Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms (AI-COV-19)

August 24, 2021 updated by: Imperial College London

Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms and Imaging

Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It is highly contagious, and severe cases can lead to acute respiratory distress or multiple organ failure. On 11 March 2020, the WHO made the assessment that COVID-19 can be characterised as a pandemic. With the development of machine learning, deep learning based artificial intelligence (AI) technology has demonstrated tremendous success in the field of medical data analysis due to its capacity of extracting rich features from imaging and complex clinical datasets. In this study, we aim to use clinical data collected as part of routine clinical care (heart tracings, X-rays and CT scans) to train artificial intelligence and machine learning algorithms, to accurately predict the course of disease in patients with Covid-19 infection, using these datasets.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It is highly contagious, and severe cases can lead to acute respiratory distress or multiple organ failure and ultimately death. The disease can be confirmed by using the reverse-transcription polymerase chain reaction (RT-PCR) test. ECGs, Chest x-rays and CT scans are rich sources of data that provide insight to disease that otherwise would not be available.

Knowing who to admit to the hospital or intensive care saves lives as it helps to mitigate resource shortages. Novel Artificial Intelligence tools such as Deep learning will allow a complex assessment of the Imaging and clinical data that could potentially help clinicians to make a faster and more accurate diagnosis, better triage patients and assess treatment response and ultimately better prediction of outcome. Our group has significant experience implementing machine learning algorithms on vast quantities of ECGs, such as from the UK Biobank, and propose to extend our techniques to data from patients with Covid-19.

This is a retrospective data study on patients with suspicious and confirmed COVID-19.

The study aims to recruit up to 2000 patients who will be referred to have ECGs, chest X-rays or CT scans at Chelsea and Westminster Hospital NHS Foundation Trust, Imperial College Healthcare NHS Trust and London North West London University Healthcare NHS Trust.

To be included in this study, the patient must:

  • have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)
  • laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab samples) or clinical suspicion for Covid19 infection
  • be aged >18 years Patients with suboptimal ECGs, chest radiograph and CT studies due to artefacts will be excluded. Patients will also be excluded if the time-interval between ECGs, chest CT and the RT-PCR assay was longer than 7 days.

This study received HRA and Health and Care Research Wales (HCRW) approval on 18 May 2020 following review by Research Ethics Committee at a meeting held on 13 May 2020(Protocol number: 20HH5967; REC reference: 20/HRA/2467).

Study Type

Observational

Enrollment (Anticipated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United Kingdom
      • London, United Kingdom, HA1 3UJ
        • Recruiting
        • London North West University Healthcare NHS Trust
        • Contact:
      • London, United Kingdom, TW7 6AF
      • London, United Kingdom, W12 0NN
        • Active, not recruiting
        • Imperial College London (Hammersmith campus)
      • London, United Kingdom, W2 1NY

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

16 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

This is a retrospective data study on patients with suspicious and confirmed COVID-19.

The study aims to recruit up to 2000 patients who will be referred to have ECGs, chest X-rays or CT scans at Chelsea and Westminster Hospital NHS Foundation Trust, Imperial College Healthcare NHS Trust and London North West London University Healthcare NHS Trust.

Description

Inclusion Criteria:

  • have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)
  • positive laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab samples) or clinical suspicion for Covid-19 infection
  • be aged >18 years

Exclusion Criteria:

  • Suboptimal ECGs, chest radiographs or CT studies for deep learning methods due to artefacts including severe
  • motion artefacts which causes blurring of the contours of or significant artefacts due to metallic prosthesis which causes image degradation
  • Time-interval between ECGs, chest CT and the RT-PCR assay was longer than 7 days

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of machine learning to be able to predict outcome of coronavirus (COVID-19) infection
Time Frame: At the end of data analyses, approximately 1 year
Accuracy with which computer based analysis (machine learning) can diagnose and/or prognosticate Covid-19 Number of Participants With COVID19 who died or survived following hospital admission
At the end of data analyses, approximately 1 year
Accuracy of machine learning to be able to predict prognosis of coronavirus (COVID-19) infection
Time Frame: At the end of data analyses, approximately 1 year
Number of participants who required invasive vs non-invasive ventilation vs ward-based care vs died
At the end of data analyses, approximately 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of machine learning to be able to predict cardiac involvement of coronavirus (COVID-19) infection
Time Frame: At the end of data analyses, approximately 1 year
Number of participants who had COVID19-related heart problems.
At the end of data analyses, approximately 1 year
Accuracy of machine learning vs human assessment to diagnose coronavirus (COVID-19) infection
Time Frame: At the end of data analyses, approximately 1 year
Number of participants that can be identified as having COVID19 using machine learning vs human or other clinical test or assessment
At the end of data analyses, approximately 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Imperial College London

Collaborators

Imperial College Healthcare NHS Trust
London North West Healthcare NHS Trust
Chelsea and Westminster Hospital NHS Foundation Trust

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 26, 2020

Primary Completion (Anticipated)

May 1, 2022

Study Completion (Anticipated)

May 1, 2022

Study Registration Dates

First Submitted

August 7, 2020

First Submitted That Met QC Criteria

August 11, 2020

First Posted (Actual)

August 12, 2020

Study Record Updates

Last Update Posted (Actual)

August 30, 2021

Last Update Submitted That Met QC Criteria

August 24, 2021

Last Verified

August 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 20HH5967

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Yes

IPD Plan Description

This is a study using retrospective, pseudo-anonymised data that were acquired as part of routine clinical care for the patients. There are no direct risks to the patients' health. The main issues revolve around data security and storage. In order to address this, members of the direct care team who are not members of the research team will perform the pseudo-anonymisation of the data and pass a set of pseudo-anonymised data to the research team with no access to the pseudo-anonymisation code. The research team will therefore be unable to identify the patients from those data. The pseudo-anonymised data will also be securely stored to further minimise risks.

IPD Sharing Time Frame

within study duration

IPD Sharing Access Criteria

Researchers of the study

IPD Sharing Supporting Information Type

  • Study Protocol
  • Statistical Analysis Plan (SAP)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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