Journal Information
Vol. 34. Issue. 2.March 2014
Pages 0-272
Vol. 34. Issue. 2.March 2014
Pages 0-272
DOI: 10.3265/Nefrologia.pre2013.Dec.12226
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Tables for estimating the glomerular filtration rate using the new CKD-EPI equation from serum creatinine concentration
Tablas para la estimación del filtrado glomerular mediante la nueva ecuación CKD-EPI a partir de la concentración de creatinina sérica
C.. Canala, Cristina Canalb, R.. Pellicera, Rodrigo Pellicerb, C.. Facundoa, Carme Facundob, S.. Gràcia-Garciac, Silvia Gràcia-Garciad, R.. Montañés-Bermúdezc, Rosario Montañés-Bermúdezd, C.. Ruiz-Garcíaa, César Ruiz-Garcíab, M.. Furlanoa, Mónica Furlanob, I.. DaSilvaa, Iara K. DaSilvab, J.. Ballarína, José A. Ballarínb, J.. Bovera, Jordi Boverb
a Servicio de Nefrolog??a, Fundaci?? Puigvert, Spain,
b Servicio de Nefrolog??a, Fundaci?? Puigvert,
c Servicio de Laboratorio, Fundaci?? Puigvert, Spain,
d Servicio de Laboratorio, Fundaci?? Puigvert,
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La enfermedad renal crónica (ERC) y las complicaciones que de ella se derivan se han convertido en un importante problema sanitario, tanto por los recursos que se requieren en los estadios finales de la enfermedad como por las complicaciones secundarias que conlleva, por lo que su diagnóstico precoz es considerado hoy de gran importancia. Las guías KDIGO 2013 recientemente publicadas basan la definición y clasificación de la ERC en los valores de filtrado glomerular y albuminuria como criterios de estadiaje y marcadores pronóstico de la enfermedad. Las ecuaciones MDRD y MDRD-IDMS (cuando se utilizan valores de creatinina obtenidos por métodos con trazabilidad al método de referencia) son las más utilizadas, pero tanto las guías internacionales KDIGO 2013 como el nuevo documento de consenso sobre la ERC 2013, en el que han participado diez sociedades científicas bajo la dirección de la Sociedad Española de Nefrología, recomiendan su sustitución por la ecuación CKD-EPI. Nuestro objetivo ha sido, tal y como hicimos con ecuaciones previas, elaborar unas tablas que permitan conocer el valor del filtrado glomerular estimado mediante la ecuación CKD-EPI a partir de la concentración sérica de creatinina, la edad y el sexo, y de este modo proporcionar un instrumento que facilite la difusión de esta nueva ecuación, especialmente en ámbitos en los que no se calcule de modo automático.

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Enfermedad renal crónica
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Filtrado glomerular
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Chronic kidney disease (CKD) and its complications have become a major healthcare problem, both due to the resources that are required in the final stages of the disease and to secondary complications. As such, its early diagnosis is considered to be very important nowadays. The recently published 2013 KDIGO guidelines base the definition and classification of CKD on glomerular filtration values and albuminuria as staging criteria and prognostic markers of the disease. The MDRD and MDRD-IDMS equations (when creatinine values can be traced to the reference method) are those most used, but both the 2013 KDIGO international guidelines and the new 2013 CKD consensus document, in which ten scientific societies participated under the direction of the Spanish Society of Nephrology, recommend to be replaced by the CKD-EPI equation. Our objective has been, as with previous equations, to develop tables that display the estimated glomerular filtration rate value using the CKD-EPI equation from serum creatinine concentration, age and sex, and thereby provide an instrument that facilitates the dissemination of this new equation, particularly in settings where it is not calculated automatically.

Chronic kidney disease
Glomerular filtration rate
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Chronic kidney disease (CKD) is a major healthcare problem, as has been stated in different epidemiological studies,1-3 not only because patients who reach end-stage ESRD require many healthcare resources, but also due to the high cardiovascular disease burden, hospitalisation and premature death inherent in its diagnosis.4

With the aim of preventing or delaying complications associated with CKD,5 in 2002, the American National Kidney Foundation (NKF) Kidney Disease Outcome Quality Initiative (K/DOQI) published clinical guidelines to define and classify it into different stages.6 In 2005, the Kidney Disease: Improving Global Outcomes (KDIGO) international initiative accepted the definition and classification initially proposed by the K/DOQI7 with minor modifications, and in January 2013, it published guidelines on the diagnosis, classification and management of CKD that confirm the previous definition of CKD and classify it into stages based on glomerular filtration rate (GFR) values and the degree of albuminuria.8

