Clinical trial NCT04641585: Brugada Syndrome and Artificial Intelligence Applications to Diagnosis
Official title: Brugada Syndrome and Artificial Intelligence Applications to Diagnosis
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Brugada Syndrome 1
Lead sponsor: Istituto di Fisiologia Clinica CNR
Sponsor class: OTHER
Collaborators: name: Fondazione Toscana Gabriele Monasterio
name: Azienda USL Toscana Sud Est
name: Azienda USL Toscana Nord Ovest
name: Azienda Ospedaliero-Universitaria Careggi
name: Azienda Ospedaliero, Universitaria Pisana
First posted: 2020-11-24
Enrollment: 144
Sex: ALL
Minimum age (years): 14
Maximum age (years): 65
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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

Primary outcomes:
measure: Machine Learning recognition of Brugada Syndrome 1
timeFrame: Week 20
measure: Machine Learning recognition of Brugada Syndrome 1
timeFrame: Week 20
measure: Machine Learning recognition of Brugada Syndrome 1
timeFrame: Week 20
measure: Machine Learning recognition of Brugada Syndrome 1
timeFrame: Week 20
Secondary outcomes:
measure: Biomarkers associated with Brugada Syndrome 1
timeFrame: week 48
measure: Stratification risk
timeFrame: week 64
Other outcomes:
Locations:
city: Arezzo
country: Italy
facility: Azienda USL Toscana Sud Est - U.O.C Cardiologia
state: Tuscany
city: Firenze
country: Italy
facility: Azienda Ospedaliera Universitaria Careggi - SOD Aritmologia
state: Tuscany
city: Pisa
country: Italy
facility: Azienda Ospedaliero Universitaria Pisana - Cardiologia 2
state: Tuscany
city: Pisa
country: Italy
facility: Fondazione Toscana Gabriele Monasterio
state: Tuscany
city: Pisa
country: Italy
facility: Istituto di Fisiologia Clinica IFC-CNR
state: Tuscany
city: Viareggio
country: Italy
facility: Azienda Usl Toscana Nord Ovest - U.O.C. Cardiologia
state: Tuscany
Study officials:
affiliation: Istituto di Fisiologia Clinica
name: Federico Vozzi, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
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. doi: 10.1016/0735-1097(92)90253-j.
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. doi: 10.1097/MD.0000000000005643.
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.
citation: 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. doi: 10.1016/j.hrthm.2005.01.005.
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. doi: 10.1053/euhj.2002.3382. No abstract available.
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.
citation: Morales MA, Piacenti M, Nesti M, Solarino G, Pieragnoli P, Zucchelli G, Del Ry S, Cabiati M, Vozzi F. The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition. BMC Cardiovasc Disord. 2021 Oct 13;21(1):494. doi: 10.1186/s12872-021-02280-3.