Machine Learning in Quantitative Stress Echocardiography
MLQSE
1 other identifier
observational
1,250
1 country
1
Brief Summary
Greater diagnostic accuracy is required to find out who is at risk of a heart attack as this can reduce the requirement of more invasive downstream tests and thereby improve the patient experience and also reduce their exposure to risk. Stress echocardiography is a routine clinical test that involves using ultrasound to image the heart whilst it is under stress to assess the risk of a heart attack. This study will focus on developing more accurate analysis tools to interpret the results of these stress echocardiographic scans. New methods will be tested to measure the function of each part of the heart muscle, using advanced analysis of the information obtained when high-frequency sound waves are bounced off the heart inside the chest. The researchers will measure and report exact heart function during stress, so that they will be able to recognise normal hearts and those with any disease. New computer methods will be developed to display any abnormality, which will make it easier for doctors to choose the best treatment for patients who are at risk. The goals and potential benefits of this research proposal are to update the interpretation of a routinely used clinical test (stress echocardiography) to produce a reliable new method for diagnosing the precise effects of diseased arteries on the function of the heart muscle; to develop new computer graphics that adapt to show individual risks for each patient; and to implement new computer models that can be constantly updated
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Nov 2019
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
November 18, 2019
CompletedStudy Start
First participant enrolled
November 22, 2019
CompletedFirst Posted
Study publicly available on registry
December 10, 2019
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
March 1, 2026
CompletedFebruary 17, 2026
March 1, 2025
1.7 years
November 18, 2019
February 12, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Inducible myocardial ischaemia
Diagnostic performance of the machine learning classifier for the detection of inducible myocardial ischaemia as determined by reduced coronary flow reserve
3 years
Secondary Outcomes (4)
Workload
3 years
Velocity
3 years
Strain rate
3 years
Strain
3 years
Study Arms (1)
Chest pain
Individuals presenting with chest pain requiring a stress echocardiogram.
Interventions
Eligibility Criteria
All patients will be recruited into this study as a single group, with an age range of 20 - 89 years and with no restriction according to their pre-test probabilities of coronary artery disease. They will be eligible for inclusion so long as they have chest pain or another clinical indication for stress testing.
You may qualify if:
- Clinically suitable for stress echocardiography examination
You may not qualify if:
- None
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Hull University Teaching Hospitals NHS Trustlead
- Barts & The London NHS Trustcollaborator
- Cardiff and Vale University Health Boardcollaborator
Study Sites (1)
Castle Hill Hospital
Cottingham, HU165JQ, United Kingdom
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER GOV
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
November 18, 2019
First Posted
December 10, 2019
Study Start
November 22, 2019
Primary Completion
August 1, 2021
Study Completion
March 1, 2026
Last Updated
February 17, 2026
Record last verified: 2025-03