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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Pneumonia; Covid19

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

• Study Type: Observational
• Enrollment (Anticipated): 300

Contacts and Locations

• Study Contact: Name: Thomas C Booth, PhD; Phone Number: +44203299482; Email: thomas.booth@kcl.ac.uk
• Study Contact Backup: Name: James Teo, PhD; Phone Number: +44203299482; Email: jamesteo@nhs.net

Study Locations

• United Kingdom
  • London, United Kingdom
    • Recruiting
    • Guy's and St Thomas' Hospital
    • Contact: Vicky Goh, FRCR
  • London, United Kingdom
    • Recruiting
    • King's College Hospital
    • Contact: Thomas Booth, PhD
  • Kent
    • Orpington, Kent, United Kingdom
      • Recruiting
      • Princess Royal University Hospital
      • Contact: James Teo, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

All adults with COVID-19 at secondary and tertiary centres

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
wave 1
wave 2

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

• Sponsor: King's College Hospital NHS Trust
• Collaborators: King's College London

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2020
• Primary Completion (Actual): May 1, 2021
• Study Completion (Anticipated): December 1, 2021

Study Registration Dates

• First Submitted: April 19, 2021
• First Submitted That Met QC Criteria: April 19, 2021
• First Posted (Actual): April 22, 2021

Study Record Updates

• Last Update Posted (Actual): September 16, 2021
• Last Update Submitted That Met QC Criteria: September 14, 2021
• Last Verified: September 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Lung Diseases; Pneumonia
• Other Study ID Numbers: IRAS 232823

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No