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

M A Morales¹, M Piacenti², M Nesti³

¹CNR Institute of Clinical Physiology, Via Giuseppe Moruzzi 1, 56124, Pisa, Italy.

BMC Cardiovascular Disorders

|October 14, 2021

Summary

This study introduces an innovative system using Machine Learning (ML) and transcriptomics to improve the diagnosis of Type 1 Brugada syndrome (BrS), a condition linked to sudden cardiac death.

Keywords:

Brugada syndrome Machine learning RNA Transcriptomic

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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.