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The purpose of this study is to prospectively evaluate a machine learning algorithm for the prediction of outcomes in COVID-19 patients.
In a multi-center prospective clinical trial, a machine learning algorithm was deployed at five partner hospitals to analyze live patient data, including blood pressure and Creatinine levels, to determine the algorithm's ability to predict COVID-19 patient prognosis. The primary endpoint was mechanical ventilation of study subjects within 24 hours after hospital admission separate from a decompensation alert related to oxygen levels.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| COViage | Other | Machine learning intervention |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| COViage | Device | The COViage machine learning algorithm is designed to predict mechanical ventilation and mortality within 24 hours after hospital admission. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Mechanically ventilated patient outcome | Ventilated or not ventilated within 24 hours | Through study completion, an average of 2 months |
| Measure | Description | Time Frame |
|---|---|---|
| Mortality or mechanically ventilated patient outcome | Death or ventilated, or no death or not ventilated within 24 hours | Through study completion, an average of 2 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Dascena | Oakland | California | 94612 | United States |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| D018352 | Coronavirus Infections |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| D014777 |
| Virus Diseases |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |