Prediction of Sudden Cardiac Death: Looking Beyond Ejection Fraction

Area of Science:

• Cardiology
• Public Health
• Medical Technology

Background:

• Sudden cardiac death (SCD) accounts for a significant portion of global mortality.
• Current SCD prevention guidelines primarily focus on low left ventricular ejection fraction.
• A majority of SCD events occur in individuals not meeting these criteria.

Purpose of the Study:

• To review contemporary tools for predicting sudden cardiac death.
• To identify gaps and opportunities in SCD prediction and prevention strategies.
• To highlight emerging methods for risk stratification.

Main Methods:

• Review of advancements in non-invasive and invasive SCD risk prediction measures.
• Inclusion of clinical morbidities, electrocardiographic measures, cardiac imaging, biomarkers, genetics, and electrophysiological characteristics.
• Exploration of novel approaches like machine learning, competing risk analysis, and computational modeling.

Main Results:

• Significant progress in various SCD risk prediction measures.
• Emerging methods offer potential for improved prediction in at-risk populations.
• Novel approaches pave the way for personalized risk prediction.

Conclusions:

• Sudden cardiac death remains a critical public health challenge.
• New methods can enhance SCD prediction for individuals outside current guideline criteria.
• Future research should focus on multi-parametric models for accurate SCD risk identification.