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A New Equation to Estimate Glomerular Filtration Rate

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, CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
[+] Article and Author Information

For a list of other CKD-EPI staff and collaborators, see the Appendix.


From Tufts Medical Center, Boston, Massachusetts; Johns Hopkins University, Baltimore, Maryland; University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; National Institutes of Health, Bethesda, Maryland; Cleveland Clinic Foundation, Cleveland, Ohio; and University of Utah, Salt Lake City, Utah.


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.


Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006
Text Size: A A A

Background: 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.

Objective: To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.

Design: 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.

Setting: Research studies and clinical populations (“studies”) with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006.

Participants: 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.

Measurements: 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.

Results: 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%).

Limitation: The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR.

Conclusion: 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.

Primary Funding Source: National Institute of Diabetes and Digestive and Kidney Diseases.

Figures

Grahic Jump Location
Figure.
Performance of the CKD-EPI and MDRD Study equations in estimating measured GFR in the external validation data set.

Both panels show the difference between measured and estimated versus estimated GFR. A smoothed regression line is shown with the 95% CI (computed by using the lowest smoothing function in R), using quantile regression, excluding the lowest and highest 2.5% of estimated GFR. To convert GFR from mL/min per 1.73 m2 to mL/s per m2, multiply by 0.0167. CKI-EPD = Chronic Kidney Disease Epidemiology Collaboration; GFR = glomerular filtration rate; MDRD = Modification of Diet in Renal Disease.

Grahic Jump Location

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Prevalence of chronic kidney disease by the new CKD-EPI and the MDRD study equations in Asians
Posted on June 3, 2009
Charumathi Sabanayagam
Singapore Eye Research Institute
Conflict of Interest: None Declared

To the Editor: Levey and colleagues presented a new equation for estimation of GFR, the CKD-EPI (1) and compared the prevalence of CKD in the US population estimated by the new equation with that by the MDRD study equation. The authors reported that the new CKD-EPI equation gives a lower estimated prevalence of CKD than the MDRD study equation and suggested that this new equation should also be tested in other ethnic groups. Recent studies in Asian populations show that the epidemiological pattern and the relative contribution of some of the known risk factors of CKD such as blood pressure (2) and body mass index (3) are different among Asians compared to Western populations. We examined the prevalence of CKD by MDRD study equation (4) and the new CKD-EPI equation, (1) in a multi-ethnic Asian population (n=4,498) comprising of Chinese (66.7%), Malay (17.5%) and Indian (15.8%) participants aged ¡Ý24years, who participated in the Singapore Prospective Study Programme (SP2), a population-based cross-sectional study in Singapore (5). We defined CKD as estimated GFR <60 mL/min/1.73 m2 or micro/macroalbuminuria (urinary albumin-to-creatinine ratio ¡Ý17mg/g for men and ¡Ý25mg/g for women). In keeping with the results from Levey et al., the median estimated GFR by CKD-EPI was 3.7 mL/min/1.73m2 higher than that by MDRD but the prevalence of CKD was similar using both equations (21.5% vs. 21.7% comparing CKD-EPI and MDRD). The prevalence of CKD estimated by both equations was similar in Chinese (18.5 vs. 18.6, comparing CKD-EPI and MDRD), Malay (28.5 vs.28.6) and Indian (26.7 vs. 27.0) participants. However, as compared to MDRD, the CKD -EPI equation leads to a higher prevalence of stage 1 (7.1 vs. 6.5), a lower prevalence of stage 2 (8.3 vs. 9.1) and a similar prevalence of stage 3 and above (6.1% vs. 6.2%) CKD categories; this pattern was seen in all three Asian ethnic groups. Although we did not have a gold standard measurement for GFR to compare the performance of the two equations, our study indicates that the new CKD-EPI equation is broadly useful for estimating CKD prevalence in three common racial ethnic groups in Asia.

References

1. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, III, Feldman HI et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604-12.

2. Ramirez SP, McClellan W, Port FK, Hsu SIH. Risk Factors for Proteinuria in a Large, Multiracial, Southeast Asian Population. J Am Soc Nephrol 2002; 13: 1907-17.

3. Shankar A, Leng C, Chia KS, Koh D, Tai ES, Saw SM et al. Association between body mass index and chronic kidney disease in men and women: population-based study of Malay adults in Singapore. Nephrol Dial Transplant 2008; 23: 1910-8.

4. Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the modification of diet in renal disease and Cockcroft-Gault equations for estimating renal function. J Am Soc Nephrol 2005; 16: 763-73.

5. Nang EE, Khoo CM, Tai ES, Lim SC, Tavintharan S, Wong TY et al. Is There a Clear Threshold for Fasting Plasma Glucose That Differentiates Between Those With and Without Neuropathy and Chronic Kidney Disease?: The Singapore Prospective Study Program. Am J Epidemiol 2009.

Conflict of Interest:

None declared

GFR estimation using the CKD-EPI equation
Posted on June 24, 2009
Andrew S Levey
Tufts Medical Center
Conflict of Interest: None Declared

Dr. Wong reports that mean estimated GFR computed using the CKD-EPI equation is higher than using the MDRD Study equation in a young Asian population (presumably with high GFR) of mixed ethnicity. This result confirms our findings in a predominantly US and European population (1) and is expected due to the form and coefficients for the variables for both equations. Studies with measured GFR are required to evaluate the accuracy of GFR estimates in Asian populations. There is no "Asian coefficient" for the CKD-EPI equation, so we suspect it will not be as accurate in Asians as in our study. Others have proposed coefficients for use of the MDRD Study in China and Japan, but the results are not consistent (2, 3), and we suggest further studies in these populations. Unlike our report, Dr. Wong does not find a large difference in the prevalence of chronic kidney disease (CKD) using the CKD-EPI equation compared to the MDRD Study equation. CKD prevalence estimates depend on many factors other than the estimating equation, including the assay for serum creatinine, the distribution of measured GFR in the study population, the distribution of age, sex and race (factors that are included in the equations), and non-GFR determinants of serum creatinine, such as muscle mass and diet (factors that are not included in the equations) (4). CKD prevalence is also affected by markers to assess kidney damage. Using the CKD-EPI equation, the prevalence of CKD Stages 3 -4 in Dr. Wong's study was only slightly lower than in the US (6.1% vs. 6.7% in our report) despite a lower mean age, but the prevalence of CKD Stages 1 and 2 was substantially higher (15.4% vs. 5.8% in our report) (Appendix Table 9). The latter finding is at least partially due to one- time ascertainment for urine albumin-to-creatinine ratio rather than our method of accounting for persistence in only a subset of individuals with microalbuminuria (5). As we noted, because of the higher mean estimated GFR, the prevalence ratio of CKD Stages 1 to 2 in Dr. Wong's study was higher using the CKD-EPI equation than using the MDRD Study equation. We encourage others to compare the performance of the CKD-EPI equation to the MDRD Study equation in estimating measured GFR, in assessing CKD prevalence, and in predicting risk of future events as part of the process of improving GFR estimation and understanding its clinical implications.

References:

1. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-12.

2. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised Equations for Estimated GFR From Serum Creatinine in Japan. Am J Kidney Dis. 2009; 53: 982-992.

3. Rule AD, Wee TB. Glomerular Filtration Rate estimation in Japan and China: what accounts for the difference? Am J Kidney Dis. 2009; 53: 932-5.

4. Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function-- measured and estimated glomerular filtration rate. N Engl J Med. 2006;354(23):2473-83.

5. Coresh J, Selvin E, Stevens LA, Manzi J, Kusek J, Eggers PW, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-47.

Conflict of Interest:

None declared

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