Coronavirus: Ventilator Outcomes Using Artificial Intelligence Chest Radiographs & Other Evidence-based Co-variates

Coronavirus Infectious Disease 2019: Ventilator Outcomes Using Artificial Intelligence, Chest Radiographs and Other Evidence-based Co-variates (COVID VOICE)

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04855539

Acronym

COVID VOICE

Enrollment

300

Registered

2021-04-22

Start date

2020-03-01

Completion date

2021-12-01

Last updated

2021-09-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Covid19, Pneumonia

Brief summary

We will determine ventilator outcomes to Coronavirus Infectious Disease 2019 (COVID-19) using artificial Intelligence with inputs of chest radiographs and other evidence-based co-variates.

Detailed description

The chest radiograph (chest x-ray) has emerged as the United Kingdom's National Health Service (NHS) frontline diagnostic imaging test for COVID-19, in conjunction with clinical history and key blood markers: C-reactive protein (CRP) and lymphopenia. Typically, every suspected COVID-19 patient presenting to the emergency department is undergoing blood tests and a chest radiograph. Therefore, it has become critical for radiologists to review and hot report the chest x-ray urgently. Primary Objective: Use chest radiographs and clinical data to determine whether patient can survive with a ventilator

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

Admitted to intensive care unit (ITU) or equivalent COVID-19 polymerase chain reaction (PCR) positive

Exclusion criteria

No imaging prior to ITU admission

Design outcomes

Primary

Measure	Time frame	Description
Sensitivity and specificity of a convolutional neural network to predict survival outcome	1 month	Defined by sensitivity, specificity, positive and negative predictive values

Countries

United Kingdom

Contacts

Primary ContactThomas C Booth, PhD

thomas.booth@kcl.ac.uk+44203299482

Backup ContactJames Teo, PhD

jamesteo@nhs.net+44203299482

Outcome results