Sudden cardiac arrest: Limitations in risk-stratification and treatment, and the potential for digital technologies...

Sudden cardiac death (SCD) remains a pervasive public health challenge, accounting for a significant proportion of cardiac and all-cause mortality worldwide. Despite notable advancements in cardiovascular therapies and reductions in overall cardiac mortality, survival following sudden cardiac arrest (SCA) remains dismally low, and prediction strategies remain inadequate. This comprehensive review examines the current landscape of SCD etiologies and the latest guidelines for primary and secondary prevention of SCD with implantable cardioverter defibrillators (ICDs). Particular attention is given to the limitations of left ventricular ejection fraction (LVEF) as the primary tool for risk stratification, given its low sensitivity, specificity, and limited applicability to the broader population in which most SCDs occur. Emerging risk scores and machine learning (ML) driven prediction models have begun to efficiently integrate clinical, electrical, imaging, genetic and laboratory parameters to improve SCD risk stratification. This review highlights examples of such artificial intelligence (AI) prediction models and discusses their potential role in the near-term and long-term prediction of SCD in both in-hospital and out-of-hospital settings, while emphasizing the need for external validation of such models. The review also discusses critical system-level gaps in the chain of survival from cardiac arrest, particularly the need for automated emergency medical services (EMS) activation, community responder engagement, high-quality cardiopulmonary resuscitation (CPR) and improved access to defibrillation. It explores the role of digital technologies such as wearable sensors, smartwatches, smartphone applications and implantable devices in improving real-time SCA detection and enhancing early aspects of the chain of survival from cardiac arrest. Finally, the review calls for a multidisciplinary, multi-sectoral approach including regulatory, technological, and public health stakeholders to bridge gaps in SCD prevention, detection, and response.