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No eligible patients could be found and funding was lost
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| Name | Class |
|---|---|
| Avania | INDUSTRY |
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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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Device Arm | Experimental | Participants will use the MouthLab device for monitoring their vital signs |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Monitoring of vital signs | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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) | Daily for 60 days |
| 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) | Daily for 60 days |
| Measure | Description | Time Frame |
|---|---|---|
| Usability of MouthLab device | Usability of the MouthLab device will be assessed via a questionnaire at study exit | At 60 days |
| Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Sujith Shetty, MD | Avania | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Maxis Llc | San Jose | California | 95120 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40146983 | Derived | 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. |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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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.
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Evaluate any short-term changes in vital signs among patients with underlying comorbid conditions
| Day 0 to Day 60 |
| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |