NCT04390516CompletedNot Applicable
Prediction Of Respiratory Decompensation In Covid-19 Patients Using Machine Learning: The READY Trial
Dascena2 sites in 1 country197 target enrollmentStarted: March 24, 2020Last updated:
Conditions
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 197
- Locations
- 2
- Primary Endpoint
- Mechanically ventilated patient outcome
Study Overview
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.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts 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
Outcomes
Primary Outcomes
Mechanically ventilated patient outcome
Time Frame: Through study completion, an average of 2 months
Ventilated or not ventilated within 24 hours
Secondary Outcomes
- Mortality or mechanically ventilated patient outcome(Through study completion, an average of 2 months)
Investigators
Study Sites (2)
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