are enrolled in any other investigational research studies of SARS-CoV2 or COVID-19 Individuals who are treated with monoclonal antibody therapy prior to diagnosis Individuals who are admitted to a hospital or acute care facility at the time of diagnosis Individuals with pacemakers or implanted cardio-defibrillators (ICDs) History of hemoptysis, pneumothorax, thoracic or abdominal aneurysm, pulmonary embolism, or stroke History of unstable cardiovascular status, including recent myocardial infarction (MI within 30 days), unstable angina, or uncontrolled hypertension Color blindness Chest, abdominal or eye surgery within the preceding 14 days Any condition that in the judgment of the investigators would interfere with the subject's ability to provide informed consent, comply with study instructions, place the subject at increased risk, or which might confound interpretation of study results.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Sujith Shetty, MD
Organizational Affiliation
MAXIS, LLC
Official's Role
Principal Investigator
Facility Information:
Facility Name
Maxis Llc
City
San Jose
State/Province
California
ZIP/Postal Code
95120
Country
United States

12. IPD Sharing Statement

Plan to Share IPD
No

Learn more about this trial

AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation

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