Artificial Intelligence for the Prioritization of Genetic Background in Brugada Syndrome

Brugada Syndrome (BS) is an inherited cardiac electrical disorder that can cause syncope and sudden cardiac arrest in young asymptomatic individuals. It is suspected to contribute to 4-12% of cases of sudden cardiac death in the general population. Diagnosis relies on identifying a type I ECG pattern characterized by ST-segment elevation with a coved morphology in the right precordial leads. The prevalence in Western countries is estimated at 1:5000. Currently, implantation of a cardioverter defibrillator (ICD) is the only treatment option, but risk stratification guidelines remain incomplete, particularly for asymptomatic individuals.

BS is inherited as an autosomal dominant trait with incomplete penetrance. While 23 genes have been associated with BS susceptibility, 70% of patients remain genetically uncharacterized, suggesting a more complex inheritance pattern. Genetics have not been incorporated into risk stratification guidelines, despite evidence linking certain genetic variants to higher arrhythmic risk. This knowledge gap underscores the importance of expanding our understanding of BS genetics to enhance diagnostic sensitivity and patient management.

This protocol builds upon preliminary data from a study granted by the Italian Ministry of Health (GR-2016-02362316), in which next-generation sequencing (NGS) was used to investigate the entire coding regions (Whole Exome Sequencing_WES) of 200 BS patients. The study aimed to identify new BS candidate genes and characterize the genetic basis of the condition.

The cohort was selected based on the presence of a type I ECG, confirmed either spontaneously or induced by flecainide or ajmaline. Patients underwent thorough cardiac evaluations to rule out other conditions. Follow-up included yearly assessments and more frequent evaluations for patients with a higher risk of ventricular tachycardia.

A large number of genetic variants were identified by exploiting WES, prompting the use of Artificial Intelligence (AI) to prioritize the sequencing data. AI techniques, including advanced algorithms and machine learning, can streamline the identification of potentially disease-causing genetic variations by filtering out common variants, predicting pathogenicity, and integrating clinical data.

Given that over 70% of BS patients remain genetically undiagnosed, high-throughput sequencing approaches are crucial for a comprehensive understanding of BS genetics. This study aims to contribute to the identification of new genetic factors and improve risk stratification for affected patients. All sequencing data for this project have been generated and will be analyzed using AI, with no further patients to be enrolled or sequenced.

Inclusion criteria

• The 200 BS patients have been selected and clinically evaluated based on the presence of a type I electrocardiogram (ECG), either spontaneous or induced by flecainide or ajmaline.

Exclusion criteria

• No exclusion criteria are adopted for this study. The entire previously sequenced cohort of 200 BS patients will be investigated and considered, exploiting AI approach for the prioritization of the sequencing available data.