Brugada ECG PatternsUCASNCT06988189
Unmasking Concealed Arrhythmia Syndromes
An observational study of Brugada ECG Patterns and Brugada Syndrome (BrS), run by Imperial College London at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Brugada ECG Patterns, Brugada Syndrome (BrS)
- What is being tested?
- 12-lead ECG, Continuous long term ambulatory ECG monitoring and Ultra-high-frequency ECG
- How many people
- 200 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 9 Sep 2024to Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Adults willing to take part
- Able to give consent
Who cannot join
- Unable to give consent
- Children age < 18 years and adults > 100 years old
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
Imperial College Healthcare NHS Trust
London, W12 0NN
Keenan Saleh, MBBS, 02033132243, keenan.saleh10@imperial.ac.uk
Ahran Arnold, 02033132243, ahran.arnold@imperial.ac.uk
About this study
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.
Read the 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.
- Conditions
- Brugada ECG Patterns, Brugada Syndrome (BrS)
- Being tested
- 12-lead ECG, Continuous long term ambulatory ECG monitoring, Ultra-high-frequency ECG
- Keywords
- Brugada syndrome, Ambulatory ECG, Wearable ECG, Long term continuous monitoring, AI ECG
- Medical subject headings
- Brugada Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
12-lead ECGDiagnostic test
12-lead ECG from a conventional ECG machine
Continuous long term ambulatory ECG monitoringDiagnostic test
Continuous long term ambulatory ECG monitoring using wearable ECG or cardiac monitor
Ultra-high-frequency ECGDiagnostic test
Ultra-high-frequency ECG acquired using specific acquisition equipment
| Study group | Role | What they receive |
|---|---|---|
| Healthy volunteers | Volunteer participants with no cardiac structural or arrhythmic conditions. Receives: Diagnostic Test: 12-lead ECG; Diagnostic Test: Continuous long term ambulatory ECG monitoring; Diagnostic Test: Ultra-high-frequency ECG | |
| Manifest Arrhythmia Syndrome | Manifest arrhythmia syndrome patients (patients with an arrhythmic syndrome with an abnormal ECG) Receives: Diagnostic Test: 12-lead ECG; Diagnostic Test: Continuous long term ambulatory ECG monitoring; Diagnostic Test: Ultra-high-frequency ECG | |
| Concealed Arrhythmia Syndrome | Concealed arrhythmia syndrome patients (patients with a normal ECG with a known underlying arrhythmic diagnosis) Receives: Diagnostic Test: 12-lead ECG; Diagnostic Test: Continuous long term ambulatory ECG monitoring; Diagnostic Test: Ultra-high-frequency ECG |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
Assessment of performance and accuracy of AI ECG detection algorithm for type 1 Brugada ECG.
Measured: At completion of algorithm validation, approximately 12 months after study start
Detection rate of Brugada ECG pattern using extended-duration multi-electrode ambulatory ECG monitoring (wearable ECG) in patients with concealed Brugada syndrome.
AI ECG detection algorithm, developed in Study A, applied to full ECG recording to detect Type 1 Brugada ECG pattern.
Measured: Up to 12 months from enrolment
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.
AI ECG detection algorithms applied to full ECG recording to detect Type 1 Brugada ECG pattern or LQTS unmasking.
Measured: Up to 12 months from enrolment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 May 2025
- Study start9 Sep 2024
- Primary completionNov 2026 (estimated)
- Study completionNov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Brugada ECG PatternsStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Zachary Whinnett, PhD, Study chair, Imperial College London
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06988189
- Sponsor's protocol ID
- REC 24/NS/0032
- Acronym
- UCAS
- Status last verified
- May 2025
- First submitted
- 8 May 2025
- Last updated
- 23 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 28528724
Study at a glance
From the registry record, last updated by the sponsor on 23 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Unmasking Concealed Arrhythmia Syndromes
- ClinicalTrials.gov ID
- NCT06988189
- Acronym / sponsor ID
- UCAS / REC 24/NS/0032
- Conditions
- Brugada ECG Patterns, Brugada Syndrome (BrS)
- Keywords
- Brugada syndrome, Ambulatory ECG, Wearable ECG, Long term continuous monitoring, AI ECG
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Study start
- 9 Sep 2024 (actual)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Nov 2026 (estimated)
- First posted
- 23 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Healthy volunteers | – | Diagnostic Test: 12-lead ECG, Diagnostic Test: Continuous long term ambulatory ECG monitoring, Diagnostic Test: Ultra-high-frequency ECGVolunteer participants with no cardiac structural or arrhythmic conditions. |
| Manifest Arrhythmia Syndrome | – | Diagnostic Test: 12-lead ECG, Diagnostic Test: Continuous long term ambulatory ECG monitoring, Diagnostic Test: Ultra-high-frequency ECGManifest arrhythmia syndrome patients (patients with an arrhythmic syndrome with an abnormal ECG) |
| Concealed Arrhythmia Syndrome | – | Diagnostic Test: 12-lead ECG, Diagnostic Test: Continuous long term ambulatory ECG monitoring, Diagnostic Test: Ultra-high-frequency ECGConcealed arrhythmia syndrome patients (patients with a normal ECG with a known underlying arrhythmic diagnosis) |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Adults willing to take part
- Able to give consent
Key exclusion criteria (2 listed)
- Unable to give consent
- Children age < 18 years and adults > 100 years old
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 12-lead ECG12-lead ECG from a conventional ECG machine | Diagnostic test | Concealed Arrhythmia Syndrome, Healthy volunteers, Manifest Arrhythmia Syndrome |
| Continuous long term ambulatory ECG monitoringContinuous long term ambulatory ECG monitoring using wearable ECG or cardiac monitor | Diagnostic test | Concealed Arrhythmia Syndrome, Healthy volunteers, Manifest Arrhythmia Syndrome |
| Ultra-high-frequency ECGUltra-high-frequency ECG acquired using specific acquisition equipment | Diagnostic test | Concealed Arrhythmia Syndrome, Healthy volunteers, Manifest Arrhythmia Syndrome |
Primary outcome
Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 May 2025.
- Background: PubMed 28528724Gray 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Zachary Whinnett, PhD study chair
Imperial College London
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Brugada ECG Patterns and Brugada Syndrome (BrS), run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 23 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Keenan Saleh, MBBS (+442033132243, keenan.saleh10@imperial.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs. (time frame: At completion of algorithm validation, approximately 12 months after study start). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06988189, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 May 2025.