Towards Clinical Implementation of Deformation Imaging in Genetic Cardiomyopathies

Genetic cardiomyopathies are a significant cause of sudden cardiac death in young, seemingly healthy individuals. Early detection of these diseases is crucial to prevent sudden cardiac death. Arrhythmogenic right ventricular cardiomyopathy (ARVC) is one of these diseases, characterized by the occurrence of life-threatening arrhythmias early in the disease course. Regular cardiological examinations of patients and at-risk family members are necessary to take timely measures, such as implanting a cardioverter-defibrillator (ICD).

Echocardiographic deformation imaging can help detect cardiomyopathies in an early stage. This technique focuses on quantifying the mechanical deformation of the heart muscle throughout the cardiac cycle. Previous studies have shown that deformation imaging can detect subtle abnormalities that remain unnoticed during visual assessment of images. Despite its benefits, such as being non-invasive, cost-effective, and widely available, deformation imaging is not yet widely implemented in clinical practice.

In this dissertation, ARVC is used as a model to investigate the clinical application of deformation imaging, aiming for early disease recognition and risk stratification for life-threatening arrhythmias.

We demonstrate that deformation imaging allows us to detect the expression of genetic cardiomyopathies at an earlier stage than conventional techniques. Additionally, using a computer model, we can estimate the underlying disease substrate at the tissue level. Finally, abnormal deformation provides additional predictive value when added to a validated risk model.

Deformation imaging deserves a more prominent place in guidelines for diagnosing cardiomyopathies. Due to extensive automation, the technique is well-suited for general practice, allowing functional changes to be recorded more objectively than through visual echocardiogram assessment.