Sudden Cardiac Death

Sudden cardiac death (SCD) is a major health issue, with severe consequences for patients and their families, especially in the younger population, where SCD remains one of the most important causes of death. While coronary artery disease, mostly correlated with lifestyle, progressively becomes a more important cause of (sudden) cardiac death with progressing age, primary inherited diseases are predominantly causing sudden death in young patients.

These inherited cardiac diseases comprise primary electrical heart diseases (e.g. Brugada syndrome, long QT syndrome,...), inherited cardiomyopathies (e.g. hypertrophic and dilated cardiomyopathy,...) and connective tissue diseases of the aorta (e.g. Marfan syndrome, Loeys-Dietz syndrome,...).

Given the devastating impact of these diseases on society, early recognition of the underlying condition - preferably in a preclinical stage - is of great importance. Therefore, cascade family screening is performed in sudden cardiac death victims or patients with known inherited cardiac disease and genetic testing is offered routinely. Whereas establishing the diagnosis is not always easy or straightforward, determining the lifetime risk for severe arrhythmia or cardiac death in patients after this diagnosis is established remains even more challenging.

Despite years of research and progress in this field, the current available diagnostic and prognostic tools appear to be insufficient. Most risk-scores are based on known clinical, electrocardiographic and imaging-derived parameters. Some of these parameters lack specificity and are especially not well adapted to the clinical tendency of sub-typing cardiac diseases. Therefore, there is a need for new parameters, especially for patients in a preclinical stage or with a new diagnosis

The aim of this research project is to develop a new risk-model for sudden cardiac death. We will base the new model on artificial intelligence (AI) analysis of electrocardiograms (ECG's) and clinical data of our cardiogenetic population in the University Hospital of Antwerp (UZA). Prior research has shown that creating a mortality risk-score by an AI algorithm in the general population is both feasible and reliable. This tool has the possibility to create new parameters relevant for mortality prediction that are not yet known to cardiologists.

The final goal of the research project is to be able to apply this risk model to all cardiogenetic susceptible patients, first in the UZA and later in other settings, and contribute to a better risk-stratification for all patients, and to be able to prevent sudden cardiac death in a more reliable and more performant way than with the current methods.

Funding: UZA Foundation

Researcher: Dr. Olivier Van Leuven

Promotors: Prof. dr. Bart Loeys, Prof. dr. Johan Saenen, dr. Ewa Sieliwonczyk