CoronavirusAI-COV-19NCT04510441
Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms
An observational study of Coronavirus, run by Imperial College London at 4 sites in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronavirus
- What is being tested?
- Nil intervention
- How many people
- 2,000 participants
- Where
- 4 sitesUnited Kingdom
- Study dates
- 26 May 2020to 1 May 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
4 sites in 1 country.
United Kingdom4 sites, 3 recruiting
- Recruiting
London North West University Healthcare NHS Trust
London, HA1 3UJ
Jaymin Shah, MRCP PhD, 02075949832, jaymin.shah@nhs.net
- Recruiting
Chelsea and Westminster Hospital NHS Foundation Trust
London, TW7 6AF
Emmanuel Ako, MRCP PhD, e 02075949832, Emmanuelle.Ako@chelwest.nhs.uk
Abtehale Al-Hussaini, MRCP PhD, e 02075949832, abtehale.Al-hussaini@chelwest.nhs.uk
- Active, not recruiting
Imperial College London (Hammersmith campus)
London, W12 0NN
- Recruiting
St Mary's Hospital
London, W2 1NY
Fu Siong Ng, 02075943614, f.ng@imperial.ac.uk
Kiran Patel, 02075943614, kiran.patel@imperial.ac.uk
About this study
Official title: 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.
Read the 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).
- Conditions
- Coronavirus
- Being tested
- Nil intervention
- Keywords
- coronavirus, electrocardiogram, artificial intelligence, COVID-19
- Medical subject headings
- Coronavirus Infections, COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Nil interventionOther
Nil intervention; retrospective cohort study
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: 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
Number of participants who had COVID19-related heart problems.
Measured: At the end of data analyses, approximately 1 year
Accuracy of machine learning vs human assessment to diagnose coronavirus (COVID-19) infection
Number of participants that can be identified as having COVID19 using machine learning vs human or other clinical test or assessment
Measured: At the end of data analyses, approximately 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2020
- Study start26 May 2020
- Primary completion1 May 2022 (estimated)
- Study completion1 May 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CoronavirusStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Collaborators
- Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04510441
- Sponsor's protocol ID
- 20HH5967
- Acronym
- AI-COV-19
- Status last verified
- Aug 2021
- First submitted
- 7 Aug 2020
- Last updated
- 30 Aug 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Study at a glance
From the registry record, last updated by the sponsor on 30 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms and Imaging
- ClinicalTrials.gov ID
- NCT04510441
- Acronym / sponsor ID
- AI-COV-19 / 20HH5967
- Conditions
- Coronavirus
- Keywords
- coronavirus, electrocardiogram, artificial intelligence, COVID-19
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Collaborators
- Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust
- Study start
- 26 May 2020 (actual)
- Primary completion
- 1 May 2022 (estimated)
- Study completion
- 1 May 2022 (estimated)
- First posted
- 12 Aug 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Nil interventionNil intervention; retrospective cohort study | Other | – |
Primary outcome
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
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
2 secondary outcomes
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
Accuracy of machine learning vs human assessment to diagnose coronavirus (COVID-19) infection
Time frame: At the end of data analyses, approximately 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Aug 2021.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
London North West University Healthcare NHS Trust
London, United Kingdom
Chelsea and Westminster Hospital NHS Foundation Trust
London, United Kingdom
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronavirus, run by Imperial College London at 4 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 30 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Aug 2021.