Amy Earley, BS; Dana Miskulin, MD, MS; Edmund J. Lamb, PhD; Andrew S. Levey, MD; Katrin Uhlig, MD, MS
Grant Support: By KDIGO.
Potential Conflicts of Interest: Ms. Earley and Drs. Miskulin and Uhlig report the following: Grant (money to institution): National Kidney Foundation; Support for travel to meetings for the study or other purposes: National Kidney Foundation. Ms. Earley and Dr. Uhlig further report: Fees for participation in review activities such as data monitoring boards, statistical analysis, end point committees, and the like (money to institution): National Kidney Foundation. Dr. Miskulin further reports: Grant: National Kidney Foundation; Employment: Dialysis Clinic. Dr. Lamb: Support for travel to meetings for the study or other purposes: Kidney Disease: Improving Global Outcomes; Grants/grants pending (money to institution): National Institutes of Health. Dr. Levey: Support for travel to meetings for the study or other purposes (money to institution): National Kidney Foundation; Board membership (money to institution): National Kidney Foundation; Grants/grants pending (money to institution): National Kidney Foundation. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-2267.
Requests for Single Reprints: Katrin Uhlig, MD, MS, Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111; e-mail, firstname.lastname@example.org.
Current Author Addresses: Ms. Earley and Drs. Miskulin, Levey, and Uhlig: Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111.
Dr. Lamb: East Kent Hospitals University National Health Service Trust, Kent and Canterbury Hospital, Ethelbert Road, Canterbury, Kent CT1 3NG, United Kingdom.
Author Contributions: Conception and design: A. Earley, E.J. Lamb, A.S. Levey, K. Uhlig.
Analysis and interpretation of the data: A. Earley, D. Miskulin, E.J. Lamb, A.S. Levey, K. Uhlig.
Drafting of the article: A. Earley, D. Miskulin, E.J. Lamb, A.S. Levey, K. Uhlig.
Critical revision of the article for important intellectual content: A. Earley, D. Miskulin, E.J. Lamb, A.S. Levey, K. Uhlig.
Final approval of the article: A. Earley, E.J. Lamb, A.S. Levey, K. Uhlig.
Statistical expertise: K. Uhlig.
Obtaining of funding: K. Uhlig.
Administrative, technical, or logistic support: A. Earley, E.J. Lamb, K. Uhlig.
Collection and assembly of data: A. Earley, D. Miskulin, E.J. Lamb, A.S. Levey, K. Uhlig.
Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating Equations for Glomerular Filtration Rate in the Era of Creatinine Standardization: A Systematic Review. Ann Intern Med. 2012;156:785-795. doi: 10.7326/0003-4819-156-11-201203200-00391
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Published: Ann Intern Med. 2012;156(11):785-795.
Clinical laboratories are increasingly reporting estimated glomerular filtration rate (GFR) by using serum creatinine assays traceable to a standard reference material.
To review the performance of GFR estimating equations to inform the selection of a single equation by laboratories and the interpretation of estimated GFR by clinicians.
A systematic search of MEDLINE, without language restriction, between 1999 and 21 October 2011.
Cross-sectional studies in adults that compared the performance of 2 or more creatinine-based GFR estimating equations with a reference GFR measurement. Eligible equations were derived or reexpressed and validated by using creatinine measurements traceable to the standard reference material.
Reviewers extracted data on study population characteristics, measured GFR, creatinine assay, and equation performance.
Eligible studies compared the MDRD (Modification of Diet in Renal Disease) Study and CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equations or modifications thereof. In 12 studies in North America, Europe, and Australia, the CKD-EPI equation performed better at higher GFRs (approximately >60 mL/min per 1.73 m2) and the MDRD Study equation performed better at lower GFRs. In 5 of 8 studies in Asia and Africa, the equations were modified to improve their performance by adding a coefficient derived in the local population or removing a coefficient.
Methods of GFR measurement and study populations were heterogeneous.
Neither the CKD-EPI nor the MDRD Study equation is optimal for all populations and GFR ranges. Using a single equation for reporting requires a tradeoff to optimize performance at either higher or lower GFR ranges. A general practice and public health perspective favors the CKD-EPI equation.
Kidney Disease: Improving Global Outcomes.
Multiple methods are used to estimate glomerular filtration rate (GFR) from serum creatinine level.
This review summarized data from cross-sectional studies that compared 2 or more creatinine-based GFR estimating equations to a reference GFR measurement. Studies from North America, Europe, and Australia showed that the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation performed better at higher GFRs and the Modification of Diet in Renal Disease (MDRD) Study equation performed better at lower GFRs. Neither equation performed as well in Asian or African populations as it did in North American or European populations.
The performance of the CKD-EPI and MDRD Study equations varies across populations and GFR ranges.
Appendix Table 1.
