Clinical trial NCT03659526: Quantitative Stress Echocardiography to Diagnose Myocardial Ischaemia
Official title: Development, Validation and Implementation of a New Quantitative Stress Echocardiographic Test for Myocardial Ischaemia
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Ischemia, Myocardial
Keywords: Myocardial velocity imaging
Strain
Strain rate
Speckle tracking
Deformation
Quantitative
Echocardiography
Machine learning
Lead sponsor: University Hospital of Wales
Sponsor class: OTHER
Collaborators: name: Universitaire Ziekenhuizen KU Leuven
name: Universitat Pompeu Fabra
name: Hull University Teaching Hospitals NHS Trust
name: Danderyd Hospital
First posted: 2018-09-06
Enrollment: 390
Sex: ALL
Minimum age (years): 20
Maximum age (years): 89
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Secondary care referrals

Brief summary: Patients with chest pain on exertion need a reliable non-invasive test to identify if they have inducible myocardial ischaemia. This would reduce the use of diagnostic coronary arteriography, avoid its risks and costs, and guide clinical decisions. Conventional stress echocardiography has poor reproducibility because it relies on qualitative and subjective interpretation. Quantitative approaches based on precise and reliable measurements of myocardial velocity, strain, strain rate and global longitudinal strain have been shown to be able to accurately diagnose myocardial ischaemia. A more accurate test using myocardial velocity imaging was not implemented by ultrasound vendors although it provided an objective measurement of myocardial functional reserve on a continuous scale from normality to severe ischaemia.

The investigators propose an original approach to create a diagnostic software tool that can be used in routine clinical practice. The investigators will extract and compare quantitative data obtained through myocardial velocity imaging and speckle tracking in subjects who undergo dobutamine stress echocardiography.

The data will be analysed using advanced computational mathematics including multiple kernel learning and joint statistics applied to multivariate data across multiple dimensions (including velocity, strain and strain rate traces). This approach will be validated against quantitative coronary arteriography and fractional flow reserve. The results will be displayed as parametric images and placed into a reporting tool. The output will determine the presence and severity of myocardial ischaemia. These new tools will have the capacity for iterative learning so that the precision of the diagnostic conclusions can be continuously refined.


Eligibility criteria: Inclusion Criteria:

* Chest pain, chest pain equivalent

Exclusion Criteria:

* acute coronary syndrome with elevated troponin, severe heart valve disease, uncontrolled hypertension (resting SBP \>200mmHg), cardiomyopathy, contraindication to dobutamine, pregnancy

Primary outcomes:
measure: Diagnostic accuracy of quantitative measures of dobutamine stress echocardiography
timeFrame: 18 months
Secondary outcomes:
measure: Lowest dose of dobutamine to provoke measurable marker of inducible myocardial ischaemia
timeFrame: 18 months
measure: Diagnostic accuracy of using machine learning to interpret multiparametric and multidimensional datasets to diagnose myocardial ischaemia
timeFrame: 18 months
Other outcomes:
Locations:
city: Leuven
country: Belgium
facility: UZ Leuven
city: Stockholm
country: Sweden
facility: Danderyd Hospital
city: Cardiff
country: United Kingdom
facility: University Hospital Wales
city: Cottingham
country: United Kingdom
facility: Castle Hill Hospital
Study officials:
affiliation: University Hospital Wales
name: Alan G Fraser
role: PRINCIPAL_INVESTIGATOR
References: