Mechanism-informed Prediction and Prevention of Sudden Cardiac Death: Bridging Electrophysiology, Data Science and Critical Care

Sudden cardiac death (SCD) caused by malignant ventricular arrhythmias remains a leading cause of out-of-hospital mortality, with survival to hospital discharge still rare. It affects patients with inherited channelopathies—including long QT syndrome, short QT syndrome, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, and early repolarization—as well as those with ischaemic and non-ischaemic cardiomyopathies. Although arrhythmic substrates can increasingly be characterized, current tools remain limited in their ability to predict which individuals will experience cardiac arrest or respond to a particular therapy. Emerging studies using mechanistic models, advanced electrocardiographic analysis, and machine learning have improved risk assessment, yet their translation into clinical decision-making remains incomplete.This Research Topic aims to connect mechanistic electrophysiology with clinical care to improve SCD prediction and prevention. It focuses on early identification of arrhythmic risk and goal-directed therapies that integrate physiological insights—including ion-channel dynamics, depolarization and repolarization biomarkers, heart-rate behaviour, and autonomic influences—with validated data-driven models. These approaches may inform pharmacological, gene-directed, and, where appropriate, device-based treatment strategies. The Research Topic also seeks to strengthen the post-arrest chain of survival, from bystander recognition and resuscitation to intensive care, neurological recovery, and secondary prevention.As part of a coordinated two-part series on SCD, this Research Topic focuses on the biological, mechanistic, and predictive foundations of arrhythmic risk, together with acute and post-arrest care. It complements a companion Research Topic dedicated to ICD/CRT-D selection and optimization, implantation strategies, CIED infection prevention, and post-implantation management. Manuscripts focused primarily on device comparison, procedural infection prevention, lead extraction, or CIED surveillance should therefore be submitted here Sudden Cardiac Death Prevention and ICD Optimization: Risk Stratification, Implantation, and Infection Avoidance.To deepen understanding across this continuum, we welcome articles addressing, but not limited to:• Predictors of ventricular arrhythmia and SCD in inherited and acquired heart disease• Mechanistic and translational models linking ion-channel behaviour, tissue electrophysiology, and substrate architecture to arrhythmic risk• Hybrid physiological and machine-learning approaches for individualized risk stratification, including external validation and assessment of clinical utility• Risk-model-guided selection of pharmacological, gene-directed, and device-based interventions• Advanced ECG, imaging, wearable, and autonomic biomarkers of ventricular arrhythmia and SCD• Acute and post-arrest resuscitation strategies and intensive care pathways that improve neurological outcomes• Long-term secondary prevention and recovery following ventricular arrhythmia or cardiac arrest• System-level, public health, and ethical aspects of equitable rhythm-care delivery and post-arrest survivalThe scope of this Research Topic includes all article types except Case Reports. Ultimately, the aim is to bridge bench-to-bedside insights in cardiac electrophysiology by translating mechanistic understanding and novel risk prediction into effective, patient-tailored SCD prevention and post-arrest care. Topic editors: Michael Brunner, Faisal F Syed Source: https://www.frontiersin.org/research-topics/83775/mechanism-informed-prediction-and-prevention-of-sudden-cardiac-death-bridging-electrophysiology-data-science-and-critical-care