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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-05-26

Study Completion Date

2022-05-01

Brief Summary

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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.

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Detailed Description

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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

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Coronavirus

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

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Nil intervention

Nil intervention; retrospective cohort study

Intervention Type OTHER

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No