Atrial fibrillation (AF) is a condition that causes an irregular and often abnormally fast heartbeat. It is common and can cause problems such as stroke, dementia and heart failure.
Whilst AF is common, it is often diagnosed too late only after complications such as stroke have occurred. There are still uncertainties about who gets AF and why, and which health conditions people with AF are likely to develop later in life.
In this research we will use hospital and primary care medical records to see if we are able to predict who is likely to get AF, and to look at what happens to patients diagnosed with AF in the long term. We will study patients information such as age and gender, as well as their medical history to determine how these may be associated with developing AF. We will also study the medications used to treat AF and to reduce the risk of stroke.
The research will use traditional statistical methods as well as artificial intelligence to try and answer these questions.
Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial health-care expenditure as a result of stroke, sudden death, heart failure and unplanned hospitalisation.
Yet, many patients with AF are diagnosed too late for example, when stroke has occurred. Moreover, there is very little information about the prediction of for whom and when new onset AF will occur a fundamental knowledge gap which if filled could transform the outcomes of patients with AF. Equally, little is known about the full extent of the health burden of AF beyond that of hypothesis-driven clinical outcomes, such as stroke, myocardial infarction and health failure. In addition, oral anticoagulation is often prescribed to patients with AF to reduce the risk of stroke prevention; however, their uptake is limited.
In essence, there are no largescale population-based studies which provide high-resolution insights into the healthcare burden, treatment and disease trajectories of patients with AF.
Therefore, this study aims to investigate the healthcare burden and clinical pathways of patients with AF using national hospital linked electronic primary healthcare databases.
Specifically, the study will:
1. Quantify the incidence of cardiovascular and non-cardiovascular outcomes and mortality, and project the disease pathways amongst AF patients compared with the general population.
2. Investigate the use of oral anticoagulants among patients with AF and quantify their association with major cardiovascular and non-cardiovascular outcomes.
3. Determine predictors of new onset of AF.
Quantifying the population trends and clinical pathways of AF patients, its management with the uptake of oral anticoagulants and subsequent healthcare burden will help target therapeutic strategies to specific groups of patients to further reduce the incidence of stroke and premature mortality. In addition, determining the predictive factors for new onset AF also help targeting the high-risk individuals for preventive measures.
Cardiovascular and non-cardiovascular related hospital admission, atrial fibrillation, mortality
Jianhua Wu - Chief Investigator - University of Leeds
Jianhua Wu - Corresponding Applicant - University of Leeds
Campbell Cowan - Collaborator - Leeds Teaching Hospitals NHS Trust
Chris Gale - Collaborator - University of Leeds
Marlous Hall - Collaborator - University of Leeds
Tatendashe Dondo - Collaborator - University of Leeds
HES Admitted Patient Care;HES Diagnostic Imaging Dataset;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation