Abstract
Dilated cardiomyopathy represents a specific heterogeneous subgroup of non-ischaemic cardiomyopathies, characterised by several degrees of reduced left ventricular ejection fraction. In general, outcomes have improved over the last few decades. The main drivers for the more favourable prognosis have been the reduction in cardiovascular and particularly fatal arrhythmic events. However, many presenting with sudden death have a left ventricular ejection fraction that does not meet consensus criteria for primary prevention device implantation, and often it occurs in those without preceding symptoms of heart failure. Also, it is recognised that many patients have a long preclinical phase characterised by few, if any symptoms – so-called hypokinetic non-dilated dilated cardiomyopathy.Knowledge regarding predictors of progression in this group is still lacking. The identification of individuals whose ventricular function will deteriorate over time and therefore require closer surveillance could improve the prognosis through the early identification of the disease. For these reasons, a deeper investigation and characterisation are required to tease early steps in the pathological pathways beyond ejection fraction and according to a specific risk of adverse events.
With the role of advanced tools in cardiac magnetic resonance imaging remaining a hotly debated emerging issue, this thesis set out to evaluate the feasibility of novel parameters and the additional benefit afforded towards an improved imaging phenotype by three novel techniques.
Following the conventional clinical characterisation of a large heterogeneous cohort of dilated cardiomyopathy patients, the first study contributes to the updates in state-of-the-art artificial intelligence, deployed for cardiac magnetic resonance pipelines in biventricular quantification. This had excellent validation in the largest evaluated dilated cardiomyopathy cohort to date.
Subsequently, through the rapid and automated generation of volumes over time, the creation of biventricular filling and ejection profiles were created to help delineate patterns of disease progression and the prediction of adverse clinical outcomes. A novel model, consisted of three parameters describing rates of change in biventricular filling and left ventricular ejection, showed enhanced predictive power for risk of adverse outcomes over conventional imaging parameters described in the current literature. As the cohort of dilated cardiomyopathy patients included those with an earlier disease phenotype of non-dilated hypokinetic changes, it is proposed that these parameters are uncovering earlier pathways of clinical dysfunction in addition to later progressive status given the correlations to end-diastolic and end-systolic volumes.
A familial subset of dilated cardiomyopathy patients with a highly arrhythmic status are identified and described in terms of their shape differences utilising computational modelling techniques. It was demonstrated that computational models derived from end-diastolic cardiac magnetic resonance acquisitions could help identify patterns associated with adverse arrhythmic risk in dilated cardiomyopathy patients.
Finally, the development and feasibility of an exercise cardiac magnetic resonance protocol capable of running in tandem with routine clinical study was utilised to enable the evaluation of recovered DCM patients whose left ventricular ejection fraction was like athletes with physiological adaptation. Parameters of filling and an estimate of ventricular-arterial coupling upon moderate exertion were obtainable and have future potential in applications of differentiating underlying pathological change from healthy adaptations.
| Date of Award | 1 Sept 2024 |
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| Original language | English |
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| Supervisor | Gerry Carr-White (Supervisor) & Reza Razavi (Supervisor) |