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

Identification of a Responsive Subpopulation to Hydroxychloroquine in COVID-19 Patients Using Machine Learning: the IDENTIFY Trial

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.

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.

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