Recently, alternative surrogate endpoints such as a 30% or 40% decline in estimated glomerular filtration rate (eGFR) or eGFR slope over 2 to 3 years have been proposed for predicting renal outcomes. However, the impact of GFR estimation methods on the accuracy and effectiveness of surrogate markers is unknown.
We retrospectively enrolled participants in health screening programs at three hospitals from 1995 to 2009. We defined two different participant groups as YR1 and YR3, which had available 1-year or 3-year eGFR values along with their baseline eGFR levels. We compared the effectiveness of eGFR percentage change or slope to estimate end-stage renal disease (ESRD) risk according to two estimating equations (modified Modification of Diet in Renal Disease equation [eGFRm] and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation [eGFRc]) for GFR.
In the YR1 and YR3 groups, 9,971 and 10,171 candidates were enrolled and ESRD incidence during follow-up was 0.26% and 0.19%, respectively. The eGFR percentage change was more effective than eGFR slope in estimating ESRD risk, regardless of the method of estimation. A 40% of decline in eGFR was better than 30%, and a 3-year baseline period was better than a 1-year period for prediction accuracy. Although some diagnostic indices from the CKD-EPI equation were better, we found no significant differences in the discriminative ability and hazard ratios for incident ESRD between eGFRc and eGFRm in either eGFR percentage change or eGFR slope.
There were no significant differences in the prediction accuracy of GFR percentage change or eGFR slope between eGFRc and eGFRm in the general population.
Chronic kidney disease (CKD) has been recognized as a leading cause of morbidity and mortality [
Currently, the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations are widely used to estimate GFR [
We retrospectively enrolled 143,890 participants aged ≥18 years with eGFR of ≥15 mL/min/1.73 m2 who underwent routine health screenings between May 1995 and April 2009 at one of three university-affiliated hospitals (Seoul National University Hospital, Seoul National University Bundang Hospital, and Seoul National University Boramae Medical Center) in Korea. Clinical characteristics including age, sex, medical history, and laboratory values, were collected from the electronic medical records of each participant at each hospital. The eGFR was calculated using the modified MDRD equation (eGFRm) or the CKD-EPI 2009 equation (eGFRc) using isotope dilution mass spectrometry-traceable creatinine values [
Data were presented as percentages for categorical variables and as mean ± standard deviation for continuous variables. Each variable was compared using the t-test for continuous variables and the chi-square or Fisher exact test for categorical variables. Receiver operating characteristic (ROC) curve analyses were performed to examine the discriminant ability of each surrogate marker for predicting ESRD development. The Bland-Altman plot was also used to represent the agreement of eGFR percentage change and eGFR slope according to the equations used for GFR estimation. The areas under the ROC curves (AUCs) of different surrogate markers were compared using the DeLong test.
The performance of surrogate markers was also presented in a manner similar to that of diagnostic tests [
Multivariable Cox proportional hazards analysis with a restricted cubic spline of three knots was performed to examine the association of GFR decline and GFR slope with renal survival. Hazard ratios (HRs) were adjusted by age, sex, and factors related to incident ESRD including systolic blood pressure, diabetes mellitus, eGFR, uric acid, albumin, alkaline phosphatase, glucose, hemoglobin, and urine dipstick protein results. The completeness of these variables is shown in
Of 143,890 subjects aged ≥18 years with eGFRc of ≥15 mL/min/1.73 m2, we finally included 9,972 and 10,171 individuals in the YR1 and YR3 groups, respectively, after excluding patients with either missing eGFRc values or inadequate follow-up data (
At baseline, eGFRc showed a greater mean value compared to eGFRm in both the YR1 (95.8 mL/min/1.73 m2 vs. 95.4 mL/min/1.73 m2) and YR3 (95.9 mL/min/1.73 m2 vs. 95.0 mL/min/1.73 m2) groups. The standard deviation and intersubject coefficient of variation (CV) were greater for eGFRm, as these values were more wildly distributed across the range of eGFR than eGFRc values were (
During the follow-up period, the rate of ESRD incidence was 0.26% and 0.19% in the YR1 and YR3 groups, respectively. Surrogate markers showed better discrimination for ESRD development in the YR3 group compared to the YR1 group (
In the restricted cubic spline model, percentage decline in eGFR was not significantly associated with the risk for ESRD in YR1 (
In subgroup analyses stratified by age, sex, presence of diabetes, eGFR, and presence of proteinuria, adjusted HRs for percentage change in eGFR for each criterion were not significant in YR1, except for eGFR of ≥60 mL/min/1.73 m2 (
In this study, we evaluated the predictive accuracy of several novel surrogate markers for ESRD, including eGFR percentage change and eGFR slope over 1- and 3-year baseline periods in the general population, using different estimating equations. Our findings showed that a 30% or 40% decline in eGFR may be a more accurate surrogate endpoint than the eGFR slope when predicting renal outcome. We also showed that these values are more predictive over a baseline period of 3 years compared to 1 year. Percentage changes in eGFRc showed higher specificity and PPV compared to those in eGFRm and were more likely to be associated with the development of ESRD in patients with eGFR of ≥60 mL/min/1.73 m2. Nevertheless, there were no significant differences in discriminative ability (AUC) or adjusted HRs between eGFRc and eGFRm for predicting ESRD risk.
