Clinical trial NCT04045639: A Study to Assess the Effectiveness of an Atrial Fibrillation (AF) Risk Prediction Algorithm and Diagnostic Test in Identifying Patients With AF.
Official title: A Randomised Controlled Trial for the Identification of Undiagnosed Atrial Fibrillation Patients Using a Machine Learning Risk Prediction Algorithm and Diagnostic Test
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Atrial Fibrillation
Lead sponsor: Bristol-Myers Squibb
Sponsor class: INDUSTRY
First posted: 2019-08-05
Enrollment: 260
Sex: ALL
Minimum age (years): 30
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_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.

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


Eligibility criteria: Inclusion Criteria:

Practice inclusion criteria for the trial are as follows;

* GP Practices within National Institute for Healthcare Research (NIHR) Clinical Research Network: West Midlands (CRN: WM) CRN: WM
* GP Practices using EMIS as their electronic medical record system of choice. Patient inclusion criteria for the trial are as follows;
* Patients registered at a participating practice, aged ≥30 years and without an AF diagnosis.
* Patients with a valid index date (see Section 3.3) Participant inclusion criteria for participation in the intervention arm are;
* As above, and who have provided written consent to: attend a research clinic, AF diagnosis check using a 12-lead ECG, and for access to medical records Participant inclusion criteria for participation in further AF remote diagnosis with an AliveCor Heart Monitor are;
* As above, and those with a negative or indeterminant ECG
* As above, and those with access to a smartphone

Exclusion criteria:

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

Primary outcomes:
measure: Percentage of participants with diagnosed Atrial Fibrillation during the research window in control and intervention arms
timeFrame: 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:
measure: Expected healthcare resource utilisation (Annual maintenance costs related to health states (informed by the primary endpoint), and daily treatment costs (including monitoring))
timeFrame: Up to 6 months
measure: Quality-adjusted life years (QALYs)
timeFrame: Up to 6 months
measure: Life years (LYs)
timeFrame: Up to 6 months
Other outcomes:
Locations:
city: Leamington Spa
country: United Kingdom
facility: Local Institution
city: Ludlow
country: United Kingdom
facility: Local Institution
city: Shropshire
country: United Kingdom
facility: Local Institution
city: Warkwickshire
country: United Kingdom
facility: Local Institution
city: Wolverhampton
country: United Kingdom
facility: Local Institution
city: Worcester
country: United Kingdom
facility: Local Institution
Study officials:
affiliation: Bristol-Myers Squibb
name: Bristol-Myers Squibb
role: STUDY_DIRECTOR
References:
citation: 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.