Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning
Not Applicable
Completed
- Conditions
- CoronavirusMortalityCOVID-19
- Interventions
- Device: COViage
- Registration Number
- NCT04423991
- Lead Sponsor
- Dascena
- Brief Summary
The purpose of this study was to assess the performance of a machine learning algorithm which identifies patients for whom hydroxychloroquine treatment is associated with predicted survival.
- Detailed Description
In a multi-center pragmatic clinical trial, COVID-19 positive patients admitted to 6 United States medical centers were enrolled between March 10 and June 4, 2020. A machine learning algorithm was used to determine which patients were suitable for treatment with hydroxychloroquine.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 290
Inclusion Criteria
- Patient admitted to covered ward and tested positive for COVID-19
- Patient had COViage applied to electronic health record data within four hours of COVID-19 test
Exclusion Criteria
- Patient not admitted to covered ward or tested negative for COVID-19
- Patient had COViage applied to electronic health record data greater than four hours after COVID-19 test
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Exposed group COViage All patients were exposed to the algorithm and were characterized as being likely responders to hydroxychloroquine treatment. Treatment decisions regarding the administration of hydroxychloroquine were made independently by care providers.
- Primary Outcome Measures
Name Time Method Mortality outcome Through study completion, an average of 3 months Time to in-hospital death
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Dascena
🇺🇸Oakland, California, United States