Prevention of Stroke and Sudden Cardiac Death by Recording of 1-Channel Electrocardiograms
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Atrial Fibrillation
- Atrial Premature Complexes
- Sinus Rhythm
- Ventricular Premature Complexes
- Ventricular Tachycardia, Nonsustained
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Between 18 years and 85 years
- Gender
- Both males and females
Description
In phase 1 of a research project titled 'Prevention of stroke and sudden cardiac death by Recording of 1-Channel Electrocardiograms' (PRICE), a total of 100,000 30-sec single-channel ECGs (lead I of 12-lead surface ECG) will be collected from approximately 10,000 subjects/patients at 11 participatin...
In phase 1 of a research project titled 'Prevention of stroke and sudden cardiac death by Recording of 1-Channel Electrocardiograms' (PRICE), a total of 100,000 30-sec single-channel ECGs (lead I of 12-lead surface ECG) will be collected from approximately 10,000 subjects/patients at 11 participating clinical centers in Germany. Relevant baseline clinical patient characteristics will also be recorded. The ECGs, diagnosed by an experienced electrophysiologist (diagnostic gold standard), will be fed into an Artificial Intelligence (AI) for the automatic detection of normal sinus rhythm (SR), atrial fibrillation (AF), atrial premature beats (APBs), ventricular premature beats (VPBs), and nonsustained ventricular tachycardia (VT). It is expected that the overall diagnostic accuracy of the AI against an experienced electrophysiologist will be on the order of 95%. In PRICE phase 2, ECG diagnosis by the AI will be compared with the diagnosis by 3 general cardiologists of the same ECGs. It is expected that the AI will surpass the general cardiologists in terms of diagnostic accuracy. The final clinical phase of the PRICE project will comprise a randomized controlled community trial of risk patients to establish the superiority in stroke prevention of AI detection of AF on smart-watch ECGs vs. no AF detection.
Tracking Information
- NCT #
- NCT04637230
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Karl-Heinz Kuck, MD LANS Cardio, Hamburg, Germany