Evaluation of a Chronic Kidney Disease e‐Phenotype: Identification and Characterisation by Electronic Health Record Data

ABSTRACT Aim Develop and evaluate a chronic kidney disease (CKD) electronic (e‐) phenotype to identify and risk‐stratify CKD patients at scale using electronic health record (EHR) data. Methods Patient encounter and laboratory data determining kidney function and proteinuria from a four‐year period (2021–2024) at a large Australian tertiary referral hospital was extracted from the hospital EHR. Following exclusion of kidney transplant recipients and patients on dialysis, eligible patients were classified as having CKD based on ICD‐10 codes, decreased estimated glomerular filtration rate (eGFR, < 60 mL/min per 1.73 m 2 ) and/or albuminuria (urine albumin‐to‐creatinine ratio [uACR] ≥ 3 mg/mmol, A2/A3 equivalent) for a minimum of 3 months. Patients with sufficient laboratory data were also staged according to the Kidney Disease Improving Global Outcomes (KDIGO) A‐by‐G grid. Results From a total undifferentiated hospital cohort of n = 342 146, the CKD e‐phenotype algorithm identified n = 17 908 likely CKD cases (5.2%). Algorithm‐detected CKD cases were validated against blinded manual chart review ( n = 200), revealing sensitivity and specificity for CKD by ICD‐10 codes (0.85 and 0.83), eGFR (0.62 and 0.98) and proteinuria criteria (0.33 and 0.75). The proportion of patients that were ICD‐10‐coded for CKD increased significantly ( p < 0.001) with increasing G‐stage (odds ratio [OR] 3.05) and A‐stage (OR 2.31). Conclusion Incorporating eGFR and proteinuria data into the e‐phenotype algorithm appears to identify novel CKD cases with high specificity, particularly in the earlier stages of CKD progression. However, overall sensitivity is limited when compared to ICD‐10 CKD codes alone.