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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

290 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-03-10

Study Completion Date

2020-06-04

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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COVID-19 Coronavirus Mortality

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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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.

Group Type EXPERIMENTAL

COViage

Intervention Type DEVICE

Machine learning intervention

Interventions

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COViage

Machine learning intervention

Intervention Type DEVICE

Eligibility Criteria

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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

Eligible Sex

ALL

Accepts Healthy Volunteers

No