Andrew S. Levey, MD; Lesley A. Stevens, MD, MS; Christopher H. Schmid, PhD; Yaping (Lucy) Zhang, MS; Alejandro F. Castro III, MPH; Harold I. Feldman, MD, MSCE; John W. Kusek, PhD; Paul Eggers, PhD; Frederick Van Lente, PhD; Tom Greene, PhD; Josef Coresh, MD, PhD, MHS; for the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) (*)
Acknowledgment: The authors thank Aghogho Okparavero, MBBS, MPH, for his assistance in communications and manuscript preparation.
Grant Support: By grants UO1 DK 053869, UO1 DK 067651, and UO1 DK 35073 as part of a cooperative agreement with the National Institute of Diabetes and Digestive and Kidney Diseases.
Potential Financial Conflicts of Interest:Stock ownership or options (other than mutual funds): J.W. Kusek (Pfizer, Eli Lilly, DeCode Genetics).
Reproducible Research Statement:Study protocol: Available from Dr. Levey (address below). Statistical code and data set: Not available.
Requests for Single Reprints: Andrew S. Levey, MD, Division of Nephrology, Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111.
Current Author Addresses: Drs. Levey and Stevens and Ms. Zhang: Division of Nephrology, Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111.
Dr. Schmid: The Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Box 063, Boston, MA 02111.
Mr. Castro and Dr. Coresh: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Suite 2-600, Baltimore, MD 21205.
Dr. Feldman: Clinical Epidemiology Unit, University of Pennsylvania School of Medicine, 923 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.
Drs. Kusek and Eggers: Kidney and Urology Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 6707 Democracy Boulevard, Bethesda, MD 20817.
Dr. Van Lente: Department of Clinical Pathology, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code L11, Cleveland, OH 44195.
Dr. Greene: Division of Clinical Epidemiology, 30 North 1900 East, Room AC221, Salt Lake City, UT 84132.
Author Contributions: Conception and design: A.S. Levey, L.A. Stevens, C.H. Schmid, H.I. Feldman, F. Van Lente, T. Greene, J. Coresh.
Analysis and interpretation of the data: A.S. Levey, L.A. Stevens, C.H. Schmid, H.I. Feldman, P. Eggers, F. Van Lente, T. Greene, J. Coresh.
Drafting of the article: A.S. Levey, C.H. Schmid, F. Van Lente, J. Coresh.
Critical revision of the article for important intellectual content: A.S. Levey, L.A. Stevens, C.H. Schmid, H.I. Feldman, J.W. Kusek, P. Eggers, T. Greene, J. Coresh.
Final approval of the article: A.S. Levey, L.A. Stevens, C.H. Schmid, Y. Zhang, H.I. Feldman, J.W. Kusek, T. Greene, J. Coresh.
Provision of study materials or patients: A.S. Levey.
Statistical expertise: C.H. Schmid, Y. Zhang, T. Greene, J. Coresh.
Obtaining of funding: A.S. Levey, J.W. Kusek, P. Eggers, J. Coresh.
Administrative, technical, or logistic support: A.S. Levey, L.A. Stevens, Y. Zhang, P. Eggers.
Collection and assembly of data: A.S. Levey, L.A. Stevens, Y. Zhang, F. Van Lente.
Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values.
To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.
Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates.
Research studies and clinical populations (“studies”) with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006.
8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16 032 participants in NHANES.
GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age.
In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m2), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m2), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m2 (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m2, and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%).
The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR.
The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use.
National Institute of Diabetes and Digestive and Kidney Diseases.
Andrew S. Levey, Lesley A. Stevens, Christopher H. Schmid, Yaping (Lucy) Zhang, Alejandro F. Castro, Harold I. Feldman, et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Intern Med. 2009;150:604–612. doi: 10.7326/0003-4819-150-9-200905050-00006
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Published: Ann Intern Med. 2009;150(9):604-612.
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