Prevention of Stroke and Sudden Cardiac Death by Recording of 1-Channel Electrocardiograms (PRICE)

Single-channel electrocardiograms (lead I of 12-lead surface ECG; 30 seconds) will be collected from subjects/patients at 11 clinical centers in Germany to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms. Heart rhythms of interest are normal sinus rhythm (SR), atrial fibrillation (AF), atrial premature beats (APBs), ventricular premature beats (VPBs), and nonsustained ventricular tachycardia (VT). Per diagnosis, 20,000 ECGs are required, for a total of 100,000 ECGs to be obtained from approximately 10,000 subjects/patients.

Study Overview

• Status: Recruiting
• Conditions: Atrial Fibrillation; Ventricular Premature Complexes; Atrial Premature Complexes; Sinus Rhythm; Ventricular Tachycardia, Nonsustained
• Intervention / Treatment: Diagnostic test: Electrocardiogram analysis by Artificial Intelligence

Detailed 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 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.

• Study Type: Observational
• Enrollment (Anticipated): 10000

Contacts and Locations

• Study Contact: Name: Karl-Heinz Kuck, MD; Phone Number: +49451 500 75301; Email: karl-heinz.kuck@uksh.de
• Study Contact Backup: Name: Michael Schlüter, PhD; Phone Number: +49172 4089325; Email: meos04@gmx.de

Study Locations

• Germany
  • Lübeck, Germany, 23538
    • Recruiting
    • Universitäres Herzzentrum, Lübeck, Germany
    • Contact: Roza Saraei, PhD; Phone Number: +49451 500 44542; Email: roza.saraei@uksh.de
    • Contact: Michael Schlüter, PhD; Phone Number: +49172 408 9325; Email: meos04@gmx.de
    • Principal Investigator: Roland R. Tilz, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 85 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients from tertiary care centers

Description

Inclusion Criteria:

• Heart rhythm of interest present on ECG

Exclusion Criteria:

• Patient incapable of or not willing to sign informed consent form

Study Plan

How is the study designed?

Number of groups / cohorts

5

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Sinus Rhythm; Subjects/patients in normal sinus rhythm	Diagnostic test: Electrocardiogram analysis by Artificial Intelligence; 1-channel electrocardiograms are collected to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms
Atrial Fibrillation; Patients with atrial fibrillation	Diagnostic test: Electrocardiogram analysis by Artificial Intelligence; 1-channel electrocardiograms are collected to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms
Atrial Premature Complexes; Patients with atrial premature complexes in between sinus beats	Diagnostic test: Electrocardiogram analysis by Artificial Intelligence; 1-channel electrocardiograms are collected to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms
Ventricular Premature Complexes; Patients with ventricular premature complexes in between sinus beats	Diagnostic test: Electrocardiogram analysis by Artificial Intelligence; 1-channel electrocardiograms are collected to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms
Ventricular Tachycardia, Nonsustained; Patients with episodes of nonsustained ventricular tachycardia in between sinus beats	Diagnostic test: Electrocardiogram analysis by Artificial Intelligence; 1-channel electrocardiograms are collected to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic accuracy of AI	Overall diagnostic accuracy of the AI in the diagnosis of normal SR, AF, APBs, VPBs, and nonsustained VT (gold standard: diagnosis by experienced electrophysiologist)	1 year
ECG R-R interval	30-sec mean and standard deviation of R-R intervals	Immediate
ECG QRS-complex duration	Measurement of width/duration of QRS complex; distinction between "narrow" (<=110ms) and "wide" (>110ms)	Immediate
ECG QRS-complex fragmentation	Assessment of presence ("Yes") or absence ("No") of QRS-complex fragmentation	Immediate
ECG QTc interval	Calculation of heart rate corrected QT interval (QTc) via Bazett formula from measured QT interval	Immediate
ECG T wave inversion	Assessment of presence ("Yes") or absence ("No") of T wave inversion	Immediate

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
ECG P wave	Assessment of presence ("Yes") or absence ("No") of P wave on ECG; measurement of P-wave duration (in ms)	Immediate
ECG PQ interval	Measurement of PQ interval (onset of P wave to onset of Q wave) on ECG	Immediate
ECG QT interval	Measurement of QT interval (onset of Q wave to end of T wave) on ECG	Immediate

Collaborators and Investigators

• Sponsor: A-Rhythmik GmbH
• Investigators: Principal Investigator: Karl-Heinz Kuck, MD, Universitäres Herzzentrum, Lübeck, Germany

Study record dates

Study Major Dates

• Study Start (ACTUAL): October 1, 2021
• Primary Completion (ANTICIPATED): July 1, 2022
• Study Completion (ANTICIPATED): June 30, 2023

Study Registration Dates

• First Submitted: November 17, 2020
• First Submitted That Met QC Criteria: November 18, 2020
• First Posted (ACTUAL): November 19, 2020

Study Record Updates

• Last Update Posted (ACTUAL): April 6, 2022
• Last Update Submitted That Met QC Criteria: April 5, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Keywords: Automated ECG diagnosis
• Additional Relevant MeSH Terms: Pathologic Processes; Heart Diseases; Cardiovascular Diseases; Death, Sudden; Arrhythmias, Cardiac; Cardiac Conduction System Disease; Pregnancy Complications; Obstetric Labor Complications; Obstetric Labor, Premature; Heart Arrest; Cardiac Complexes, Premature; Death; Atrial Fibrillation; Premature Birth; Tachycardia; Tachycardia, Ventricular; Death, Sudden, Cardiac; Ventricular Premature Complexes; Atrial Premature Complexes
• Other Study ID Numbers: 20-139

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No