Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning

Not Applicable

Completed

• Conditions: Coronavirus; Mortality; COVID-19

• 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.

Study Timeline

• Study Start: 2020-03-10
• Status Verified: 2020-06
• Primary Completion: 2020-06-04
• Study Completion: 2020-06-04
• Study First Submitted: 2020-06-08
• Study First Submitted QC: 2020-06-08
• Last Update Submitted: 2020-06-08
• Study First Posted: 2020-06-09
• Last Update Posted: 2020-06-09

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

Primary Outcome Measures

Name	Time	Method
Mortality outcome	Through study completion, an average of 3 months	Time to in-hospital death

Trial Locations

Locations (1)

Dascena; 🇺🇸 Oakland, California, United States