Recent studies have provided evidence in favor of using alternative surrogate endpoints such as eGFR percentage decline and eGFR slope for predicting renal outcomes [
These early-identifiable markers are important, as conventional endpoints such as creatinine doubling or ESRD require much longer follow-up periods in the general population. The application of these surrogate endpoints can enable early detection and management of CKD progression. However, there are some considerations regarding surrogate markers of eGFR change in patients with high baseline GFR levels. The MDRD and CKD-EPI equations are most commonly used to assess kidney function in medical research and clinical practice. Clinically meaningful differences between the two equations are mainly seen when GFR levels are high [
However, we found that the CKD-EPI equation was better with respect to some diagnostic indices such as specificity, DOR, and PPV, representing more accurate prediction for true endpoint. Our findings can be attributed to (1) less bias in estimating GFR and (2) lower within-subject variability of CKD-EPI equation in patients with high GFR [
There are certain limitations in this study. The number of enrolled patients was relatively small, and the rate of incidence of ESRD was low due to the characteristics of the studied population. Surrogate markers based on changes in eGFR were assessed using serum creatinine measurements merely at the beginning and the end of the defined baseline period. Therefore, GFR variability and acute treatment effects during the baseline period were not considered. In addition, several unmeasured confounders could be present, considering the observational nature of the study. Nevertheless, our study rigorously analyzed the differences in different novel surrogate endpoints with respect to the equations used for estimating eGFR, which allowed us to provide detailed implications. To the best of our knowledge, the impact of GFR-estimating equations on the efficacy of novel surrogate markers for predicting renal outcomes has not been evaluated previously.
In conclusion, there were no significant differences in estimated ESRD risk using the GFR percentage change or eGFR slope between the CKD-EPI equation and the modified MDRD equation in the general population. Surrogate markers using the CKD-EPI equation may be slightly more accurate in patients with high GFRs.
All authors have no conflicts of interest to declare.
Conceptualization: HJC
Data curation: KK, EB, SG, HES, JYR, YY, JCJ, SK
Formal analysis: KK, YY, HJC
Investigation: EB, SG, HES, JYR
Writing–original draft: KK
Writing–review & editing: All authors
All authors read and approved the final manuscript.
1-year period, 6–18 months after the first examination; 3-year period, 30–42 months after the first examination.
eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation.
During 1-year (A) and 3-year (B) periods for ESRD estimation. The eGFR was estimated based on isotope dilution mass spectrometry-traceable creatinine.
eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation; ESRD, end-stage renal disease.
eGFRc (A) and eGFRm (B) over 1 year; eGFRc (C) and eGFRm (D) over 3 years.
eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation.
eGFRc slope (A) and eGFRm slope (B) over 1 year; the eGFRc slope (C) and eGFRm slope (D) over 3 years.
eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation.