In recent years, GFR has been considered the best index for assessing renal function. Given that it is not feasible to measure it in daily practice, various equations have been developed that allow its estimation from serum creatinine concentration, age, sex and race. However, other renal lesion markers have been required to define CKD when the GFR is >60ml/min/1.73m2 (albuminuria, haematuria, abnormalities in imaging tests, etc.), fundamentally due to the imprecision and inaccuracy of estimated glomerular filtration rate (eGFR) equations, especially for higher values.6-8 Although many equations have been published, the most used in our country are those of the Modification of Diet in Renal Disease9 study, whether in its classical version MDRD-410 or IDMS-MDRD, according to whether the serum creatinine method is traceable with respect to the isotopic dilution mass spectrometry (IDMS)11 reference measuring procedure. These equations also have been used to assess CKD prevalence in epidemiology and public health studies.12,13

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) is a research group dependent on the National Institute of Diabetes and Digestive and Kidney Disease which has been set up to develop equations to estimate the GFR using data from different studies. In 2009, this group published a new equation using standardised creatinine methods, obtained from a population with higher GFR values, with a mean eGFR of 93.2ml/min/1.73m2 being obtained using the CKD-EPI equation compared to 86.3ml/min/1.73m2 with the IDMS-MDRD equation.14 This is the equation recommended by the new 2013 KDIGO guidelines, given that it is more accurate than IDMS-MDRD for high GFR values, although it is highly imprecise, and as such, it is not useful for classifying CKD in stages 1 and 2, with signs of renal lesion also being required for the latter.8 The improvement in the predictive capacity of the real GFR, especially between values of 60 and 90ml/min/1.73m,2 and its greater capacity in the prognosis of overall mortality, cardiovascular episodes and risk of ESRD15 mean that we must consider the new CKD-EPI equation as the equation of choice in the future. In fact, the recently published “Chronic Kidney Disease Consensus Document”, in which ten scientific societies participated under the direction of the Spanish Society of Nephrology (S.E.N.), recommends using this equation.16

However, the recommendation to use CKD-EPI has not yet been implemented by most clinical laboratories,9 and as such, there is a need for tools that allow serum creatinine concentration to be quickly converted to the eGFR value using this equation. With this objective in mind, we have calculated and designed a table that allows the GFR to be estimated from serum creatinine concentration, age and sex, using the CKD-EPI equation, as we did previously with the IDMS-MDRD formula.17




To create these tables, we used a spreadsheet with the Excel 7 software (Microsoft, USA). They are the result of applying the CKD-EPI formula to the mean values for age and creatinine concentration intervals, according to sex. Given the characteristics of the majority population in our country, we have omitted the correction factor for black individuals.




The tables show the eGFR value calculated using the CKD-EPI formula according to serum creatinine concentration, age and sex, using mean creatinine and age values for each interval and it is graded according to the different CKD stages. The results are displayed in two different tables, one in which serum creatinine concentration values are expressed in conventional units (mg/dl) and the other, which uses the International System (µmol/l) (Tables 1 and 2). The colour indicates the CKD stage to which the eGFR value corresponds.




As is well-known, most scientific societies,6,7,18-24 including the S.E.N. and the Spanish Society of Clinical Biochemistry and Molecular Pathology,25 currently advise using eGFR through equations obtained by measuring serum creatinine concentration, age, sex and race. These equations have been a great step forward in early diagnosis and classification of CKD stages, with resulting major advantages, since they allow different treatments to be established that are aimed at stopping or slowing down kidney disease progression and achieving early treatment of its complications (anaemia, secondary hyperparathyroidism, etc.).26-28

Until recently, the MDRD equation was recommended by most clinical guidelines and scientific societies6,7,18,29-31 and it has been demonstrated that the eGFR obtained from this equation is also useful for adjusting drug doses, since it is better suited than the Cockcroft-Gault equation for GFR32 values lower than 60ml/min/1.73m2. However, the MDRD equation has a series of limitations due to the population used in its development,33 who were individuals with different degrees of CKD, which resulted in its imprecision and systematic underestimation of the real GFR, particularly for GFR34-39 values greater than 60ml/min/1.73m2. This underestimation may cause some individuals to undergo unnecessary studies, receive non-optimal doses of renal clearance drugs or avoid important but potentially nephrotoxic diagnostic procedures.

