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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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Measure | Description | Time Frame |
|---|---|---|
| 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) |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Practice inclusion criteria for the trial are as follows;
Exclusion criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Bristol-Myers Squibb | Bristol-Myers Squibb | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Local Institution | Ludlow | SY8 2AB | United Kingdom | |||
| Local Institution |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33091585 | Derived | 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. |
| Label | URL |
|---|---|
| BMS Clinical Trial Information | View source |
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| Life years (LYs) | Up to 6 months |
| Royal Leamington Spa |
| CV32 4RA |
| United Kingdom |
| Local Institution | Shropshire | SY11 1RD | United Kingdom |
| Local Institution | Warkwickshire | B49 6QR | United Kingdom |
| Local Institution | Wolverhampton | WV10 8RN | United Kingdom |
| Local Institution | Worcester | WR1 2BS | United Kingdom |
| Investigator Inquiry Form | View source |
| FDA Safety Alerts and Recalls | View source |
| ID | Term |
|---|---|
| D001281 | Atrial Fibrillation |
| ID | Term |
|---|---|
| D001145 | Arrhythmias, Cardiac |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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