Brugada Syndrome and Artificial Intelligence Applications to Diagnosis

Study Overview
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Brief Summary
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Aim of the project is the development of an integrated platform, based on machine learning and omic techniques, able to support physicians in as much as possible accurate diagnosis of Type 1 Brugada Syndrome (BrS).

Detailed Description
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The aim of BrAID project is to integrate classic clinical guidelines for Brugada Syndrome 1 diagnosis evaluation with innovative Information and Communication Technologies and omic approaches, generating new diagnostic strategies in cardiovascular precision medicine of this disease.

Official Title
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Brugada Syndrome and Artificial Intelligence Applications to Diagnosis

Conditions
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Brugada Syndrome 1

Intervention / Treatment
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* Diagnostic Test: Patients affected by Brugada Syndrome 1


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Brugada patients: patients with Brugada Syndrome 1 spontaneous or induced by the ajmaline test; patients with non-diagnostic electrocardiographic pattern for Brugada Syndrome 1 or negative in the presence of high clinical suspicion (family history for Brugada Syndrome, patients who survived cardiac arrest without organic heart disease) Control patients: patients with frequent premature ventricular complex and normal left and right ventricular function; patients with suspected Brugada Syndrome 1 not confirmed by ajmaline test Exclusion Criteria: organic heart disease or diseases interfering with protocol completion lack of signed informed consent pregnancy acute coronary artery disease, heart failure in the previous 3 months severe renal or liver failure

Ages Eligible for Study
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Minimum Age: 14 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Patients affected by Brugada Syndrome 1<br>Patients with spontaneous or drug-induced Brugada Syndrome 1 | Diagnostic Test: Patients affected by Brugada Syndrome 1<br>* ECG analysis by Machine Learning algorithms and blood collection for the transcriptomic study of markers possibly associated with the disease<br>|
| Active Comparator: Controls<br>Patients with no condition associated with spontaneous or drug-induced Brugada Syndrome 1 | Diagnostic Test: Patients affected by Brugada Syndrome 1<br>* ECG analysis by Machine Learning algorithms and blood collection for the transcriptomic study of markers possibly associated with the disease<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Machine Learning recognition of Brugada Syndrome 1 | Identification of Brugada type 1 Syndrome coved ST component in a cohort of 44 patients (prospective study) and validated in a cohort of 100 patients (validation study) according to the diagnostic patterns related to Brugada Syndrome 1 on 12-leads ECG as already published on current international guidelines | Week 20 | 
| Machine Learning recognition of Brugada Syndrome 1 | Identification of Brugada type 1 Syndrome QRS fragmentation component in a cohort of 44 patients (prospective study) and validated in a cohort of 100 patients (validation study) according to the diagnostic patterns related to Brugada Syndrome 1 on 12-leads ECG as already published on current international guidelines | Week 20 | 
| Machine Learning recognition of Brugada Syndrome 1 | Identification and characterization of Brugada type 1 Syndrome T segment depression component in a cohort of 44 patients (prospective study) and validated in a cohort of 100 patients (validation study) according to the diagnostic patterns related to Brugada Syndrome 1 on 12-leads ECG as already published on current international guidelines | Week 20 | 
| Machine Learning recognition of Brugada Syndrome 1 | Identification of Brugada type 1 Syndrome broad P wave with PQ prolongation component in a cohort of 44 patients (prospective study) and validated in a cohort of 100 patients (validation study) according to the diagnostic patterns related to Brugada Syndrome 1 on 12-leads ECG as already published on current international guidelines | Week 20 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Biomarkers associated with Brugada Syndrome 1 | Identification of biomarkers associated with Brugada Syndrome 1 by the means of blood transcriptomic profile and exosomes analysis of patients. Transcriptomic and exosome could provide new insight into the pathophysiology of signalling in this pathology, as well as for application in Brugada Syndrome 1 diagnosis and therapeutics. Transcriptomic will provide a global picture of phenotypical changes associated with the disease, highlighting the potential genes involved in the development of Brugada Syndrome 1 The analysis of exosome coding and noncoding RNAs, participating in a variety of basic cellular functions, could also evidence potentially important pathophysiologic effects both in cardiac cells as well as on the release of electrical stimuli. The study will be performed in a cohort of 44 patients (prospective study) and results will be validated in a cohort of 100 patients (validation study) | week 48 | 
| Stratification risk | Development of stratification risk system for Brugada type 1 Syndrome by the integration of ECG Machine Learning algorithms and biomarkers. In particular, the module will combine the peculiar ECG patterns associated with BrS (coved ST, QRS fragmentation, T segment depression, broad P wave with PQ prolongation)(outcome 1-4) and omic (genes) and exosome markers (coding and noncoding RNAs)(outcome 5) with the aim to improve patient risk stratification. Specifically, gene expression modulation (expressed as % respect to control population) of Na+ (e.g., Nav1.5, Nav1.3, Nav2.1), Ca2+ (e.g. Cav3.1, HCN3) and K+ channels (e.g.,TWIK1, Kv4.3) will be evaluated. The study will be performed in a cohort of 44 patients (prospective study) and results will be validated in a cohort of 100 patients (validation study). | week 64 |