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).
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 Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
144
ECG analysis by Machine Learning algorithms and blood collection for the transcriptomic study of markers possibly associated with the disease
Azienda USL Toscana Sud Est - U.O.C Cardiologia
Arezzo, Tuscany, Italy
Azienda Ospedaliera Universitaria Careggi - SOD Aritmologia
Florence, Tuscany, Italy
Azienda Ospedaliero Universitaria Pisana - Cardiologia 2
Pisa, Tuscany, Italy
Fondazione Toscana Gabriele Monasterio
Pisa, Tuscany, Italy
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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: Week 20
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)
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
Istituto di Fisiologia Clinica IFC-CNR
Pisa, Tuscany, Italy
Azienda Usl Toscana Nord Ovest - U.O.C. Cardiologia
Viareggio, Tuscany, Italy
Time frame: 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).
Time frame: week 64