COVID-19 Mortality Prediction Model

NCT ID: NCT04358510

Last Updated: 2020-04-24

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

Total Enrollment

114 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-04-01

Study Completion Date

2020-04-17

Brief Summary

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The objective of this study is to develop and evaluate an algorithm which accurately predicts mortality in COVID-19, pneumonia and mechanically ventilated ICU patients.

Detailed Description

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Retrospective study of 53,001 total ICU patients, including 9,166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. The NPH patient dataset includes 114 patients positive for SARS-COV-2 by PCR test.

Conditions

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COVID-19 Pneumonia Mechanical Ventilation

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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COViage

Machine learning intervention

COViage

Intervention Type DEVICE

The COViage machine learning algorithm is designed to predict mortality in COVID-19, pneumonia and mechanically ventilated ICU patients.

Interventions

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COViage

The COViage machine learning algorithm is designed to predict mortality in COVID-19, pneumonia and mechanically ventilated ICU patients.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Patients aged 18 years or older
* Record of ICU stay

Exclusion Criteria

* Patients aged less than 18 years
* Patients for which there were no records of raw data or no discharge or death dates.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Dascena

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Dascena

Oakland, California, United States

Site Status

Countries

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United States

Other Identifiers

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04202001

Identifier Type: -

Identifier Source: org_study_id

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