UK Renal Registry

ILD143

In the UK around 6% of adults are diagnosed with moderate to severe chronic kidney disease per year. This is estimated to increase by 17% by 2030. Some of these patients progress to end-stage kidney disease (ESKD) meaning patients need dialysis or a transplant to stay alive, which places a huge burden on the individuals, their families and communities, and the NHS. People from minority ethnic backgrounds are at least twice as likely to progress to ESKD as people from other ethnic backgrounds. Patients at risk of ESKD are identified mainly based on a kidney blood test (serum creatinine), which is used to calculate a measure of how well the kidneys are working (called eGFR). However, eGFR can be inaccurate by 20-30%. Urine tests to detect protein can help to identify rapid kidney function loss, but not all patients have these tests. Another way to identify which patients are at risk of ESKD is to use a prediction model known as the Kidney Failure Risk Equation (KFRE). This looks at a number of aspects such as age, sex, eGFR and urine results to calculate a % risk of kidney failure. The KFRE has been recommended by National Institute for Health and Care Excellence (NICE) guidelines. However, the KFRE has not undergone validation studies in ethnically diverse groups. There are different ways to calculate eGFR and we don't know which one is the best to use for different ethnic groups. We also don't know whether including additional information into the KFRE could improve its accuracy for different ethnic groups. In this study we will link GP electronic health records from five ethnically diverse regions in the UK to dialysis data from the UK Renal Registry. We will compare different eGFR calculations in diverse populations, and we will look at whether other biomarkers and factors such as demographic data, laboratory test results, comorbidities, blood pressure, medications, lifestyle factors, body mass index, and practice factors contribute to risk prediction. This study will help find more accurate ways to predict progression of kidney disease in ethnically diverse populations.