Development and validation of GFR-estimating equations using diabetes, transplant and weight

Development and validation of GFR-estimating equations using diabetes, transplant and weight 13. Gundersen HJG, Jensen EB. The efficiency of systematic sampling in stereology and its prediction. J Microsc 1987; 147: 229–263 14. Heeringa SF, Branten AJW, Deegens JKJ et al. Focal segmental glomerulosclerosis is not a sufficient predictor of renal outcome in patients with membranous nephropathy. Nephrol Dial Transplant 2007; 22: 2201–2207 15. Troyanov S, Roasio L, Pandes M et al. Renal pathology in idiopathic membranous nephropathy: a new perspective. Kidney Int 2006; 69: 1641–1648 449 16. Fogo A, Hawkins EP, Berry PL et al. Glomerular hypertrophy in minimal change disease predicts subsequent progression to focal glomerular sclerosis. Kidney Int 1990; 38: 115–123 17. Shea SM, Raskova J, Morrison AB. A stereologic study of glomerular hypertrophy in the subtotally nephrectomized rat. Am J Pathol 1978; 90: 201–210 18. Heeg JE, de Jong PE, Van Der Hem GK et al. Efficacy and variability of the antiproteinuric effect of ACE inhibition by lisinopril. Kidney Int 1989; 36: 272–279 Received for publication: 3.5.09; Accepted in revised form: 7.9.09 Nephrol Dial Transplant (2010) 25: 449–457 doi: 10.1093/ndt/gfp510 Advance Access publication 30 September 2009 Development and validation of GFR-estimating equations using diabetes, transplant and weight Lesley A. Stevens1 , Christopher H. Schmid1 , Yaping L. Zhang1 , Josef Coresh2 , Jane Manzi2 , Richard Landis3 , Omran Bakoush4 , Gabriel Contreras5 , Saul Genuth6 , Goran B. Klintmalm7 , Emilio Poggio8 , Peter Rossing9 , Andrew D. Rule10 , Matthew R. Weir11 , John Kusek12 , Tom Greene13 and Andrew S. Levey1 1 Tufts Medical Center, Boston, MA, 2 Johns Hopkins University, Baltimore, MD, 3 University of Pennsylvania School of Medicine, Philadelphia, PA, USA, 4 Lund University Hospital, Lund, Sweden, 5 University of Miami Miller School of Medicine, Miami, FL, 6 Case Western Reserve University, Cleveland, OH, 7 Baylor University Medical Center, Dallas, TX, 8 Cleveland Clinic Foundation, Cleveland, OH, USA, 9 Steno Diabetes Center, Gentofte, Denmark, 10 Mayo Clinic, Rochester, MN, 11 University of Maryland Medical Center, MD, 12 National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, MD and 13 University of Utah, Salt Lake City, UT, USA Correspondence and offprint requests to: Lesley A. Stevens; E-mail: Abstract Background. We have reported a new equation (CKD-EPI equation) that reduces bias and improves accuracy for GFR estimation compared to the MDRD study equation while using the same four basic predictor variables: creatinine, age, sex and race. Here, we describe the development and validation of this equation as well as other equations that incorporate diabetes, transplant and weight as additional predictor variables. Methods. Linear regression was used to relate logmeasured GFR (mGFR) to sex, race, diabetes, transplant, weight, various transformations of creatinine and age with and without interactions. Equations were developed in a pooled database of 10 studies [2/3 (N = 5504) for development and 1/3 (N = 2750) for internal validation], and final model selection occurred in 16 additional studies [external validation (N = 3896)]. Results. The mean mGFR was 68, 67 and 68 ml/min/ 1.73 m2 in the development, internal validation and external validation datasets, respectively. In external validation, an equation that included a linear age term and spline terms in creatinine to account for a reduction in the magnitude of the slope at low serum creatinine values exhibited the best performance (bias = 2.5, RMSE = 0.250) among models using the four basic predictor variables. Addition of terms for diabetes and transplant did not improve performance. Equations with weight showed a small improvement in the subgroup with BMI <20 kg/m2 . Conclusions. The CKD-EPI equation, based on creatinine, age, sex and race, has been validated and is more accurate than the MDRD study equation. The addition of weight, diabetes and transplant does not significantly improve equation performance. Keywords: creatinine; development; estimating equation; glomerular filtration rate; validation Introduction Glomerular filtration rate (GFR) is an important indicator of kidney function, critical for detection, evaluation and management of chronic kidney disease (CKD). GFR cannot be practically measured for routine clinical or research purposes, and therefore, serum creatinine is often used to  C The Author 2009. Published by Oxford University Press [on behalf of ERA-EDTA]. All rights reserved. For Permissions, please e-mail: 450 estimate GFR. Several factors affect the level of serum creatinine other than GFR, including the generation of creatinine from muscle metabolism. GFR-estimating equations, such as the Modification of Diet in Renal Disease (MDRD) study equation, including age, sex and race, account for the average differences in muscle mass among these subgroups and have been shown to be a more accurate assessment of the level of kidney function than serum creatinine alone. National and international organizations recommend that clinical laboratories report estimated GFR (eGFR) and that clinicians use eGFR to evaluate kidney function for all patients [1–4]. This is now a considerable body of the literature demonstrating limitations in the performance of the MDRD study equation in people with higher levels of GFR, such as younger patients with diabetes [5–7]. A potential explanation for this limitation is that the MDRD study equation was developed in people with CKD who did not have type 1 diabetes and who were not transplant recipients, who may have differences in creatinine generation that are compared to the people who were included in the MDRD study population that were not captured by the average values for the regression coefficients for age, sex and race. We hypothesized that the performance of the MDRD study equation could be improved by a new equation developed in a diverse study population including individuals with and without diagnosed kidney disease, diabetes and transplants that utilizes novel transformations of creatinine and age, new predictor variables and pairwise interactions among its predictor variables. Our goal was to develop an equation that had improved performance at higher levels of GFR and had consistent performance among subgroups based on clinical and demographic characteristics. We recently reported a new equation, the CKD-EPI equation, based on creatinine, age, sex and race, which is more accurate than the MDRD study equation [8]. However, the precision of this equation remains limited, in part due to non-GFR determinants of serum creatinine that are not captured by creatinine level, age, sex and race. Here, we report on the development and validation of this equation as well as other equations that were developed that also included diabetes, transplant and weight as additional predictor variables. Met (...truncated)