Innovative Approaches for Predicting and Reducing Sudden Cardiac Death in Early-Stage Cardiomyopathy Patients with Well-Maintained Systolic Ventricular Function

Cardiomyopathies, including ischemic and non-ischemic subtypes, are a critical clinical concern, as they significantly contribute to the global burden of sudden cardiac death (SCD), particularly when diagnosed in the early stages without severe left ventricular dysfunction. Despite substantial improvements in the epidemiological trends of SCD, early-stage cardiomyopathy patients continue to pose clinical challenges regarding reliable risk stratification and choosing effective preventive strategies. Current research highlights limitations within existing prognostic models that predominantly rely on traditional non-invasive imaging and clinical parameters, which alone may not sufficiently identify individuals at the highest risk. Furthermore, the inadequacies observed in recently explored artificial intelligence-driven imaging risk prediction approaches, such as the PROFID study, emphasize the complexity and multifactorial nature involved in accurately anticipating arrhythmic risks.

Recent studies suggest a promising integration of combined invasive and non-invasive diagnostic strategies to enhance prediction accuracy in early cardiomyopathy patients. Nevertheless, consensus regarding the appropriate integration of invasive electrophysiological (EP) studies alongside advances in genetics, advanced imaging modalities, and non-invasive electrophysiological parameters remains incomplete and warrants further exploration. Crucial gaps persist, particularly regarding optimal techniques to prospectively stratify risk in asymptomatic or minimally symptomatic patients, thus impeding timely and tailored therapeutic interventions.

This Research Topic aims to uncover emerging evidence, validate innovative electrocardiographic and imaging diagnostic modalities along with important clinical history data (i.e. SCD and/or syncope history in the family, medications predisposing to malignant ventricular arrhythmias, etc.) and establish refined and personalized risk stratification models that enable clinicians to better identify and manage patients with early-stage ischemic or non-ischemic cardiomyopathy at risk for SCD. Specific objectives involve assessing novel predictive technologies, validating comprehensive integrative risk assessments, and critically examining current therapeutic methods—including intracardiac ablation, device-related strategies (ICDs or CRT), and emerging technologies such as wearables.

To gather further insights into improving early risk identification, preventive therapeutic decisions, and patient outcomes for sudden cardiac death in early-stage cardiomyopathy, we welcome articles addressing, but not limited to, the following themes:
• Advanced applications of non-invasive imaging modalities, such as echocardiography, cardiac MRI, and CT, for SCD risk profiling.
• Novel genetic markers, molecular and circulating biomarkers, and their predictive role in early-stage cardiomyopathy.
• Comparative effectiveness of invasive electrophysiological studies and their complementary role in enhancing non-invasive risk stratification procedures.
• Exploration of pathophysiological differences and associated risks between ischemic and non-ischemic cardiomyopathy regarding SCD.
• Integration of machine learning, artificial intelligence, and emerging computational techniques into comprehensive predictive frameworks.
• Evaluation of the efficacy, safety, and quality-of-life outcomes related to therapeutic interventions, including device implantation (ICD, CRT), ablation therapies, and wearable monitoring technologies.

We welcome all article types that contribute to our understanding of this critical area, including original research, systematic reviews, meta-analyses, clinical trials, methodological studies, and perspective articles regarding new diagnostic and therapeutic avenues.