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

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.

Study Overview

• Status: Completed
• Conditions: COVID-19; Mortality; Coronavirus
• Intervention / Treatment: Device: COViage

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 Type: Interventional
• Enrollment (Actual): 290
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • California
    • Oakland, California, United States, 94612
      • Dascena

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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 Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
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.	Device: COViage; Machine learning intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mortality outcome	Time to in-hospital death	Through study completion, an average of 3 months

Collaborators and Investigators

• Sponsor: Dascena

Study record dates

Study Major Dates

• Study Start (Actual): March 10, 2020
• Primary Completion (Actual): June 4, 2020
• Study Completion (Actual): June 4, 2020

Study Registration Dates

• First Submitted: June 8, 2020
• First Submitted That Met QC Criteria: June 8, 2020
• First Posted (Actual): June 9, 2020

Study Record Updates

• Last Update Posted (Actual): June 9, 2020
• Last Update Submitted That Met QC Criteria: June 8, 2020
• Last Verified: June 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: 060820

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No