Respiratory Decompensation and Model for the Triage of COVID-19 Patients

Dascena197 enrolled

Overview

The purpose of this study is to prospectively evaluate a machine learning algorithm for the prediction of outcomes in COVID-19 patients.

In a multi-center prospective clinical trial, a machine learning algorithm was deployed at five partner hospitals to analyze live patient data, including blood pressure and Creatinine levels, to determine the algorithm's ability to predict COVID-19 patient prognosis. The primary endpoint was mechanical ventilation of study subjects within 24 hours after hospital admission separate from a decompensation alert related to oxygen levels.

Study Type

INTERVENTIONAL

Allocation

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

197

Conditions

COVID-19 Coronavirus Mortality Mechanical Ventilation

Interventions

COViageDEVICE

The COViage machine learning algorithm is designed to predict mechanical ventilation and mortality within 24 hours after hospital admission.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Patients aged 18 years or older * Confirmed COVID-19 infection through RT-PCR test Exclusion Criteria: * Patients aged less than 18 years

Locations (1)

Dascena

Oakland, California, United States

Outcomes

Primary Outcomes

Mechanically ventilated patient outcome

Ventilated or not ventilated within 24 hours

Time frame: Through study completion, an average of 2 months

Secondary Outcomes

Mortality or mechanically ventilated patient outcome

Death or ventilated, or no death or not ventilated within 24 hours

Time frame: Through study completion, an average of 2 months

Data from ClinicalTrials.gov

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