Machine Learning in Quantitative Stress Echocardiography
Machine Learning in Quantitative Stress Echocardiography
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
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.
Inclusion Criteria:

 -  Clinically suitable for stress echocardiography examination

 Exclusion Criteria:

 -  None