Personalized sudden cardiac death risk prediction in genetic heart diseases: Beyond one-size-fits-all

Sudden cardiac death (SCD) risk prediction in genetic heart diseases is essential to identify patients who will benefit from implantable cardioverter-defibrillator (ICD) implantation. Although many prediction tools have been developed, risk prediction remains challenging due to variability in underlying arrhythmic substrates and statistical modeling approaches. This review addresses 2 major challenges in current clinical practice. First, the use of surrogate SCD end points, such as appropriate ICD therapy, can potentially and actually does lead to overestimation of the “true” SCD risk. This may result in unnecessary ICD implantation in low-risk patients and exposing them to device-related complications. Second, most risk models are static and do not account for temporal changes in risk. We provide an overview of SCD prediction models and offer recommendations to address these challenges. This review underscores the need for disease-specific surrogate end points and dynamic risk models that reflect individual risk over time.