NCT04208971

Brief Summary

This is a prospective study to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis of unrecognized atrial fibrillation (AF) and stroke prevention.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,225

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Nov 2020

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

December 19, 2019

Completed
4 days until next milestone

First Posted

Study publicly available on registry

December 23, 2019

Completed
11 months until next milestone

Study Start

First participant enrolled

November 2, 2020

Completed
1.2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 27, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

January 27, 2022

Completed
Last Updated

August 18, 2022

Status Verified

August 1, 2022

Enrollment Period

1.2 years

First QC Date

December 19, 2019

Last Update Submit

August 16, 2022

Conditions

Atrial Fibrillation

Outcome Measures

Primary Outcomes (1)

  • Diagnosis of Atrial Fibrillation as Detected by Patch Application

    The data will be used to examine the performance of the algorithm in detecting unrecognized atrial fibrillation (e.g. positive predictive value, negative predictive value, sensitivity, specificity, and area under the curve \[AUC\]).

    Three Months

Study Arms (1)

BEAGLE Participants

Adult patients who have not been previously diagnosed with AF, are eligible for anticoagulation and have AI-predicted risks based on a normal sinus rhythm ECG.

Other: AI-enabled ECG-based Screening Tool for AF

Interventions

AI-enabled ECG-based Screening Tool for AFOTHER

A novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool to improve atrial fibrillation diagnosis and stroke prevention.

BEAGLE Participants

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

This study aims to enroll adult patients who have not been previously diagnosed with AF, are eligible for anticoagulation and have AI-predicted risks based on a normal sinus rhythm ECG.

You may qualify if:

  • Age ≥18 years
  • Had a 10-second 12-lead ECG done at Mayo Clinic
  • Men with CHA2DS2-VASc ≥2 or women with CHA2DS2-VASc ≥3

You may not qualify if:

  • Diagnosed atrial fibrillation or atrial flutter
  • Missing date of birth or sex in the electronic health record (EHR)
  • A history of intracranial bleeding
  • A history of end-stage kidney disease
  • Have an implantable cardiac monitoring device, including a pacemaker, a defibrillator, or implanted loop recorder
  • Deemed by research personnel to have limitations that would prevent them from being able to provide informed consent, use the patch, or complete interviews will not be included.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Mayo Clinic

Rochester, Minnesota, 55905, United States

Location

Related Publications (2)

  • Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, Gersh BJ, Graff-Radford J, Rabinstein AA, Siontis KC, Friedman PA, Yao X. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022 Oct 8;400(10359):1206-1212. doi: 10.1016/S0140-6736(22)01637-3. Epub 2022 Sep 27.

    PMID: 36179758DERIVED

  • Yao X, Attia ZI, Behnken EM, Walvatne K, Giblon RE, Liu S, Siontis KC, Gersh BJ, Graff-Radford J, Rabinstein AA, Friedman PA, Noseworthy PA. Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial. Am Heart J. 2021 Sep;239:73-79. doi: 10.1016/j.ahj.2021.05.006. Epub 2021 May 24.

    PMID: 34033803DERIVED

Related Links

MeSH Terms

Conditions

Atrial Fibrillation

Condition Hierarchy (Ancestors)

Arrhythmias, CardiacHeart DiseasesCardiovascular DiseasesPathologic ProcessesPathological Conditions, Signs and Symptoms

Study Officials

  • Xiaoxi Yao, PhD, MPH

    Mayo Clinic

    PRINCIPAL INVESTIGATOR

  • Peter Noseworthy, MD

    Mayo Clinic

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Principal Investigator

Study Record Dates

First Submitted

December 19, 2019

First Posted

December 23, 2019

Study Start

November 2, 2020

Primary Completion

January 27, 2022

Study Completion

January 27, 2022

Last Updated

August 18, 2022

Record last verified: 2022-08

Locations

US(1)