Due to all of the above, the need to seek new renal function markers or new GFR estimation equations have been advocated, which may improve the results of MDRD, especially for GFR greater than 60ml/min/1.73m2. In 2009, the CKD-EPI group published a new equation developed from a population of 8254 individuals with different clinical characteristics, with or without kidney disease, which included serum creatinine concentration, age, sex and race as variables.14 The GFR was measured in all individuals by iothalamate clearance (mean 68ml/min/1.73m2, standard deviation = 40ml/min/1.73m2) and serum creatinine (mean 145μmol/l) using methods that were traceable with respect to the IDMS reference method. The mean age of the population was 47 years old, with 9% of patients aged between 66 and 70 years old and 3% older than 71. They developed different equations according to race, sex and serum creatinine concentration value. The comparison of CKD-EPI with IDMS-MDRD demonstrated that the former was more accurate, particularly with regard to GFR values greater than 60ml/min/1.73m2 and this was the reason for which the authors reached the conclusion that CKD-EPI should replace IDMS-MDRD in standard clinical practice, even though it was highly imprecise compared to directly measuring the GFR. The application of CKD-EPI in the NHANES study (1999-2006) (National Survey on Health and Nutrition Examination) demonstrated that the median eGFR was 94.5ml/min/1.73m2 compared with 85ml/min/1.73m2 estimated with IDMS-MDRD, with a CKD prevalence of 11.5% compared to 13.1%, a reduction basically caused by a decrease in cases classified by IDMS-MDRD as stage 3 CKD.

In a study published by our group, the first that attempted to assess the new CKD-EPI equation in our setting in a large patient cohort, we confirmed that the new equation produced higher values than those obtained with IDMS-MDRD, which resulted in a reclassification of patients to higher CKD stages, such that 9.8% of cases that were classified as stage 3b CKD were changed to 3a, 17% of 3a CKD were changed to stage 2 and 15.7% were changed from stage 2 CKD to stage 1.40 Furthermore, the analysis of age by subgroup demonstrated that this change towards higher GFR stages was greater in patients under 70 years of age. In the over 70 group, we observed a 90% concordance for CKD stages 2 to 5; by contrast, for those assigned to stage 1 CKD by IDMS-MDRD, a high number of cases were classified as stage 2 CKD by CKD-EPI. These data are similar to those obtained in a study carried out recently in our country in primary care, where fewer differences were observed between both equations for GFR estimation in older individuals than in the younger population.41 Similar results were reported in a recently published study in which it was observed that CKD-EPI is less biased and more accurate than MDRD also in an aged European population older than 74 years old, with the equation for this age range being as satisfactory as in young subjects.12

As was previously mentioned, we believe it is important to highlight that in various studies, the CKD-EPI equation was associated with a better prognostic classification than IDMS-MDRD with respect to overall mortality, cardiovascular episodes and end-stage renal disease.43-45 Likewise, the results of a recent meta-analysis conclude that CKD-EPI classifies fewer individuals with CKD and more reliably categorises the risk of mortality and ESRD than the IDMS-MDRD equation in a wide population range.15

New equations have recently been published based on serum creatinine concentration that aim to improve precision and decrease bias;46 the current guidelines consider their use to be acceptable provided that they demonstrate more accuracy than CKD-EPI. We should also remember that the use of cystatin C serum concentration or eGFR by equations from it is subject to variables that are not dependent on the GFR and that its methods of measurement are being standardised. For now its use is recommend as a confirmatory measurement in adults with a GFR between 45 and 59ml/min/1.73m2 without other renal lesion markers. In this case, the recommended equation is CKD-EPI for recently published standardised cystatin C.47

Due to the above, we believe that providing tools that allow a quick conversion of serum creatinine concentration to eGFR using this new equation could be useful, particularly when clinical laboratories do not have it incorporated in their reports, whether it be in our country or abroad, such as Latin America. It is important to note that the CKD-EPI equation is only applicable if standardised methods of measuring creatinine are employed. Furthermore, today, measuring eGFR is an important parameter on which many guidelines base the referral of patients to nephrologists, amongst others.

In summary, although the method currently recommended for determining the eGFR would be automatic calculation of CKD-EPI formulae by laboratories, in the meantime the availability of these tables allows the visualisation and conversion of standardised serum creatinine concentration by doctors, in order that they may transform it quickly and simply into a more clinically significant parameter, such as eGFR. Likewise, this additional information may provide a higher predictive capacity than isolated creatinine concentration or the eGFR equations used previously.


Conflicts of interest


The authors declare that they have no conflicts of interest related to the contents of this article.

Table 1. Calculation of the glomerular filtration rate according to serum creatinine concentration (mg/dl) and age using the CKD-EPI equation (white individuals).

Table 2. Calculation of the glomerular filtration rate according to serum creatinine concentration (µmol/l) and age using the CKD-EPI equation (white individuals).

Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007;298(17):2038-47. [Pubmed]
Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health-problem approaches and initiatives-a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 2007;72(3):247-59. [Pubmed]
Chadban SJ, Briganti EM, Kerr PG, Dunstan DW, Welborn TA, Zimmet PZ, et al. Prevalence of kidney damage in Australian adults: the AusDiab kidney study. J Am Soc Nephrol 2003;14(7 Suppl 2):S131-8. [Pubmed]
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004;351(13):1296-305. [Pubmed]
Levey AS, Beto JA, Coronado BE, Eknoyan G, Foley RN, Kasiske RL, et al. Controlling the epidemic of cardiovascular disease in chronic renal disease: what do we know? What do we need to learn? Where do we go from here? Am J Kidney Dis 1998;32: 853-905.
National Kidney Foundation. K/DOQI Clinical Practice Guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-266. [Pubmed]
Levey AS, Eckardt KU, Tsukamoto Y, Levin A, Coresh J, Rossert J, et al. Definition and classification of chronic kidney disease: a position statement from kidney disease: Improving Global Outcomes (KDIGO). Kidney Int 2005;67(6):2089-100. [Pubmed]
KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int Suppl 2013;3(1):1-308.
Gr??cia-Garcia S, Monta????s-Berm??dez R, Morales-Garc??a LJ, D??ez-de Los R??os MJ, Jim??nez-Garc??a J??, Mac??as-Blanco C, et al. Current use of equations for estimating glomerular filtration rate in Spanish laboratories. Nefrologia 2012;32(4):508-16. [Pubmed]
Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Chronic Kidney Disease Epidemiology Collaboration: Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006;145(4):247-54. [Pubmed]
Miller WG. Reporting estimated GFR: a laboratory perspective [Editorial]. Am J Kidney Dis 2008;52:645-8. [Pubmed]
Delanaye P, Cavalier E, Mariat C, Maillard N, Krzesinski JM. MDRD or CKD-EPI study equations for estimating prevalence of stage 3 CKD in epidemiological studies: which difference? Is this difference relevant? BMC Nephrol 2010;11:8.
Levey AS, Andreoli SP, DuBose T, Provenzano R, Collins AJ. CKD: common, harmful, and treatable??World Kidney Day 2007. Am J Kidney Dis 2007;49:175-9.
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al.; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150(9):604-12. [Pubmed]
Matsushita K, Mahmoodi BK, Woodward M, Emberson JR, Jafar TH, Jee SH, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA 2012;307(18):1941-51. [Pubmed]
Documento de consenso sobre la Enfermedad Renal Cr??nica. 27 Noviembre 2012. Available at: [Accessed: 2 de junio de 2013].
Canal C, Pellicer R, Rocha CI, Calero F, Gracia S, Monta????s R, et al. Tablas para la estimaci??n del filtrado glomerular a partir de la creatinina plasm??tica. Nefrologia 2008;28(3):317-24. [Pubmed]
CARI. Caring for Australasians with Renal Impairment. Available at: [Accessed: 5 de mayo de 2013].
Mathew TH, Australasian Creatinine Consensus Working Group. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: a position statement. Med J Aust 2005;183(3):138-41. [Pubmed]
Crowe E, Halpin D, Stevens P, Guideline Development Group. Early identification and management of chronic kidney disease: summary of NICE guidance. BMJ 2008;337:a1530.
Canadian Society of Nephrology. Professional Practice Guidelines. Available at: [Accessed: 2 de junio de 2013].
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003;289(19):2560-72. [Pubmed]
Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al.; American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003;108(17):2154-69.
Brosius FC 3rd, Hostetter TH, Kelepouris E, Mitsnefes MM, Moe SM, Moore MA, et al.; American Heart Association Kidney and Cardiovascular Disease Council; Council on High Blood Pressure Research; Council on Cardiovascular Disease in the Young; Council on Epidemiology and Prevention; Quality of Care and Outcomes Research Interdisciplinary Working Group. Detection of chronic kidney disease in patients with or at increased risk of cardiovascular disease: a science advisory from the American Heart Association Kidney and Cardiovascular Disease Council; the Councils on High Blood Pressure Research, Cardiovascular Disease in the Young, and Epidemiology and Prevention; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: developed in collaboration with the National Kidney Foundation. Circulation 2006;114(10):1083-7.
