Unmasking Concealed Arrhythmia Syndromes

Recruiting

• Conditions: Brugada ECG Patterns; Brugada Syndrome (BrS)

• Registration Number: NCT06988189
• Lead Sponsor: Imperial College London

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.

Study Timeline

• Study Start: 2024-09-09
• Status Verified: 2025-05
• Study First Submitted: 2025-05-08
• Study First Submitted QC: 2025-05-16
• Last Update Submitted: 2025-05-16
• Study First Posted: 2025-05-23
• Last Update Posted: 2025-05-23
• Primary Completion: 2026-11
• Study Completion: 2026-11

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 200

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Imperial College Healthcare NHS Trust; 🇬🇧 London, United Kingdom