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

Area of Science:

• Cardiology
• Genetics
• Medical Devices

Background:

• Accurate prediction of sudden cardiac death (SCD) risk in patients with genetic heart diseases is vital for appropriate implantable cardioverter-defibrillator (ICD) implantation.
• Existing risk prediction tools face challenges due to diverse arrhythmic substrates and statistical modeling limitations.

Purpose of the Study:

• To review current challenges in SCD risk prediction for genetic heart diseases.
• To address the overestimation of SCD risk by surrogate endpoints like appropriate ICD therapy.
• To highlight the limitations of static risk models and advocate for dynamic risk assessment.

Main Methods:

• Review of existing literature on SCD prediction models in genetic heart diseases.
• Analysis of challenges posed by surrogate endpoints and static risk models.
• Discussion of potential solutions including disease-specific endpoints and dynamic modeling.

Main Results:

• Current surrogate endpoints, such as appropriate ICD therapy, can lead to overestimation of true SCD risk.
• This overestimation may result in unnecessary ICD implantation and associated complications in low-risk individuals.
• Most risk prediction models are static and fail to capture temporal variations in individual risk.

Conclusions:

• There is a critical need for disease-specific surrogate endpoints in genetic heart disease to improve SCD risk prediction accuracy.
• Development and implementation of dynamic risk models are essential to reflect evolving individual risk over time.
• Adopting these advancements will optimize ICD implantation decisions and patient outcomes.