Coronavirus, COVID-19, Mechanical Ventilation, Mortality
Conditions
Brief summary
The purpose of this study is to prospectively evaluate a machine learning algorithm for the prediction of outcomes in COVID-19 patients.
Detailed description
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.
Interventions
DEVICECOViage
The COViage machine learning algorithm is designed to predict mechanical ventilation and mortality within 24 hours after hospital admission.
Sponsors
Dascena
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients aged 18 years or older * Confirmed COVID-19 infection through RT-PCR test
Exclusion criteria
* Patients aged less than 18 years
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mechanically ventilated patient outcome | Through study completion, an average of 2 months | Ventilated or not ventilated within 24 hours |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Mortality or mechanically ventilated patient outcome | Through study completion, an average of 2 months | Death or ventilated, or no death or not ventilated within 24 hours |
Countries
United States
Outcome results
None listed