CD2H COVID-19

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

Purpose

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

Conditions

COVID-19
Coronavirus
Mortality
Mechanical Ventilation

Eligibility

Eligible Ages: Over 18 Years
Eligible Sex: All
Accepts Healthy Volunteers: No

Inclusion Criteria

Patients aged 18 years or older - Confirmed COVID-19 infection through RT-PCR test

Exclusion Criteria

Patients aged less than 18 years

Study Design

Phase: N/A
Study Type: Interventional
Allocation: N/A
Intervention Model: Single Group Assignment
Primary Purpose: Diagnostic
Masking: None (Open Label)

Arm Groups

Arm	Description	Assigned Intervention
Other COViage	Machine learning intervention	Device: COViage The COViage machine learning algorithm is designed to predict mechanical ventilation and mortality within 24 hours after hospital admission.

Recruiting Locations

More Details

NCT ID: NCT04390516
Status: Completed
Sponsor: Dascena

Detailed Description

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.

Notice

Study information shown on this site is derived from ClinicalTrials.gov (a public registry operated by the National Institutes of Health). The listing of studies provided is not certain to be all studies for which you might be eligible. Furthermore, study eligibility requirements can be difficult to understand and may change over time, so it is wise to speak with your medical care provider and individual research study teams when making decisions related to participation.