NCT04045639

Brief Summary

This is a trial to assess the effectiveness of an atrial fibrillation (AF) risk prediction algorithm and diagnostic test for the identification of patients with atrial fibrillation

Trial Health

87
On Track

Trial Health Score

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

Enrollment
260

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jun 2019

Geographic Reach
1 country

6 active sites

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

Study Start

First participant enrolled

June 30, 2019

Completed
23 days until next milestone

First Submitted

Initial submission to the registry

July 23, 2019

Completed
13 days until next milestone

First Posted

Study publicly available on registry

August 5, 2019

Completed
1.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 12, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

January 12, 2021

Completed
Last Updated

August 2, 2021

Status Verified

July 1, 2021

Enrollment Period

1.5 years

First QC Date

July 23, 2019

Last Update Submit

July 29, 2021

Conditions

Atrial Fibrillation

Outcome Measures

Primary Outcomes (1)

  • Percentage of participants with diagnosed Atrial Fibrillation during the research window in control and intervention arms

    Prevalence of AF in the research window assessed

    From the first collection of patient medical records at the beginning of the trial to the last collection of patient records following the intervention at the end of the trial (Up to 6 months)

Secondary Outcomes (3)

  • Expected healthcare resource utilisation (Annual maintenance costs related to health states (informed by the primary endpoint), and daily treatment costs (including monitoring))

    Up to 6 months

  • Quality-adjusted life years (QALYs)

    Up to 6 months

  • Life years (LYs)

    Up to 6 months

Study Arms (2)

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

Control arm

Patients may be diagnosed with AF through routine clinical practice only

Eligibility Criteria

Age30 Years+
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

It is anticipated that approximately 18,000 patient records will be included in the trial. It is anticipated that approximately 1,200 undiagnosed patients would be invited for AF diagnosis across all study sites.30 years is taken as the minimum age entry criteria as the algorithm was built on information from patients \>30 years where AF becomes more prevalent.

You may qualify if:

  • GP Practices within National Institute for Healthcare Research (NIHR) Clinical Research Network: West Midlands (CRN: WM) CRN: WM
  • 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

You may not qualify if:

  • Patients \<30 years
  • Patients with an existing diagnosis of AF
  • Patients for whom the healthcare professional feels the study is unsuitable

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (6)

Local Institution

Ludlow, SY8 2AB, United Kingdom

Location

Local Institution

Royal Leamington Spa, CV32 4RA, United Kingdom

Location

Local Institution

Shropshire, SY11 1RD, United Kingdom

Location

Local Institution

Warkwickshire, B49 6QR, United Kingdom

Location

Local Institution

Wolverhampton, WV10 8RN, United Kingdom

Location

Local Institution

Worcester, WR1 2BS, United Kingdom

Location

Related Publications (1)

  • 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.

    PMID: 33091585DERIVED

Related Links

MeSH Terms

Conditions

Atrial Fibrillation

Condition Hierarchy (Ancestors)

Arrhythmias, CardiacHeart DiseasesCardiovascular DiseasesPathologic ProcessesPathological Conditions, Signs and Symptoms

Study Officials

  • Bristol-Myers Squibb

    Bristol-Myers Squibb

    STUDY DIRECTOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

July 23, 2019

First Posted

August 5, 2019

Study Start

June 30, 2019

Primary Completion

January 12, 2021

Study Completion

January 12, 2021

Last Updated

August 2, 2021

Record last verified: 2021-07

Locations

GB(6)