Predicting Risk of Atrial Fibrillation and Association With Other Diseases

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.