Baseline characteristics of the study participants
Variable | Participant |
p-value |
|
---|---|---|---|
YR1 | YR3 | ||
No. of patients | 9,972 | 10,171 | |
Age (yr) | 53.8 ± 11.1 | 53.3 ± 11.2 | 0.004 |
Male sex | 5,980 (60.0) | 5,998 (59.0) | 0.15 |
Diabetes mellitus | 1,053 (10.6) | 1,056 (10.4) | 0.98 |
Hypertension | 2,539 (25.5) | 2,513 (24.7) | 0.25 |
Body mass index (kg/m2) | 24.0 ± 2.9 | 23.9 ± 2.9 | 0.09 |
SBP (mmHg) | 121 ± 17 | 120 ± 17 | 0.14 |
DBP (mmHg) | 75 ± 12 | 75 ± 12 | 0.83 |
Hemoglobin (g/dL) | 14.6 ± 1.5 | 14.5 ± 1.5 | 0.28 |
Cholesterol (mg/dL) | 205 ± 36 | 204 ± 36 | 0.32 |
Triglyceride (mg/dL) | 129 ± 81 | 128 ± 87 | 0.43 |
HDL-C (mg/dL) | 56 ± 14 | 56 ± 14 | 0.18 |
Fasting glucose (mg/dL) | 99 ± 24 | 99 ± 24 | 0.48 |
Protein (g/dL) | 7.4 ± 0.4 | 7.4 ± 0.4 | 0.27 |
Albumin (g/dL) | 4.4 ± 0.3 | 4.4 ± 0.3 | 0.38 |
AST (U/L) | 26 ± 15 | 26 ± 18 | 0.59 |
ALT (U/L) | 29 ± 28 | 29 ± 27 | 0.86 |
ALP (U/L) | 69 ± 21 | 69 ± 20 | 0.50 |
Uric acid (mg/dL) | 5.5 ± 1.4 | 5.5 ± 1.5 | 0.69 |
Calcium (mg/dL) | 9.1 ± 0.5 | 9.1 ± 0.5 | 0.21 |
Phosphorus (mg/dL) | 3.7 ± 0.8 | 3.7 ± 0.8 | 0.98 |
eGFRc (mL/min/1.73 m2) | 95.8 ± 15.4 | 95.9 ± 15.4 | 0.39 |
≥90 | 6,773 (67.9) | 6,949 (68.3) | |
<90, ≥60 | 3,024 (30.3) | 3,049 (30.0) | |
<60, ≥30 | 162 (1.6) | 161 (1.6) | |
<30 | 13 (0.1) | 12 (0.1) | |
eGFRm (mL/min/1.73 m2) | 95.4 ± 27.3 | 95.0 ± 25.9 | 0.31 |
≥90 | 5,148 (51.6) | 5,233 (51.5) | |
<90, ≥60 | 4,592 (46.0) | 4,702 (46.2) | |
<60, ≥30 | 219 (2.2) | 224 (2.2) | |
<30 | 13 (0.1) | 12 (0.1) | |
Proteinuria by dipstick | 0.29 | ||
None or trace | 8,546 (85.7) | 8,832 (87.2) | |
1+ | 1,102 (11.1) | 1,010 (10.0) | |
≥2+ | 288 (2.9) | 283 (2.8) |
Data are expressed as number only, mean ± standard deviation, or number (%).
ALP, alkaline phosphatase; ALT, alanine transaminase; AST, aspartate transaminase; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation; HDL-C, high density lipoprotein cholesterol; SBP, systolic blood pressure; YR1, 1-year eGFR group; YR3, 3-year eGFR group.
Comparison between YR1 and YR3.
Comparison of AUCs of ROC curves for eGFR changes to estimate incident ESRD
Period | Variable | Percent of eGFRm change | Slope of eGFRc per year | Slope of eGFRm per year |
---|---|---|---|---|
YR1 | Percent of eGFRc change (AUC, 0.71) | 0.20 | 0.004 | 0.05 |
Percent of eGFRm change (AUC, 0.68) | 0.001 | 0.03 | ||
Slope of eGFRc per year (AUC, 0.63) | 0.72 | |||
YR3 | Percent of eGFRc change (AUC, 0.84) | 0.08 | <0.001 | 0.001 |
Percent of eGFRm change (AUC, 0.80) | 0.004 | 0.002 | ||
Slope of eGFRc per year (AUC, 0.66) | 0.13 |
AUC, area under the ROC curve; eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation; ESRD, end-stage renal disease; ROC, receiver operating characteristic; YR1, 1-year eGFR group; YR3, 3-year eGFR group.
