Brugada Syndrome 1 Clinical Trial
— BrAIDOfficial title:
Brugada Syndrome and Artificial Intelligence Applications to Diagnosis
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).
| Status | Not yet recruiting |
| Enrollment | 144 |
| Est. completion date | September 15, 2023 |
| Est. primary completion date | March 15, 2023 |
| Accepts healthy volunteers | Accepts Healthy Volunteers |
| Gender | All |
| Age group | 14 Years to 65 Years |
| Eligibility | 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 |
| Country | Name | City | State |
|---|---|---|---|
| Italy | Azienda USL Toscana Sud Est - U.O.C Cardiologia | Arezzo | Tuscany |
| Italy | Azienda Ospedaliera Universitaria Careggi - SOD Aritmologia | Firenze | Tuscany |
| Italy | Azienda Ospedaliero Universitaria Pisana - Cardiologia 2 | Pisa | Tuscany |
| Italy | Fondazione Toscana Gabriele Monasterio | Pisa | Tuscany |
| Italy | Istituto di Fisiologia Clinica IFC-CNR | Pisa | Tuscany |
| Italy | Azienda Usl Toscana Nord Ovest - U.O.C. Cardiologia | Viareggio | Tuscany |
| Lead Sponsor | Collaborator |
|---|---|
| Istituto di Fisiologia Clinica CNR | Azienda Ospedaliero, Universitaria Pisana, Azienda Ospedaliero-Universitaria Careggi, Azienda USL Toscana Nord Ovest, Azienda USL Toscana Sud Est, Fondazione Toscana Gabriele Monasterio |
Italy,
Antzelevitch C, Brugada P, Borggrefe M, Brugada J, Brugada R, Corrado D, Gussak I, LeMarec H, Nademanee K, Perez Riera AR, Shimizu W, Schulze-Bahr E, Tan H, Wilde A. Brugada syndrome: report of the second consensus conference. Heart Rhythm. 2005 Apr;2(4):429-40. Review. Erratum in: Heart Rhythm. 2005 Aug;2(8):905. — View Citation
Brugada P, Brugada J. Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome. A multicenter report. J Am Coll Cardiol. 1992 Nov 15;20(6):1391-6. — View Citation
Quan XQ, Li S, Liu R, Zheng K, Wu XF, Tang Q. A meta-analytic review of prevalence for Brugada ECG patterns and the risk for death. Medicine (Baltimore). 2016 Dec;95(50):e5643. — View Citation
Sarquella-Brugada G, Campuzano O, Arbelo E, Brugada J, Brugada R. Brugada syndrome: clinical and genetic findings. Genet Med. 2016 Jan;18(1):3-12. doi: 10.1038/gim.2015.35. Epub 2015 Apr 23. Review. — View Citation
Vutthikraivit W, Rattanawong P, Putthapiban P, Sukhumthammarat W, Vathesatogkit P, Ngarmukos T, Thakkinstian A. Worldwide Prevalence of Brugada Syndrome: A Systematic Review and Meta-Analysis. Acta Cardiol Sin. 2018 May;34(3):267-277. doi: 10.6515/ACS.201805_34(3).20180302B. Erratum in: Acta Cardiol Sin. 2019 Mar;35(2):192. — View Citation
Wilde AA, Antzelevitch C, Borggrefe M, Brugada J, Brugada R, Brugada P, Corrado D, Hauer RN, Kass RS, Nademanee K, Priori SG, Towbin JA; Study Group on the Molecular Basis of Arrhythmias of the European Society of Cardiology. Proposed diagnostic criteria for the Brugada syndrome. Eur Heart J. 2002 Nov;23(21):1648-54. Review. — View Citation
| Type | Measure | Description | Time frame | Safety issue |
|---|---|---|---|---|
| Primary | 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 | |
| Primary | 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 | |
| Primary | 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 | |
| Primary | 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 | 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 | |
| Secondary | 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 |