AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation
NCT ID: NCT05220306
Last Updated: 2023-02-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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WITHDRAWN
NA
INTERVENTIONAL
2022-01-27
2022-07-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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NA
SEQUENTIAL
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.
DIAGNOSTIC
NONE
Study Groups
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Device Arm
Participants will use the MouthLab device for monitoring their vital signs
Monitoring of vital signs
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.
Interventions
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Monitoring of vital signs
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.
Eligibility Criteria
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Inclusion Criteria
* 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
* 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.
18 Years
ALL
No
Sponsors
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Avania
INDUSTRY
AIDAR Health, Inc.
INDUSTRY
Responsible Party
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Principal Investigators
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Sujith Shetty, MD
Role: PRINCIPAL_INVESTIGATOR
Avania
Locations
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Maxis Llc
San Jose, California, United States
Countries
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References
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Mathew J, Pagliaro JA, Elumalai S, Wash LK, Ly K, Leibowitz AJ, Vimalananda VG. Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study. JMIR Res Protoc. 2025 Mar 27;14:e54993. doi: 10.2196/54993.
Other Identifiers
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ADR04-AIDI-C-21
Identifier Type: -
Identifier Source: org_study_id
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