Back to resultsIdentification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning (IDENTIFY)
Primary Purpose
COVID-19, Coronavirus, Mortality
Status
Completed
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
COViage
Sponsored by
About this trial
This is an interventional diagnostic trial for COVID-19
Eligibility Criteria
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
Sites / Locations
- Dascena
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Exposed group
Arm Description
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.
Outcomes
Primary Outcome Measures
Mortality outcome
Time to in-hospital death
Secondary Outcome Measures
Full Information
1. Study Identification
Unique Protocol Identification Number
NCT04423991
Brief Title
Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning
Acronym
IDENTIFY
Official Title
Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning: the IDENTIFY Trial
Study Type
Interventional
2. Study Status
Record Verification Date
June 2020
Overall Recruitment Status
Completed
Study Start Date
March 10, 2020 (Actual)
Primary Completion Date
June 4, 2020 (Actual)
Study Completion Date
June 4, 2020 (Actual)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Dascena
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes
5. Study Description
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
COVID-19, Coronavirus, Mortality
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Non-Randomized
Enrollment
290 (Actual)
8. Arms, Groups, and Interventions
Arm Title
Exposed group
Arm Type
Experimental
Arm Description
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.
Intervention Type
Device
Intervention Name(s)
COViage
Intervention Description
Machine learning intervention
Primary Outcome Measure Information:
Title
Mortality outcome
Description
Time to in-hospital death
Time Frame
Through study completion, an average of 3 months
10. Eligibility
Sex
All
Accepts Healthy Volunteers
No
Eligibility Criteria
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
Facility Information:
Facility Name
Dascena
City
Oakland
State/Province
California
ZIP/Postal Code
94612
Country
United States
12. IPD Sharing Statement
Learn more about this trial
Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning
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