Brugada ECG Patterns, Brugada Syndrome (BrS)
Conditions
Keywords
Brugada syndrome, Ambulatory ECG, Wearable ECG, Long term continuous monitoring, AI ECG
Brief summary
This study seeks to evaluate whether using non-invasive electrocardiograph (ECG) techniques, including long term ECG monitoring with wearable ECGs, can improve the detection of concealed Brugada syndrome.
Detailed description
Application of long term continuous ECG monitoring via ECG wearables and ambulatory ECG monitors to detect manifestations of Brugada syndrome. This approach will be combined with development of an AI (artificial intelligence) enabled ECG platform to automate Brugada ECG detection and analysis. The protocol will comprise the following parts: Study A: Brugada ECG AI development. This will automate the recognition of the type 1 Brugada ECG pattern on 12 lead ECGs. Study B: Remote arrhythmia diagnostics. A prospective observational study whereby recruited participants will be fitted with a wearable ECG or cardiac monitor to undergo continuous long term ambulatory ECG monitoring. The algorithms developed in study A will be applied to long term ECG data captured in this study. Study C: Arrhythmic risk stratification using ultra-high-frequency ECG. This exploratory study will look for markers of arrhythmic risk in patients with manifest and concealed arrhythmia syndromes.
Interventions
DIAGNOSTIC_TEST12-lead ECG
12-lead ECG from a conventional ECG machine
DIAGNOSTIC_TESTContinuous long term ambulatory ECG monitoring
Continuous long term ambulatory ECG monitoring using wearable ECG or cardiac monitor
DIAGNOSTIC_TESTUltra-high-frequency ECG
Ultra-high-frequency ECG acquired using specific acquisition equipment
Sponsors
Imperial College London
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes
Inclusion criteria
* Adults willing to take part * Able to give consent
Exclusion criteria
* Unable to give consent * Children age \< 18 years and adults \> 100 years old
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs. | At completion of algorithm validation, approximately 12 months after study start | Assessment of performance and accuracy of AI ECG detection algorithm for type 1 Brugada ECG. |
| Detection rate of Brugada ECG pattern using extended-duration multi-electrode ambulatory ECG monitoring (wearable ECG) in patients with concealed Brugada syndrome. | Up to 12 months from enrolment | AI ECG detection algorithm, developed in Study A, applied to full ECG recording to detect Type 1 Brugada ECG pattern. |
| Number of cases of Brugada or Long QT Syndrome (LQTS) detected using extended-duration multi-electrode ambulatory ECG monitoring in patients with idiopathic ventricular fibrillation (VF), after application of AI ECG detection algorithms. | Up to 12 months from enrolment | AI ECG detection algorithms applied to full ECG recording to detect Type 1 Brugada ECG pattern or LQTS unmasking. |
Countries
United Kingdom
Contacts
Primary ContactKeenan Saleh, MBBS
Backup ContactAhran Arnold, PhD
Outcome results
None listed