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.
BJGP Open
; 2024 Jan 24.
Article
em En
| MEDLINE
| ID: mdl-37709350
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.
Texto completo:
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article