Unmasking Concealed Arrhythmia Syndromes
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults willing to take part
- •Able to give consent
排除标准
- •Unable to give consent
- •Children age < 18 years and adults > 100 years old
结局指标
主要结局
Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
时间窗: 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.
时间窗: 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.
时间窗: 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.
次要结局
未报告次要终点