Diagnosis codes underestimate chronic kidney disease incidence compared with eGFR-based evidence: a retrospective observational study of patients with type 2 diabetes in UK primary care

Abstract

Background: Type two diabetes (T2D) is a leading cause of both chronic kidney disease (CKD) and onward progression to end-stage renal disease. Timely diagnosis coding of CKD in patients with T2D could lead to improvements in quality of care and patient outcomes. Aim: To assess the consistency between estimated glomerular filtration rate (eGFR)-based evidence of CKD and CKD diagnosis coding in UK primary care. Design & setting: A retrospective analysis of electronic health record data in a cohort of people with T2D from 60 primary care centres within England between 2012 and 2022. Method: We estimated the incidence rate of CKD per 100 person–years using eGFR-based CKD and diagnosis codes. Logistic regression was applied to establish which attributes were associated with diagnosis coding. Time from eGFR-based CKD to entry of a diagnosis code was summarised using the median and interquartile range. Results: The overall incidence of CKD was 2.32 (95% confidence interval [CI] = 2.24 to 2.41) and significantly higher for eGFR-based criteria than diagnosis codes: 1.98 (95% CI = 1.90 to 2.05) versus 1.06 (95% CI = 1.00 to 1.11), respectively; P<0.001. Only 45.4% of CKD incidences identified using eGFR-based criteria had a corresponding diagnosis code. Patients who were younger, had a higher CKD stage (G4), had an observed urine albumin-to-creatinine ratio (A1), or no observed HbA1c in the past year were more likely to have a diagnosis code. Conclusion: Diagnosis coding of patients with eGFR-based evidence of CKD in UK primary care is poor within patients with T2D, despite CKD being a well-known complication of diabetes.

Keywords

• diagnosis
• diabetes
• renal medicine
• renal insufficiency, chronic
• primary health care