- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04193475
Machine Learning in Quantitative Stress Echocardiography (MLQSE)
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
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
New onset chest pain is a common presenting complaint and can be a marker of significant cardiovascular disease and risk of myocardial infarction and death; therefore obtaining an accurate diagnosis is critical to guide patient management. It is noteworthy that only 40-50% of patients who have invasive arteriography subsequently undergo revascularisation. This underscores the imprecision of the initial tests employed prior to arteriography.
Historically electrocardiographic stress testing during exercise has been used to detect inducible myocardial ischaemia but its diagnostic sensitivity and specificity are low (about 65%). Diagnostic accuracy can be improved by by incorporating echocardiography or single photon emission computed tomography. Current NICE guidelines recommend that patients with chest pain of recent onset should be investigated with CT coronary angiography as a first line, and if this reveals a significant stenosis then a functional imaging test should be performed.
The Myocardial Doppler in Stress Echocardiography (MYDISE) study assessed the diagnostic value of quantitative stress echocardiography during the infusion of dobutamine, a short-acting synthetic catecholamine that acts on β-1 adrenergic receptors to increase heart rate and myocardial contractility. Measuring the systolic velocities of LV long-axis function at peak stress had good reproducibility (coefficients of variation in basal segments 9-14% at rest and 11-18% at peak stress) and similar sensitivities and specificities (about 70%) to published studies in which expert observers reported wall motion scores. When adjusted for the independent effects of age, gender and heart rate, however, diagnostic accuracy increased significantly with C statistics (area under receiver-operator curves) up to 90%.
Visual analysis of stress echocardiography to detect myocardial ischaemia depends on qualitative assessment of multiple parameters. Major studies of quantitative stress echocardiography have been limited to identifying the single best echocardiographic variable, and they have used diameter stenosis as the reference criterion. Progressive subclinical reductions of regional (long-axis) myocardial function have been demonstrated in subjects with cardiovascular risk factors, affecting myocardial deformation (strain and strain rate) as well as velocities. Ischaemia changes the timing of events during the cardiac cycle - for example prolonging pre-ejection and post-ejection phases. These factors confirm the clinical need for objective measurement of regional myocardial function throughout the cardiac cycle.
It is now possible to create algorithms that are based not just on a single time point (e.g., peak velocity) but instead rely on analysis of the whole of the velocity trace. This concept can also be extended to include strain and strain rate curves. Investigators at Universitat Pompeu Fabra, Barcelona, have developed this approach to create a statistical atlas of the heart to detect dyssynchrony. A similar concept has been applied using multiple kernel learning to patients with dyspnoea who have undergone exercise stress testing to identify those with evidence of diastolic heart failure.This has enabled velocity traces taken from the whole of the cardiac cycle to be compared and discriminated between control subjects (with and without dyspnoea) and those diagnosed with heart failure with preserved ejection fraction (HFpEF); the major differences observed are in early diastolic function. This application has not previously been used to explore inducible myocardial ischaemia in stress echocardiography, but similar findings might be expected, as changes during diastole are amongst the earliest and most sensitive indicators of myocardial ischaemia. Individuals at the University of Leuven (Prof Jan D'hooge) have recently developed supervised machine-learning methods that allow for automatic classification of myocardial segments based on their local mechanical behaviour (i.e. velocity/strain/strain rate) after going through a training phase; the proposed machine-learning approach outperforms expert wall motion readings as well as expert interpretation of segmental strain (rate) traces in classifying ischemic segments.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Imran Sunderji, MBBS
- Phone Number: 07540844481
- Email: imran.sunderji@nhs.net
Study Locations
-
-
-
Cottingham, United Kingdom, HU165JQ
- Recruiting
- Castle Hill Hospital
-
Contact:
- Imran Sunderji
- Email: imran.sunderji@nhs.net
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Clinically suitable for stress echocardiography examination
Exclusion Criteria:
- None
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Chest pain
Individuals presenting with chest pain requiring a stress echocardiogram.
|
No intervention planned.
Novel analysis of echocardiographic data.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Inducible myocardial ischaemia
Time Frame: 3 years
|
Diagnostic performance of the machine learning classifier for the detection of inducible myocardial ischaemia as determined by reduced coronary flow reserve
|
3 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Workload
Time Frame: 3 years
|
Diagnostic performance of workload (units = watts) for the detection of inducible myocardial ischaemia as determined by reduced coronary flow reserve.
|
3 years
|
Velocity
Time Frame: 3 years
|
Diagnostic performance of velocity (units = m/s) for the detection of myocardial functional reserve compared with quantitative coronary arteriography and with coronary flow reserve.
|
3 years
|
Strain rate
Time Frame: 3 years
|
Diagnostic performance of strain rate (units = s^-1) for the detection of myocardial functional reserve compared with quantitative coronary arteriography and with coronary flow reserve.
|
3 years
|
Strain
Time Frame: 3 years
|
Diagnostic performance of strain (units = s) for the detection of myocardial functional reserve compared with quantitative coronary arteriography and with coronary flow reserve.
|
3 years
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- R2458
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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