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

Dascena290 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

NON_RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

290

Conditions

COVID-19 Coronavirus Mortality

Interventions

COViageDEVICE

Machine learning intervention

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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

Locations (1)

Dascena

Oakland, California, United States

Outcomes

Primary Outcomes

Mortality outcome

Time to in-hospital death

Time frame: Through study completion, an average of 3 months

Data from ClinicalTrials.gov

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