Summary of evidence search and selection.
SCr = serum creatinine; SRM = standard reference material.
Appendix Table 2.
Information on Development of Equations Based on Serum Creatinine Assays That Are Traceable to the Standard Reference Material
Appendix Table 3.
Overview Table of Equations Developed to Predict GFR Based on Serum Creatinine Assays Not Traceable to the Standard Reference Material
Performance Comparison of Creatinine-Based GFR Estimating Equations in North America, Europe, and Australia
Differences in accuracy and bias between estimated GFR by CKD-EPI and MDRD Study equations in North America, Europe, and Australia.
Difference in accuracy (top), as measured by P30 (P30 for CKD-EPI minus P30 for MDRD), is plotted against mean measured GFR in the study population. Difference in bias (bottom) (absolute value for bias for estimated GFR by MDRD Study equation minus absolute value for bias for estimated GFR by CKD-EPI equation) is plotted against mean measured GFR in the study population. CKD-EPI = Chronic Kidney Disease Epidemiology Collaboration; GFR = glomerular filtration rate; MDRD = Modification of Diet in Renal Disease; P30 = percentage of estimated GFR values within 30% of measured GFR.
* Denotes study in which all patients received trimethoprim.
† Could be reported in mL/min.
Appendix Table 4.
Performance of Creatinine-Based GFR Estimating Equations in North America, Europe, and Australia in Subgroups by GFR
Appendix Table 5.
Performance of Creatinine-Based GFR Estimating Equations in North America, Europe, and Australia in Subgroups by Race
Performance Comparison of Creatinine-Based GFR Estimating Equations Outside of North America, Europe, and Australia
Appendix Table 6.
Performance of Creatinine-Based GFR Estimating Equations Outside North America, Europe, and Australia in Subgroups by GFR
Suggested Criteria for Developing and Validating GFR Estimating Equations
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T.S. Dharmarajan, MD, FACP, AGSF, Professor of Medicine and Associate Dean, Edward P. Norkus PhD, FACN, Associate Professor, Preventive and Community Medicine
New York Medical College, Valhalla, NY, Montefiore Medical Center (North division), Bronx, NY
June 19, 2012
Estimating Equations for Glomerular Filtration Rate in the Old: As Age Advances, the Formulae Concur Even Less!
To the Editor: We appreciate the review, “Estimating equations for glomerular filtration rate in the era of creatinine standardization”, by Earley et al (1), comparing the glomerular filtration rates (GFR) derived using the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration Initiative (CKD-EPI) formulae from publications between 1999 and 2011. The authors noted the CKD-EPI formula performed better at higher GFRs (>60 mL/min/1.73 m2) than the MDRD equation, while the MDRD equation did better at lower GFRs. They concluded that neither the CKD-EPI nor the MDRD formulae was optimal for all populations and GFR ranges, and acknowledged the paucity of data comparing the formulae across different age subgroups.
In February 2012, we published data that compared GFR estimates derived from the MDRD, CKD-EPI and Cockcroft-Gault (CG) formulae in a cross-section of 1535 older Americans (59 - 104 years) (2). Our sample included 29% White, 36% African American and 35% Hispanic adults, with 55% residents in long-term care; 39% had medical records indicating renal insufficiency determined by their primary physician. We observed a significant disconnect in CKD staging, with the potential to influence recommendations for monitoring and management, based on the formula used. In our report, the C-G formula provided significantly lower GFR estimates than either the MDRD or CKD-EPI formulae in the entire sample and in the subset of individuals classified as having renal insufficiency.
We also compared GFR estimates after stratifying our sample into four age categories (59-69, 70-79, 80-89 and 90+ years). The three formulae produced nearly identical GFR estimates, across race, in individuals aged <70 years. However, in subjects 70 - 104 years, the GFR estimates significantly differed across formulae. Individuals >69 years were classified into lower CKD stages (levels 3-5) significantly more often using the C-G and CKD-EPI formulae compared to the MDRD formula (C-G>CKD-EPI>MDRD); conversely, classification into CKD stages 1 and 2 (better function) occurred significantly more often with the MDRD formula (Table 1).
Our study's aim was simply to determine if GFR estimates differed across the three equations. We did not determine the accuracy or bias in the derived GFR estimates based on standard reference methods (urinary or plasma clearance of an exogenous marker). Nevertheless, we believe our findings that the CKD-EPI (and C-G) formulae classifies older, White, African American and Hispanic Americans into lower CKD stages more often than the MDRD equation are relevant in the old for several reasons. Based on the National Health and Nutrition Examination Survey (NHANES) data, the prevalence of CKD stages 3-5 in adults above 60 years was 22% (3). The increasing decline in muscle mass with age (sarcopenia) renders serum creatinine values less reliable as a marker of renal function in the old, emphasizing the need for reliable means to estimate GFR. Decline in renal function may occur with age and commonly does from disease (3, 4). In practice, accurate staging of CKD is essential: to accurately determine dosing of medications handled by the kidney; determine risk for surgery; assess risk for contrast use in imaging studies; plan nutritional therapy in CKD; and recognize and address complications linked to stage of kidney disease (5).
