Beyond the type 1 pattern: comprehensive risk stratification in Brugada syndrome

Area of Science:

• Cardiology
• Genetics
• Medical Diagnostics

Background:

• Brugada Syndrome (BrS) is an inherited cardiac ion channelopathy causing sudden cardiac death from ventricular arrhythmias.
• Diagnosis relies on Type 1 ECG patterns, but risk stratification in asymptomatic patients is difficult.

Purpose of the Study:

• To review current and emerging methods for Brugada Syndrome risk stratification.
• To highlight the importance of an integrated, multimodal approach for improved patient outcomes.

Main Methods:

• Review of electrocardiographic (ECG), electrophysiological, imaging, and computational markers.
• Analysis of non-invasive ECG indicators, adjunctive diagnostic tools, and advanced imaging modalities.
• Inclusion of invasive electrophysiological studies, risk scoring systems, and machine learning models.

Main Results:

• ECG markers (e.g., β-angle, fragmented QRS) and dynamic tests predict arrhythmic events.
• Imaging reveals subclinical structural abnormalities, challenging the purely electrical disorder concept.
• Risk scores and machine learning offer personalized risk assessment.

Conclusions:

• An integrated, multimodal strategy is crucial for accurate BrS risk stratification.
• Optimized risk assessment guides implantable cardioverter-defibrillator decisions and improves outcomes in high-risk patients.