Health TrialsbyHealthPanel

Unmasking Concealed Arrhythmia Syndromes

ConditionsBrugada ECG PatternsBrugada Syndrome (BrS)
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-100
SponsorImperial College London

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Adults willing to take part

Able to give consent

Disqualifiers

Unable to give consent

Children age < 18 years and adults > 100 years old

Trial 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.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • 12-lead ECG

    Diagnostic test

    12-lead ECG from a conventional ECG machine

  • Continuous long term ambulatory ECG monitoring

    Diagnostic test

    Continuous long term ambulatory ECG monitoring using wearable ECG or cardiac monitor

  • Ultra-high-frequency ECG

    Diagnostic test

    Ultra-high-frequency ECG acquired using specific acquisition equipment

Treatment groups

200 Participants
are divided into 3 treatment groups
Group A: Healthy volunteers3 interventions
Group B: Manifest Arrhythmia Syndrome3 interventions
Group C: Concealed Arrhythmia Syndrome3 interventions

Trial outcomes

Primary outcomes

1

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.

Time frame
At completion of algorithm validation, approximately 12 months after study start
2

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.

Time frame
Up to 12 months from enrolment
3

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.

Time frame
Up to 12 months from enrolment

Secondary outcomes

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Imperial College London

Lead sponsor

Source ClinicalTrials.gov
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