Background and objectives: Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD.
Design, setting, participants, & measurements: The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine albumin (UA) dipstick cutoffs for CKD to enable use in the e-phenotype when lacking urine albumin-to-creatinine ratio (UACR), we compared same day UACR and UA results at four sites. A sample of patients, spanning no CKD to ESKD, was randomly selected at four sites for validation via blinded chart review.
Results: The CKD e-phenotype was defined as most recent eGFR <60 ml/min per 1.73 m2 with at least one value <60 ml/min per 1.73 m2 >90 days prior and/or a UACR of ≥30 mg/g in the most recent test with at least one positive value >90 days prior. Dialysis and transplant were identified using diagnosis codes. In absence of UACR, a sensitive CKD definition would consider negative UA results as normal to mildly increased (KDIGO A1), trace to 1+ as moderately increased (KDIGO A2), and ≥2+ as severely increased (KDIGO A3). Sensitivity, specificity, and diagnostic accuracy of the CKD e-phenotype were 99%, 99%, and 98%, respectively. For dialysis sensitivity was 94% and specificity was 89%. For transplant, sensitivity was 97% and specificity was 91%.
Conclusions: The CKD e-phenotype provides a pragmatic and accurate method for EHR-based identification of patients likely to have CKD.
Keywords: EGFR protein, human; adult; albumins; chronic kidney failure; chronic renal insufficiency; creatinine; epidermal growth factor; glomerular filtration rate; humans; outpatients; phenotype; receptor, epidermal growth factor; renal dialysis.
Copyright © 2019 by the American Society of Nephrology.