Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms
AI-COV-19
1 other identifier
observational
2,000
1 country
4
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started May 2020
4 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
May 26, 2020
CompletedFirst Submitted
Initial submission to the registry
August 7, 2020
CompletedFirst Posted
Study publicly available on registry
August 12, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
May 1, 2022
CompletedAugust 30, 2021
August 1, 2021
1.9 years
August 7, 2020
August 24, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Accuracy of machine learning to be able to predict outcome of coronavirus (COVID-19) infection
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
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 Outcomes (2)
Accuracy of machine learning to be able to predict cardiac involvement of coronavirus (COVID-19) infection
At the end of data analyses, approximately 1 year
Accuracy of machine learning vs human assessment to diagnose coronavirus (COVID-19) infection
At the end of data analyses, approximately 1 year
Interventions
Nil intervention; retrospective cohort study
Eligibility Criteria
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.
You may qualify if:
- 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
You may not qualify if:
- 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
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Imperial College Londonlead
- Imperial College Healthcare NHS Trustcollaborator
- Chelsea and Westminster NHS Foundation Trustcollaborator
- London North West Healthcare NHS Trustcollaborator
Study Sites (4)
London North West University Healthcare NHS Trust
London, HA1 3UJ, United Kingdom
Chelsea and Westminster Hospital NHS Foundation Trust
London, TW7 6AF, United Kingdom
Imperial College London (Hammersmith campus)
London, W12 0NN, United Kingdom
St Mary's Hospital
London, W2 1NY, United Kingdom
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
August 7, 2020
First Posted
August 12, 2020
Study Start
May 26, 2020
Primary Completion
May 1, 2022
Study Completion
May 1, 2022
Last Updated
August 30, 2021
Record last verified: 2021-08
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP
- Time Frame
- within study duration
- Access Criteria
- Researchers of the study
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.