Based on the formula used, disconnect in GFR value and consequent staging of CKD, approximately half of older adults with CKD may be misclassified and inappropriately monitored. These implications emphasize a need for the ideal formula to estimate GFR, especially for the geriatric patient.
1. Earley, A, Miskulin D, Lamb EJ et al. Estimating equations for glomerular filtration rate in the era of creatinine standardization. Ann Intern Med., 2012; 156:785-795
2. Dharmarajan TS, Yoo J, Russell RO, Norkus EP. Chronic kidney disease staging in nursing home and community older adults: Does the choice of Cockcroft-Gault, Modification of Diet in Renal disease or the Chronic Kidney Disease Epidemiology Collaboration Initiative equations matter? JAMDA, 2012; 13:151-155.
3. Coresh J, Selvin E, Stevens LA et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298:2038-47
4. Lindeman RD, Tobin J, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc. 1985; 33: 278-8
5. Fink HA, Ishani A, Taylor BC et al. Screening for, monitoring and treatment of chronic kidney disease stages 1-3: A systematic review for the U.S. Preventive Serviuces Task Force and for an American College of Physicians Clinical Practice Guideline. Ann Intern Med.2012; 156:570-81
Sandra P Silveiro, Ariana A. Soares, Letícia S. Weinert, Eduardo G. Camargo
Endocrinology Division, Hospital de Clínicas de Porto Alegre, Brazil
August 29, 2012
Performance of CKD-EPI equation in diabetic individuals
TO THE EDITOR: Earley and colleagues have elegantly presented the results of a timely systematic review “Estimating equations for glomerular filtration rate in the era of creatinine standardization - a systematic review” (1), concluding that the performance of the CKD-EPI and MDRD study equations varies across populations and GFR ranges, taking into account the use of a standardized creatinine measurement. The authors required, as inclusion criteria, the minimum number of a 100 individuals, the use of a glomerular filtration rate (GFR) reference method and the use of an IDMS traceable creatinine method. As the review encompassed the period of 1999 up to October 2011, we would like to share our results published in the November 2011 issue of Diabetes Care (2), because not only our data fulfills the required inclusion criteria, but also describes the findings of a South-Brazilian population, not evaluated in the systematic review. In our cross-sectional study, we evaluated 105 patients with type 2 diabetes, with a mean age of 57±8 years; 53 (50%) men and 90 (86%) white. Forty-six (44%) patients had microalbuminuria, and 14 (13%) had macroalbuminuria - all patients had GFRs >60 mL/min/1.73 m2, as measured by 51Cr-EDTA single-injection method. Measured 51Cr-EDTA GFR was 103±23, CKD-EPI GFR was 83±15 (bias: 20), and MDRD GFR was 78±17 mL/min/1.73 m2 (bias: 24). Accuracy P30 (95%CI) was 67% (58–74) for CKD-EPI and 64% (56–75) for MDRD. We concluded that both equations pronouncedly underestimated GFR in type 2 diabetic patients with GFR above 60 mL/min/1.73 m2. These findings confirmed our previous report of a significantly worse performance of both equations in patients with diabetes when compared with healthy individuals, mainly due to the fact that diabetic patients had higher serum creatinine levels than the healthy group, even after matched by GFR (3). In contrast, when previously analyzing a group of 96 healthy volunteers with normal GFR, we observed the improved accuracy (P30) of CDK-EPI equation as compared to the MDRD equation (85% vs. 69%, respectively, P <0.001) (4). We congratulate the authors on the excellent job of reporting the situation of estimating equations on the present days. A more appropriated interpretation of the equations performance allows the eventual development of strategies to improve the identified misrepresentations
1) Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization - A systematic review. Early release. Ann Intern Med 2012.
2) Silveiro SP, Araújo GN, Ferreira MN, Souza FD, Yamaguchi HM, Camargo EG. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation pronouncedly underestimates glomerular filtration rate in type 2 diabetes. Diabetes Care. 2011;34(11):2353-5.
3) Camargo EG, Soares AA, Detanico AB, Weinert LS, Veronese FV, Gomes EC, Silveiro SP. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is less accurate in patients with Type 2 diabetes when compared with healthy individuals. Diabet Med. 2011;28(1):90-5.
4) Soares AA, Eyff TF, Campani RB, Ritter L, Weinert LS, Camargo JL, Silveiro SP. Performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) Study equations in healthy South Brazilians. Am J Kidney Dis. 2010;55(6):1162-3
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