Brugada Syndrome and Artificial Intelligence Applications to Diagnosis

Not Applicable

• Conditions: Brugada Syndrome 1
• Interventions: Diagnostic Test: Patients affected by Brugada Syndrome 1

• Registration Number: NCT04641585
• Lead Sponsor: Istituto di Fisiologia Clinica CNR

Brief Summary

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

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.

Study Timeline

• Study First Submitted: 2020-10-22
• Status Verified: 2020-11
• Study First Submitted QC: 2020-11-17
• Last Update Submitted: 2020-11-17
• Study First Posted: 2020-11-24
• Last Update Posted: 2020-11-24
• Study Start: 2021-01-15
• Primary Completion: 2023-03-15
• Study Completion: 2023-09-15

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 144

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Patients affected by Brugada Syndrome 1	Patients affected by Brugada Syndrome 1	Patients with spontaneous or drug-induced Brugada Syndrome 1
Controls	Patients affected by Brugada Syndrome 1	Patients with no condition associated with spontaneous or drug-induced Brugada Syndrome 1

Primary Outcome Measures

Name	Time	Method
Machine Learning recognition of Brugada Syndrome 1	Week 20	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

Secondary Outcome Measures

Name	Time	Method
Stratification risk	week 64	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).
Biomarkers associated with Brugada Syndrome 1	week 48	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)

Trial Locations

Locations (6)

Azienda Ospedaliero Universitaria Pisana - Cardiologia 2; 🇮🇹 Pisa, Tuscany, Italy

Istituto di Fisiologia Clinica IFC-CNR; 🇮🇹 Pisa, Tuscany, Italy

Azienda Usl Toscana Nord Ovest - U.O.C. Cardiologia; 🇮🇹 Viareggio, Tuscany, Italy

Fondazione Toscana Gabriele Monasterio; 🇮🇹 Pisa, Tuscany, Italy

Azienda Ospedaliera Universitaria Careggi - SOD Aritmologia; 🇮🇹 Firenze, Tuscany, Italy

Azienda USL Toscana Sud Est - U.O.C Cardiologia; 🇮🇹 Arezzo, Tuscany, Italy