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

D

Dascena

Status

Completed

Conditions

Mortality
COVID-19
Coronavirus

Treatments

Device: COViage

Study type

Interventional

Funder types

Industry

Identifiers

NCT04423991
060820

Details and patient eligibility

About

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.

Full 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.

Enrollment

290 patients

Sex

All

Volunteers

No Healthy Volunteers

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

Trial design

Primary purpose

Diagnostic

Allocation

Non-Randomized

Interventional model

Parallel Assignment

Masking

None (Open label)

290 participants in 1 patient group

Exposed group
Experimental group
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.
Treatment:
Device: COViage

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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