Overall, our results suggest that categorization of kidney function using the CKD-EPI equation more appropriately stratifies middle-aged individuals according to risk of important clinical outcomes as compared to the conventional MDRD Study equation. The prevalence of CKD stage 3 (eGFR 30–59 ml/min/1.73m2) at baseline was reduced from 2.5% (n=347) to 1.4% (n=196) when comparing the CKD-EPI and MDRD study equations in a large, community-based middle-aged population. Importantly, participants who were reclassified upward from CKD stage 3 based on the MDRD Study equation to mildly reduced eGFR (from <60 to 60–89 ml/min/1.73m2) by the CKD-EPI equation had lower risks of all clinical outcomes as compared to those who were not reclassified.
Improved risk stratification using categories of eGFRCKD-EPI
is partially a function of inherent properties of the equation: lower risk populations, i.e., female, whites, and younger,3, 28
are systematically assigned to a higher eGFR category as compared to use of the MDRD Study equation. Indeed, the significantly lower risk for all-cause mortality, CHD, and stroke among persons who were reclassified upward as compared with those who were not reclassified was attenuated by the adjustment for age, gender, and race. The coefficients of age, gender, and race in the survival model may compensate for the different age, gender, and race terms in the CKD-EPI and MDRD study equations. Nevertheless, these findings suggest that the total information content in serum creatinine and demographics is only marginally improved if one calculates the eGFRCKD-EPI
and uses them in a risk equation along with the same demographics. Indeed, risk reclassification improvement based on 10-year risk by the CKD-EPI equation was marginal when adjusted for other risk factors.
The gain by the CKD-EPI equation was also limited when spline terms of eGFR were implemented in the models. Again, it seems that coefficients of multiple spline terms can compensate for differences between the MDRD and CKD-EPI study equations. Predicted risk by spline models allows for a different risk association across GFR, limiting differences between eGFR equations which use identical variables. Nevertheless, since clinical decisions and guidelines are based on eGFR categories, the improvement in risk prediction with eGFR categories is clinically important.
Importantly, for ESRD, an outcome directly linked to decreased eGFR, more accurate risk reclassification based on eGFR categories by the CKD-EPI equation remained statistically significant even after the adjustment for demographic variables. Furthermore, it is of note that net reclassification improvement was significantly positive for all outcomes in most subgroups according to age, gender, or race (Table S5
). These data indicate that the CKD-EPI GFR estimate is more closely related to risk, classifying a smaller and higher risk subgroup as having CKD stage 3. Thus, in a middle-aged population, CKD-EPI eGFR focuses the attention of clinicians on a subgroup that is more likely to benefit from interventions.
Both the MDRD and CKD-EPI study equations are limited by the information available in serum creatinine and demographics. Further improvements are likely to require additional markers such as serum cystatin C, which improves risk prediction29
and, when added to creatinine, GFR estimation.2
Cystatin C standardization across methods and laboratories is lagging that of serum creatinine. Thus, it is anticipated that eGFR based on serum creatinine will continue to be used in most clinical practice settings.12
Clinical guidelines recommend that clinical laboratories should report eGFR using the MDRD Study equation whenever serum creatinine measurement is requested.3, 4
Indeed, about 70% of laboratories are currently reporting eGFR along with serum creatinine results.6
Although false-positive CKD due to underestimation of GFR by the MDRD Study equation is a concern,30
the original CKD-EPI and our results suggest the new CKD-EPI equation reduce this false-positive rate.
The present study also raises important interpretative issues about the CKD-EPI equation. In blacks, eGFRCKD-EPI
does not differ as much from eGFRMDRD
as in whites and, consequently, there were lower net reclassification improvements for all outcomes in blacks as compared to whites. The majority of blacks in the study population used to develop the CKD-EPI equation had CKD with reduced GFR.12
Therefore, the CKD-EPI equation may lack precision in GFR estimation ≥60 ml/min/1.73m2
but the risk relationship with all outcomes was at least as strong as in whites (data not shown). Further studies are needed to evaluate the accuracy of the CKD-EPI equation among individuals of different race and ethnicity groups with mildly decreased or normal GFR levels.
Participants with eGFRCKD-EPI or eGFRMDRD ≥ 120 ml/min/1.73m2 had statistically significantly higher risks for all-cause mortality and ESRD. The result for all-cause mortality was not unexpected, as high eGFR can result from low serum creatinine due to muscle wasting secondary to ill health, reflecting inherent limitations of all serum creatinine-based GFR equations.
Why eGFR ≥120 ml/min/1.73m2
by both equations was associated with incident ESRD is unclear. Participants in this category by both equations were likely to be black, to have higher body mass index, and to have diabetes at baseline as compared to the reference group ( and Table S1
). These results suggest that the high eGFR group in this study over-represents diabetic and obese persons with hyperfiltration at risk for progression to CKD. The over-representation of blacks might also contribute to this finding. Blacks are known to have higher risk of ESRD and are at risk for a more rapid decline in GFR as compared to whites.31
Indeed, persons with eGFR ≥120 ml/min/1.73m2
who had incident ESRD in our study were mostly black (16 of 16 for eGFRCKD-EPI
and 20 of 23 for eGFRMDRD
Reliability of eGFR at high values is another important issue. Individuals, particularly blacks, with measured GFR ≥120 ml/min/1.73m2
were under-represented in the populations, from which the CKD-EPI and MDRD study equations were derived,7, 12
limiting the ability to quantify hyperfiltration and its progression. Indeed, GFR estimates by both equations have lower precision at higher GFRs.8, 9, 12
Some limitations of the present study should be mentioned. First, since the ARIC study consists of a middle-aged, bi-ethnic community-based population of the US, additional studies are needed in younger populations, the elderly, or other ethnicities. Second, there were relatively few participants with eGFR of 30–59 ml/min/1.73m2, the range where eGFR alone is used to define CKD and risk relationships become steeper. Finally, albuminuria was not measured at baseline. Thus, we could not evaluate eGFR and albuminuria simultaneously along with other factors which are important for the most accurate risk prediction.
In conclusion, the CKD-EPI equation was recently developed through a large collaborative effort to reduce bias and improve precision and accuracy in estimating measured GFR. The equation uses the same variables (serum creatinine, age, gender and race) as the MDRD Study equation, facilitating its implementation in computerized algorithms to estimate GFR in clinical practice and laboratories. This study shows that in a large community-based middle-age population the CKD-EPI equation more appropriately classified individuals with respect to risk of ESRD, mortality, CHD and stroke as compared to the MDRD Study equation. This demonstrates that the improved accuracy in estimating GFR by the CKD-EPI equation translated to improved risk prediction and greater clinical utility among middle-aged individuals.
Descriptive Text for Online Delivery
About: Characteristics of Participants According to Clinical Categories of eGFRMDRD
About: Reclassification of eGFR Categories by the CKD-EPI and the MDRD Study Equations, Stratified According to All-Cause Mortality (yes or no) During Follow-up
About: Reclassification of eGFR Categories by the CKD-EPI and MDRD Study Equations, Stratified According to Incident CHD (yes or no) During Follow-up
About: Reclassification of eGFR categories by the CKD-EPI and MDRD Study Equations, Stratified According to Incident Stroke (yes or no) During Follow-up
About: Net Reclassification Improvement by the CKD-EPI Equation Among Participants With eGFR < 120 mL/min/1.73 m2 by Both Equations
About: Net Reclassification Improvement by the CKD-EPI Equation based on 10-year risk categories (<5%, 5-<10%, 10-<20%, and≥20%)