Gracia S, Monta????s R, Bover J, Cases A, Deulofeu R, Mart??n de Francisco AL, et al. Documento de consenso: Recomendaciones sobre la utilizaci??n de ecuaciones para la estimaci??n del filtrado glomerular en adultos. Nefrologia 2006;26(6):658-65. [Pubmed]
Finkelstein FO, Story K, Firanek C, Mendelssohn D, Barre P, Takano T, et al. Health-related quality of life and hemoglobin levels in chronic kidney disease patients. Clin J Am Soc Nephrol 2009;4(1):33-8. [Pubmed]
Bover J, Farr?? N, Andr??s E, Canal C, Olaya MT, Alonso M, et al. Update on the treatment of chronic kidney disease-mineral and bone disorder. J Ren Care 2009;35(Suppl 1):19-27. [Pubmed]
Bover J, Canal C, Marco H, Fern??ndez-Llama P, Bosch RJ, Ballar??n J. Diagnostic procedures and rationale for specific therapies in chronic kidney disease-mineral and bone disorder. Contrib Nephrol 2008;161:222-33. [Pubmed]
Mathew TH, Johnson DW, Jones GR, Australasian Creatinine Consensus Working Group. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: revised recommendations statement. Med J Aust 2007;187(8):459-63. [Pubmed]
Archibald G, Bartlett W, Brown A, Christie B, Elliott A, Griffith K, et al. UK Consensus Conference on Early Chronic Kidney Disease. Nephrol Dial Transplant 2007;22(9):2455-7.
The Renal Association: the UK CKD Guidelines. Available at: [Accessed: 3 de junio de 2013].
Monta??es-Bermudez R, Gracia-Garcia S. Use of estimated glomerular filtration formulas for dose adjustment. Nefrologia 2012;32(2):253-5. [Pubmed]
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461-70.
Bostom AG, Kronenberg F, Ritz E. Predictive performance of renal function equations for patients with chronic kidney disease and normal serum creatinine levels. J Am Soc Nephrol 2002;13:2140-4. [Pubmed]
Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med 2004;141:929-37. [Pubmed]
Rule AD, Gussak HM, Pond GR, Bergstralh EJ, Stegall MD, Cosio FG, et al. Measured and estimated GFR in healthy potential kidney donors. Am J Kidney Dis 2004;43:112-9. [Pubmed]
Ibrahim H, Mondress M, Tello A, Fan Y, Koopmeiners J, Thomas W. An alternative formula to the Cockcroft-Gault and the modification of diet in renal diseases formulas in predicting GFR in individuals with type 1 diabetes. J Am Soc Nephrol 2005;16:1051-60. [Pubmed]
Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault Equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 2005;16:459-66. [Pubmed]
Lin J, Knight EL, Hogan ML, Singh AK. A comparison of prediction equations for estimating glomerular filtration rate in adults without kidney disease. J Am Soc Nephrol 2003;14:2573-80. [Pubmed]
Monta????s Berm??dez R, Bover Sanju??n J, Oliver Samper A, Ballar??n Cast??n JA, Gr??cia Garc??a S. Assessment of the new CKD-EPI equation to estimate the glomerular filtration rate. Nefrologia 2010;30(2):185-94. [Pubmed]
Salvador-Gonz??lez B, Rodr??guez-Latre LM, G??ell-Mir?? R, ??lvarez-Funes V, Sanz-R??denas H, Tovillas-Mor??n FJ. Estimation of glomerular filtration rate by MDRD-4 IDMS and CKD-EPI in individuals of 60 years of age or older in primary care. Nefrologia 2013;33(4):552-63. [Pubmed]
Kilbride HS, Stevens PE, Eaglestone G, Knight S, Carter JL, Delaney MP, et al. Accuracy of the MDRD (Modification of Diet in Renal Disease) study and CKD-EPI (CKD Epidemiology Collaboration) equations for estimation of GFR in the elderly. Am J Kidney Dis 2013;61(1):57-66. [Pubmed]
Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD Study equation for estimated GFR: the Atherosclerosis Risk in Communities (ARIC) study. Am J Kidney Dis 2010;55(4):648-59. [Pubmed]
White SL, Polkinghorne KR, Atkins RC, Chadban SJ. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKDEPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: The AusDiab (Australian Diabetes, Obesity and Lifestyle) Study. Am J Kidney Dis 2010;55(4):660-70. [Pubmed]
Shafi T, Matsushita K, Selvin E, Sang Y, Astor BC, Inker LA, et al. Comparing the association of GFR estimated by the CKD-EPI and MDRD study equations and mortality: the third national health and nutrition examination survey (NHANES III). BMC Nephrology 2012;13:42. [Pubmed]
Schaeffner ES, Ebert N, Delanaye P, Frei U, Gaedeke J, Jakob O, et al. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Intern Med 2012;157(7):471-81. [Pubmed]
Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al.; CKD-EPI Investigators. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367(1):20-9. [Pubmed]
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