Risk stratification of sudden cardiac death.

Sudden cardiac death (SCD) is defined as a non-traumatic, fatal event occurring within 1 hour of the onset of symptoms in an apparently healthy subject. The annual incidence varies widely across different studies. In the USA reported incidences varied from 180 000 to over 450 000 cases of SCD nationwide. SCD can present as a ventricular tachyarrhytimia, either ventricular tachycardie (VT), ventricular fibrillation, pulseless electrical activity, or asystole. Approximately 90 to 95% of SCD events occur in a diseased or structurally abnormal heart. The most common underlying pathology being coronary artery disease (CAD), accounting for 80% of events. An additional 10 to 15% of events occur in patients with a non-ischemic cardiomyopathy (NICM), the most frequent being a hypertrophic cardiomyopathy (HCM). Less frequent NICM's are dilated cardiomyopathies, arrhytmogenic right ventricular cardiomyopathy, or infiltrative cardiomyopathies (such as amyloidosis or sarcoidosis). The remaining 5 to 10% of SCD occurs in patients with a structurally normal heart and are regarded as primary electrical disorders. For exemple long QT syndrome, short QT syndrome, Brugada syndrome and Catecholaminergic polymorphic VT. Risk stratification of SCD aims at identifying patients at high risk of SCD-related events as ventricular arrhythmia, because precautionary measures in these patients as starting or altering pharmacological therapy or implantation of an ICD, could improve their survival. Current strategies have mainly focused on the highest risk group because of their high relative risk of SCD, where in fact this population is only a very ssmall proportion ot the total number of deaths annually. For example, current guidelines on ICD indications are mainly driven by left ventricle ejection fraction (LVEF) and NYHA status, whereas the incidence of SCD is up to 30 times higher in these high risk populations, the absolute number events in the general population far exceeds the number of events in these high risk populations. Therefore, the ideal risk stratification tool would identify all patients who will experience VT or VF, excluding those who will not and those who will die from non-arrhythmic causes. Current risk stratification strategy in patients with ICM and NICM are from from ideal. LVEF has a poor sensitivity and specificity to predict arrhythmia resulting in questionable cost-effectiveness of ICD therapy in the primary prevention of SCD. The pathophysiology of ventricular arrhythmias represents a complex interplay of numerous variables that vary over time. Ventricular arrhythmias are initiated by triggers arising from electrical instable or vulnerable myocardial cells and have been linked to preceding abnormalities in the autonomic control of the cardiovascular system. These triggers and alterations in autonomic function are unlikely to cause events without the presence of myocardial abnormalities, called the myocardial substrate. Non-invasive risk stratification tools focus on these pathophysioloigcal mechanisms underlying ventriculair arrhythmia: disturbances in autonomic tone, slow conduction, the extent of myocardial damage, ventricular ectopy and abnormalities in ventricular repolarization. In the past years, there has been a shift from evidence-based medicine at population levels towards personalized clinical care, also known as precision medicine. This shift resulted in the introduction of digital twin technology in healthcare. A digital twin is a virtual representation of a process or system to simulate real-world events using synthetic data and artificial intelligence. Digital twin technology is based on the combination of statistical modelling, as we know from routine population data, and knowledge-based biophysical modelling which integrates patient data into artificial intelligence models that simulate patient-specific outcome incidences. We hypothesize that digital twin technology can further improve SCD risk stratification. Currenty, the bottleneck of digital twins in healthcare is the lack of well-phenotyped registries with detailed ascertainment of major clinical events. The first research objective would be to build, expand and curate a large retrospective registry, including electrical recordings, imaging, and electronic medical records, of all patients with cardiac implantable electronic devices implanted and followed in UZ Leuven. Following steps would include statistical modelling to predict the risk of ventricular arrhythmias and SCD allowing the comparison of the predictive value between a merely data-driven statistical model and the cardiac digital twin, and to support and perform biophysical modelling aimed to better describe and understand the arrhythmogenicity in patients at increased risk of ventricular arrhythmias and SCD. The ultimate goal is to support and develop a cardiac digital twin focused on prediction of ventricular arrhythmias.