The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition

Area of Science:

• Cardiology
• Genetics
• Computational Biology

Background:

• Type 1 Brugada syndrome (BrS) is a genetic arrhythmogenic disease characterized by specific ECG abnormalities and risk of sudden cardiac death.
• Diagnostic challenges arise from high variability in individual ECG patterns.
• Accurate diagnosis is crucial for risk stratification and management.

Purpose of the Study:

• To develop an innovative diagnostic system for Type 1 Brugada syndrome (BrS).
• To leverage Machine Learning (ML) for ECG pattern recognition.
• To integrate transcriptomic analysis of blood markers for enhanced diagnostic accuracy.

Main Methods:

• Retrospective analysis of 300 ECGs (BrS patients and controls) using ML for pattern recognition.
• Prospective study involving ECG ML analysis and transcriptomic/microvesicle analysis in 44 patients across different BrS categories and controls.
• Validation study with 100 patients to test the ML algorithm and identified biomarkers.

Main Results:

• The study is designed to establish the efficacy of the BrAID system in improving Type 1 BrS diagnosis.
• Integration of ECG, clinical, and biochemical data is expected to enhance diagnostic precision.
• The system aims to reduce the time from ECG recording to diagnosis.

Conclusions:

• The developed BrAID system is anticipated to improve clinical diagnosis of Type 1 BrS.
• The integration of ML and transcriptomics offers a novel approach to diagnosing this arrhythmogenic disease.
• This system promises more effective resource utilization in diagnosing BrS.