There are numerous studies attempting to identify a replacement for serum creatinine as a filtration marker, but no one marker has thus been definitely established 18, 31-34
. Thus, in spite of acknowledged weaknesses,35, 36
GFR estimates based on serum creatinine will remain the mainstay of clinical assessment of kidney function for the foreseeable future, and use of equations that improve the accuracy of GFR estimated from serum creatinine is an important goal. A new creatinine-based estimating equation, the CKD-EPI equation, which is more accurate than the MDRD Study equation, was recently reported. Using the CKD-EPI equation, bias is substantially decreased bias compared to measured GFR, especially among people with eGFR greater than 60 ml/min/1.73m2
, and classification according to the presence or absence of CKD and the stage of CKD is more often correct5
. In a separate publication, it was demonstrated that the addition of diabetes, transplant and weight as predictor variables did not improve performance of the CKD-EPI equation6
. In this report, the bias across the range of eGFR for subgroups defined by demographic and clinical characteristics was described. This has important clinical implications for reporting of eGFR by clinical laboratories and interpretation in practice.
The current analyses demonstrate that compared to the MDRD Study equation, the CKD-EPI equation reduced bias in almost all subgroups. In particular, there was improvement in bias in subgroups at low risk for CKD where underestimation of measured GFR may have led to overestimation of CKD prevalence, including age less than 65 years, women, and whites. For eGFR up to 90 ml/min/1.73 m2, bias is small and consistent across subgroups. Importantly, at this range of eGFR, there is unbiased GFR estimation in groups at increased risk of CKD, including the elderly, blacks, patients with diabetes and organ transplant recipients and the overweight and obese. For eGFR greater than 90 ml/min per 1.73 m2, the overall bias is low, but heterogeneity is observed among subgroups. Despite this heterogeneity, median bias using the CKD-EPI equation is lower than the median bias for eGFR less than 60 ml/min/1.73 m2 using the MDRD Study equation.
The improvement in the performance of the CKD-EPI equation is due in part to the inclusion of a diverse population in the development of the equation. However, the dataset did not include all people in whom GFR will be estimated and therefore the ability to comment on some groups is limited by the available data. Key populations not adequately represented are people with eGFR greater than 60 ml/min/1.73 m2, particularly those older than 65 years of age and racial and ethnic minorities. The study population is also limited by possible selection bias for recruitment in the studies included in the pooled database. Studies in representative populations will be required to overcome this limitation.
The large overestimate observed in transplant recipients at eGFR greater than 90 ml/min/1.73 m2
may be an artifact as few transplant recipients have measured GFR this high. Possibly, the large overestimate that was observed reflects reduced creatinine production and low serum creatinine due to muscle wasting associated with comorbid conditions in transplant recipients selected for GFR measurement. Similarly, the large overestimate in subjects with low BMI and eGFR greater than 90 ml/min/1.73 m2
may also reflect muscle wasting. Endogenous filtration markers other than serum creatinine are likely to be necessary to improve GFR estimation in patients with muscle wasting. Furthermore, it was previously shown that inclusion of terms in the estimating equation for transplant status and body weight did not improve overall performance of the equations in the validation dataset, suggesting this large bias is not related to transplant status or weight per se.6
More accurate estimates has important implications in public health and clinical care. The CKD-EPI equation leads to lower estimated prevalence of CKD in NHANES (the National Health and Nutrition Examination Survey), with reclassification to higher CKD Stages particularly in groups at low risk for CKD. 5
Recent studies in the ARIC (Atherosclerosis Research in Communities) and Aus-Diab (Australian Diabetes, Obesity and Lifestyle) studies have confirmed these findings37, 38
. These studies also showed that participants reclassified to higher GFR stages had lower risk for subsequent adverse events. In addition to improved prognosis, reclassification to a higher GFR stage would lead to benefit across the range of GFR for better detection, evaluation and management of CKD. For example, reclassification would improve clinicians’ ability to adjust drug dosages and identify individuals who may be at increased risk for side effects of medications or diagnostic procedures such as contrast media for imaging or oral phosphate-based solutions in preparation for colonoscopy, and as such would improve patient safety39, 40
. In addition, reporting of eGFR using the CKD-EPI equation would allow reporting of numeric results for eGFR values greater than 60 ml/min/1.73 m2
. The clinical impact of availability of accurate eGFR values above 60 ml/min per 1.73 m2
has not been evaluated because until now there has been no simple clinical tool for accurate estimation. Potential applications include monitoring eGFR decline from normal to mild to moderate reduction, particularly in patients with an increased risk for development and progression of CKD, such as Blacks or patients with diabetes. 41-43
Implementation of reporting of eGFR > 60 ml/min per 1.73 m2 should be performed with adequate education tools such that clinicians do not falsely diagnosis CKD at eGFR ≥ 60 ml/min/1.73 m2
in the absence of persistent markers of kidney damage.
Despite the substantial reduction in bias with the CKD-EPI equation, GFR estimates remain imprecise 5, 6
. Both the MDRD Study and CKD-EPI equations are based on serum creatinine; like all other creatinine-based estimation equations, they suffer the same irremediable limitations of creatinine as a filtration marker 35, 44-46
. The terms for age, sex and race in both equations only capture some of the non-GFR determinants of creatinine, and the coefficients represent average effects observed in the development sample. For patients at the extremes of muscle mass, those with unusual diets, and those with conditions associated with reduced secretion or extra-renal elimination of creatinine, all estimates of GFR based on serum creatinine may be inaccurate. This is particularly relevant for populations who are most likely to require medications, such as the frail elderly, critically ill, or cancer patients 47
. Clinicians should be mindful of muscle mass in interpretation of estimated GFR. Confirmatory tests with exogenous measured GFR or measured creatinine clearance should be performed for people in whom the estimates are thought to be inaccurate or when a highly accurate level is needed such as for toxic medications with a narrow therapeutic index or for some clinical trials looking at change in GFR over time48
, although studies need to be performed to establish reference ranges for creatinine clearance using IDMS traceable creatinine methods.
The strengths of the study include the large diverse study population of people with and without kidney diseases; calibration of the creatinine assays in each study to standardized values; and rigorous statistical techniques for equation development and validation. Comparison of equations in a separate validation dataset overcomes some of the limitations of differences among studies in patient characteristics and methods for measurement of GFR and serum creatinine.
There are limitations to this study. As discussed above, studies were pooled from different populations to develop and validate the CKD-EPI equation. The selection of these study populations according to presence or absence of kidney disease may bias equation performance. This may affect the assessment of performance of either equation, but it would not affect their comparison. Complete data on ethnicity, diabetes type, immunosuppressive agents for transplantation, measures of muscle mass, and other clinical conditions and medications that might affect serum creatinine independently from GFR were not available. These variables may identify particular groups of people who are likely to have large errors in the estimates. However, the variables that were evaluated are the most readily available and easy to ascertain for widespread clinical application. Finally, in the validation dataset, GFR was measured in some individuals using a different exogenous marker than was used in the development of the equations. However, this would affect the performance of both equations, and therefore would not affect the relative performance of the equations.
In summary, the CKD-EPI equation is less biased than the MDRD Study equation in most subgroups defined by demographic and clinical characteristics and level of GFR. Implementation of eGFR reporting using the CKD-EPI equation across the entire range of eGFR will allow a better clinical assessment of kidney function than is now available. The CKD-EPI equation should replace the MDRD Study equation for general clinical use and can be reported throughout the GFR range.