Non-valvular atrial fibrillation (NVAF) is an irregular heart beat in individuals who do not have artificial heart valves or a heart valve problem. People who have NVAF are five times more likely to have a stroke than people who don't have the condition.
Chronic kidney disease (CKD) is a condition where there is a gradual loss of kidney function over time, that leads to eventual kidney failure. If people with CKD also have NVAF then the risk of stroke is even greater.
Usually NVAF is treated by medicines that reduce blood clotting (oral anticoagulants) but for people who also have CKD these medicines carry the added risk of bleeding. Although this is not usually a problem in the early stages of CKD, not much is known about people with severe CKD as they are often excluded from scientific studies.
We will use a large patient database to investigate what happens to people with CKD after they are diagnosed with NVAF. By finding out if treatment pathways are different according to the stage of CKD we will be able to provide evidence to help make decisions about the best treatment for this group of patients.
Objectives:
To provide evidence to support the development of treatment guidelines in individuals with severe renal dysfunction on diagnosis of non-valvular atrial fibrillation (NVAF).
Methods:
This will be a descriptive population-based cross sectional study in patients with chronic kidney disease (CKD) who are newly diagnosed with NVAF. Patients with an incident AF who already have a CKD diagnosis will be identified from the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) for the period of 5 years from 2012 to 2017. Renal function at the time of NVAF diagnosis will be described in terms of creatinine clearance (Cr Cl), Glomerular Filtration Rate (GFR) and the prevalence of end stage renal disease. Prescriptions with relevant OAC drug codes will be identified to classify individuals into drug type categories.
Data analysis:
An analysis stratified by year will use appropriate descriptive statistics to summarise Cr Cl, GFR/eGFR and ESRD. These will be categorised to reflect kidney function and number and proportion in each drug category will be described.
A sensitivity analysis will explore extending time frames for determining GFR and serum creatinine records and weight records.
Data set(s) to be used:
- CPRD GOLD
- HES Admitted Patient Care
Chronic Kidney disease
Sreeram Ramagopalan - Chief Investigator - London School Of Economics & Political Science
Cormac Sammon - Corresponding Applicant - PHMR Associates Limited ( UK )
Elaine Stamp - Collaborator - PHMR Associates Limited ( UK )
HES Admitted Patient Care;HES Outpatient