Clinical trial NCT06988189: Unmasking Concealed Arrhythmia Syndromes
Official title: Unmasking Concealed Arrhythmia Syndromes
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Brugada ECG Patterns
Brugada Syndrome (BrS)
Keywords: Brugada syndrome
Ambulatory ECG
Wearable ECG
Long term continuous monitoring
AI ECG
Lead sponsor: Imperial College London
Sponsor class: OTHER
First posted: 2025-05-23
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Healthy volunteer controls, patients with a diagnosis of Brugada syndrome, patients with a diagnosis of idiopathic VF syndrome and patients with other inherited arrhythmogenic conditions.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
timeFrame: At completion of algorithm validation, approximately 12 months after study start
measure: Detection rate of Brugada ECG pattern using extended-duration multi-electrode ambulatory ECG monitoring (wearable ECG) in patients with concealed Brugada syndrome.
timeFrame: Up to 12 months from enrolment
measure: 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.
timeFrame: Up to 12 months from enrolment
Secondary outcomes:
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Imperial College Healthcare NHS Trust
Study officials:
affiliation: Imperial College London
name: Zachary Whinnett, PhD
role: STUDY_CHAIR
References:
citation: Gray B, Kirby A, Kabunga P, Freedman SB, Yeates L, Kanthan A, Medi C, Keech A, Semsarian C, Sy RW. Twelve-lead ambulatory electrocardiographic monitoring in Brugada syndrome: Potential diagnostic and prognostic implications. Heart Rhythm. 2017 Jun;14(6):866-874. doi: 10.1016/j.hrthm.2017.02.026.