Predicting Risk of Atrial Fibrillation and Association With Other Diseases

Summary

Atrial fibrillation (AF) is a major public health issue: it is increasingly common, incurs substantial healthcare expenditure, and is associated with a range of adverse outcomes. There is rationale for the early diagnosis of AF, before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. An accurate model that utilises existing routinely-collected data is needed to inform clinicians of patient-level risk of AF, inform national screening policy and highlight opportunities to improve patient outcomes from AF screening beyond that of only stroke prevention. The investigators will use routinely-collected hospital-linked primary care data to develop and validate a model for prediction of incident AF within a short prediction horizon, incorporating both a machine learning and traditional regression method. They will also investigate how atrial fibrillation risk is associated with other diseases and death. Using only clinical factors readily accessible in the community, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, thus accelerating research assessing whether atrial fibrillation screening is clinically effective when targeted to high-risk individuals.

Conditions

• Atrial Fibrillation
• Arrhythmias, Cardiac
• Heart Diseases
• Cardiovascular Diseases
• Pathologic Processes

Interventions

OTHER

Development of an algorithm

Development of an algorithm to predict the risk of new onset Atrial Fibrillation

Sponsors & Collaborators

• British Heart Foundation

  collaborator OTHER

• Clalit Health Services

  collaborator OTHER

• Ben-Gurion University of the Negev

  collaborator OTHER

• University of Leeds

  lead OTHER

Principal Investigators

• Christopher P Gale · University of Leeds

Eligibility

Min Age

30 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2020-11-02

Primary Completion

2023-10-31

Completion

2023-10-31

Countries

• United Kingdom

Study Locations