Key result
Current SCD risk models in genetic heart diseases overestimate risk, driven by static surrogate endpoints.
This review emphasizes the need for dynamic risk models and disease-specific surrogate endpoints to improve sudden cardiac death risk prediction in genetic heart diseases and avoid unnecessary ICD implantations.
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 modelling approaches. This review addresses two major challenges in current clinical practice. First, the use of surrogate SCD endpoints, 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 endpoints and dynamic risk models that reflect individual risk over time.
No takes yet. Share an insight, caveat, or question.
Heide et al. (2025) conducted a review in Genetic heart diseases. Sudden cardiac death risk prediction models was evaluated. Current sudden cardiac death risk prediction models in genetic heart diseases often overestimate risk due to reliance on surrogate endpoints and static designs that ignore temporal risk changes.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: