A Study to Assess the Effectiveness of an Atrial Fibrillation (AF) Risk Prediction Algorithm and Diagnostic Test in Identifying Patients With AF.
NCT ID: NCT04045639
Last Updated: 2021-08-02
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
COMPLETED
260 participants
OBSERVATIONAL
2019-06-30
2021-01-12
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Screening for Atrial Fibrillation With Self Pulse Monitoring
NCT05818592
Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): Pilot Study
NCT05898165
Leveraging AI-ECG Technology for Early Notification and Tracking of AF Development
NCT06847932
Atrial Fibrillation Incidence, Risk Factors and Genetics
NCT00021905
Investigation of Genetic Risk of Atrial Fibrillation
NCT00412438
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
PROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Intervention arm
The AF risk prediction algorithm will be run on patient records within the Egton Medical Information Systems (EMIS) data base, in order to identify patients at risk of developing AF
No interventions assigned to this group
Control arm
Patients may be diagnosed with AF through routine clinical practice only
No interventions assigned to this group
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Patients registered at a participating practice, aged ≥30 years and without an AF diagnosis.
* As above, and those with a negative or indeterminant ECG
* As above, and those with access to a smartphone
Exclusion Criteria
* Patients with an existing diagnosis of AF
* Patients for whom the healthcare professional feels the study is unsuitable
30 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Bristol-Myers Squibb
INDUSTRY
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Bristol-Myers Squibb
Role: STUDY_DIRECTOR
Bristol-Myers Squibb
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Local Institution
Ludlow, , United Kingdom
Local Institution
Royal Leamington Spa, , United Kingdom
Local Institution
Shropshire, , United Kingdom
Local Institution
Warkwickshire, , United Kingdom
Local Institution
Wolverhampton, , United Kingdom
Local Institution
Worcester, , United Kingdom
Countries
Review the countries where the study has at least one active or historical site.
References
Explore related publications, articles, or registry entries linked to this study.
Hill NR, Arden C, Beresford-Hulme L, Camm AJ, Clifton D, Davies DW, Farooqui U, Gordon J, Groves L, Hurst M, Lawton S, Lister S, Mallen C, Martin AC, McEwan P, Pollock KG, Rogers J, Sandler B, Sugrue DM, Cohen AT. Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial. Contemp Clin Trials. 2020 Dec;99:106191. doi: 10.1016/j.cct.2020.106191. Epub 2020 Oct 19.
Related Links
Access external resources that provide additional context or updates about the study.
BMS Clinical Trial Information
Investigator Inquiry Form
FDA Safety Alerts and Recalls
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
CV185-703
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.