Key result
BrAID evaluates machine learning and transcriptomics to improve Type 1 Brugada syndrome diagnosis.
Why the study?
Diagnosis of Type 1 Brugada syndrome is problematic due to high variability in individual ECG patterns despite known characteristic features.
Does an innovative system integrating machine learning for ECG pattern recognition and transcriptomics accurately diagnose Type 1 Brugada syndrome?
Does an innovative system integrating machine learning for ECG pattern recognition and transcriptomics accurately diagnose Type 1 Brugada syndrome?
The BrAID study protocol outlines a multi-phase approach to develop a machine learning and transcriptomics-based system for improved and timely diagnosis of Type 1 Brugada syndrome.
May aid Type 1 Brugada diagnosis if validated; leaves open accuracy of ML-transcriptomics integration.
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthermore, although various ECG patterns are described in the literature, different individual ECG may show high-grade variability, making the diagnosis problematic. The study aims to develop an innovative system for an accurate diagnosis of Type 1 BrS based on ECG pattern recognition by Machine Learning (ML) models and blood markers analysis trough transcriptomic techniques. METHODS: The study is structured in 3 parts: (a) a retrospective study, with the first cohort of 300 anonymized ECG obtained in already diagnosed Type 1 BrS (75 spontaneous, 150 suspected) and 75 from control patients, which will be processed by ML analysis for pattern recognition; (b) a prospective study, with a cohort of 11 patients with spontaneous Type 1 BrS, 11 with drug-induced Type 1 BrS, 11 suspected BrS but negative to Na + channel blockers administration, and 11 controls, enrolled for ECG ML analysis and blood collection for transcriptomics and microvesicles analysis; (c) a validation study, with the third cohort of 100 patients (35 spontaneous and 35 drug-induced BrS, 30 controls) for ML algorithm and biomarkers testing. DISCUSSION: The BrAID system will help clinicians improve the diagnosis of Type 1 BrS by using multiple information, reducing the time between ECG recording and final diagnosis, integrating clinical, biochemical and ECG information thus favoring a more effective use of available resources. Trial registration Clinical Trial.gov, NCT04641585. Registered 17 November 2020, https://clinicaltrials.gov/ct2/show/NCT04641585.
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Morales et al. (2021) studied Type 1 Brugada syndrome (n=444). Machine Learning ECG analysis and transcriptomics vs. Diagnosis made by expert Cardiologists was evaluated on Percentage of identification of Type 1 BrS ECG pattern with ML models compared to the diagnosis made by expert Cardiologists. The BrAID study is a planned multicenter protocol that will evaluate the integration of machine learning for ECG pattern recognition and transcriptomics to improve the diagnosis of Type 1 Brugada syndrome.
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