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.
|Original language||English (US)|
|Number of pages||9|
|Journal||Clinical Journal of the American Society of Nephrology|
|State||Published - Sep 6 2019|
Bibliographical noteFunding Information:
The manual validation was supported by a contract from the National Kidney Disease Education Program. Dr. Jurkovitz reports partial support from Institutional Development Awards from National Institute of General Medical Sciences, National Institutes of Health grants U54-GM104941 and P20 GM103446, and contract funding from National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health. Dr. Kiryluk and Dr. Shang report partial support from National Institute of Diabetes and Digestive and Kidney Diseases Columbia Kidney Precision Medicine Project grant UG3DK114926 and National Human Genome Research Institute Electronic Medical Records and Genomics Consortium grant U01HG8680. Dr. Navaneethan reports receiving a grant from Keryx. Dr. Park reports partial support from National Center for Advancing Translational Sciences, National Institutes of Health University of California, San Francisco Clinical & Translational Science Institute grant UL1 TR001872.
© 2019 by the American Society of Nephrology.