Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning
Purpose
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.
Conditions
- COVID-19
- Coronavirus
- Mortality
Eligibility
- Eligible Ages
- All ages
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
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
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Parallel Assignment
- Primary Purpose
- Diagnostic
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Exposed group |
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. |
|
Recruiting Locations
More Details
- NCT ID
- NCT04423991
- Status
- Completed
- Sponsor
- Dascena
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.