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Clin J Am Soc Nephrol. 2019 Sep 6;14(9):1306-1314. doi: 10.2215/CJN.00360119. Epub 2019 Aug 12.

Development and Validation of a Pragmatic Electronic Phenotype for CKD.

Author information

1
National Kidney Disease Education Program, Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland; jenna.norton@nih.gov.
2
National Kidney Disease Education Program, Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
3
Value Institute, Christiana Care Health System, Newark, Delaware.
4
Division of Nephrology, Department of Medicine and.
5
Division of Nephrology, Department of Medicine, University of California, San Francisco, California.
6
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
7
Department of Bioinformatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York.
8
Section of Nephrology, Department of Medicine, Selzman Institute for Kidney Health, Baylor College of Medicine, Houston, Texas.
9
Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas; and.
10
Division of Renal Diseases and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.

Abstract

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

PMID:
31405830
PMCID:
PMC6730512
[Available on 2020-09-06]
DOI:
10.2215/CJN.00360119

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