Diagnostic accuracy of the criteria for eGFR changes to estimate ESRD
Variable | Criteria for eGFRc change (No. of subjects at risk) | Criteria for eGFRm change (No. of subjects at risk) | ||||
---|---|---|---|---|---|---|
YR1 group (n = 9,972) | 30.0% (n = 139) | 40.0% (n = 39) | 57.0% (n = 5) | 30.0% (n = 661) | 40.0% (n = 261) | 55.0% (n = 32) |
Sensitivity | 0.115 (0.024–0.302) | 0.077 (0.009–0.251) | 0.038 (0.001–0.196) | 0.154 (0.044–0.349) | 0.115 (0.024–0.302) | 0.038 (0.001–0.196) |
Specificity | 0.986 (0.984–0.989) | 0.996 (0.995–0.997) | 1.000 (0.999–1.000) | 0.934 (0.929–0.939) | 0.974 (0.971–0.977) | 0.997 (0.996–0.998) |
PPV | 0.022 (0.004–0.062) | 0.051 (0.006–0.173) | 0.200 (0.005–0.716) | 0.006 (0.002–0.015) | 0.011 (0.002–0.033) | 0.031 (0.001–0.162) |
NPV | 0.998 (0.996–0.999) | 0.998 (0.996–0.998) | 0.997 (0.996–0.998) | 0.998 (0.996–0.999) | 0.998 (0.996–0.998) | 0.997 (0.996–0.998) |
LR (+) | 8.438 (2.873–24.782) | 20.678 (5.255–81.360) | 95.635 (11.060–826.976) | 2.329 (0.943–5.754) | 4.448 (1.524–12.982) | 12.340 (1.749–87.062) |
LR (–) | 0.897 (0.781–1.030) | 0.927 (0.829–1.035) | 0.962 (0.891–1.039) | 0.906 (0.769–1.067) | 0.908 (0.790–1.043) | 0.965 (0.893–1.042) |
DOR | 9.409 (2.792–31.709) | 22.318 (5.090–97.861) | 99.420 (10.731–921.066) | 2.571 (0.883–7.482) | 4.899 (1.461–16.416) | 12.794 (1.681–97.376) |
Accuracy | 0.984 (0.981–0.986) | 0.994 (0.992–0.995) | 0.997 (0.996–0.998) | 0.932 (0.927–0.937) | 0.972 (0.968–0.975) | 0.994 (0.927–0.996) |
YR3 group (n = 10,171) | 30.0% (n = 186) | 40.0% (n = 49) | 57.0% (n = 6) | 30.0% (n = 820) | 40.0% (n = 245) | 55.0% (n = 26) |
Sensitivity | 0.526 (0.289–0.756) | 0.368 (0.163–0.616) | 0.105 (0.013–0.331) | 0.526 (0.289–0.756) | 0.368 (0.163–0.616) | 0.105 (0.013–0.331) |
Specificity | 0.983 (0.980–0.985) | 0.996 (0.994–0.997) | 1.000 (0.999–1.000) | 0.920 (0.915–0.925) | 0.977 (0.973–0.979) | 0.998 (0.996–0.998) |
PPV | 0.054 (0.026–0.097) | 0.143 (0.059–0.272) | 0.333 (0.043–0.777) | 0.012 (0.006–0.022) | 0.029 (0.012–0.058) | 0.077 (0.009–0.251) |
NPV | 0.999 (0.998–1.000) | 0.999 (0.998–0.999) | 0.998 (0.997–0.999) | 0.999 (0.998–1.000) | 0.999 (0.998–0.999) | 0.998 (0.997–0.999) |
LR (+) | 30.359 (19.338–47.660) | 89.053 (45.954–172.570) | 267.158 (51.999–1372.600) | 6.596 (4.284–10.157) | 15.715 (8.608–28.691) | 44.526 (11.309–175.312) |
LR (–) | 0.482 (0.300–0.774) | 0.634 (0.450–0.894) | 0.895 (0.767–1.044) | 0.515 (0.320–0.827) | 0.647 (0.459–0.912) | 0.897 (0.769–1.046) |
DOR | 62.980 (25.280–156.899) | 140.417 (52.691–374.200) | 298.471 (51.207–1739.690) | 12.815 (5.192–31.627) | 24.299 (9.483–62.266) | 49.647 (10.870–226.754) |
Accuracy | 0.982 (0.979–0.984) | 0.995 (0.993–0.996) | 0.998 (0.997–0.999) | 0.919 (0.914–0.925) | 0.975 (0.972–0.978) | 0.996 (0.995–0.997) |
Data are expressed as eGFR change (%) or index (95% confidence interval).
DOR, diagnostic odds ratio; eGFR, estimated glomerular filtration rate; eGFRc, eGFR by the 2009 Chronic Kidney Disease-Epidemiology Collaboration creatinine equation; eGFRm, eGFR by the modified Modification of Diet in Renal Disease equation; ESRD, end-stage renal disease; LR (–), negative likelihood ratio; LR (+), positive likelihood ratio; NPV, negative predictive value; PPV, positive predictive value; YR1, 1-year eGFR group; YR3, 3-year eGFR group.