A New Equation to Estimate Glomerular Filtration Rate
Andrew S. Levey, MD; Lesley A. Stevens, MD, MS; Christopher H. Schmid, PhD; Yaping (Lucy) Zhang, MS; Alejandro F. Castro III, MPH;
Harold I. Feldman, MD, MSCE; John W. Kusek, PhD; Paul Eggers, PhD; Frederick Van Lente, PhD; Tom Greene, PhD; and
Josef Coresh, MD, PhD, MHS, for the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)*
Background: Equations to estimate glomerular filtration rate (GFR)
are routinely used to assess kidney function. Current equations
have limited precision and systematically underestimate measured
GFR at higher values.
Objective: To develop a new estimating equation for GFR: the
Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)
equation.
Design: Cross-sectional analysis with separate pooled data sets for
equation development and validation and a representative sample
of the U.S. population for prevalence estimates.
Setting: Research studies and clinical populations (“studies”) with
measured GFR and NHANES (National Health and Nutrition Exam-
ination Survey), 1999 to 2006.
Participants: 8254 participants in 10 studies (equation develop-
ment data set) and 3896 participants in 16 studies (validation data
set). Prevalence estimates were based on 16 032 participants in
NHANES.
Measurements: GFR, measured as the clearance of exogenous
filtration markers (iothalamate in the development data set;
iothalamate and other markers in the validation data set), and linear
regression to estimate the logarithm of measured GFR from stan-
dardized creatinine levels, sex, race, and age.
Results: In the validation data set, the CKD-EPI equation per-
formed better than the Modification of Diet in Renal Disease Study
equation, especially at higher GFR (P ⬍ 0.001 for all subsequent
comparisons), with less bias (median difference between measured
and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m
2
), improved
precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3
mL/min per 1.73 m
2
), and greater accuracy (percentage of esti
-
mated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In
NHANES, the median estimated GFR was 94.5 mL/min per 1.73
m
2
(IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per
1.73 m
2
, and the prevalence of chronic kidney disease was 11.5%
(95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%).
Limitation: The sample contained a limited number of elderly peo-
ple and racial and ethnic minorities with measured GFR.
Conclusion: The CKD-EPI creatinine equation is more accurate
than the Modification of Diet in Renal Disease Study equation and
could replace it for routine clinical use.
Primary Funding Source: National Institute of Diabetes and Diges-
tive and Kidney Diseases.
Ann Intern Med. 2009;150:604-612. www.annals.org
For author affiliations, see end of text.
*For a list of other CKD-EPI staff and collaborators, see the Appendix (avail-
able at www.annals.org).
C
linical assessment of kidney function is part of routine
medical practice for adults and is essential for assessing
overall health; interpreting signs and symptoms; selecting
the correct dosage for drugs that are excreted by the kid-
neys; preparing for invasive diagnostic or therapeutic pro-
cedures; and detecting, evaluating, and monitoring acute
and chronic kidney diseases. The glomerular filtration rate
(GFR) is considered the best overall index of kidney func-
tion in health and disease. The GFR cannot be measured
easily in clinical practice; instead, it is estimated from equa-
tions by using serum creatinine level, age, race, sex, and
body size (1, 2). One such equation, the MDRD (Modi-
fication of Diet in Renal Disease) Study equation, has
gained widespread acceptance (3, 4), and most clinical lab-
oratories estimate GFR by using this equation when serum
creatinine measurement is ordered (5). The MDRD Study
equation is also used to assess the burden of chronic kidney
disease in epidemiologic studies and public health (6). The
prevalence of chronic kidney disease in the United States
has increased from approximately 10% in 1988 to 1994 to
13% in 1999 to 2004, which corresponds to approximately
26.3 million people in 2000 (6, 7).
The MDRD Study equation was developed by study-
ing people with chronic kidney disease, and as such, its
major limitations are imprecision and systematic under-
estimation of measured GFR (bias) at higher values (8).
We sought to develop and validate a new estimating equa-
tion based on the serum creatinine level that would be as
accurate as the MDRD Study equation at a GFR less than 60
mL/min per 1.73 m
2
and more accurate at a higher GFR. We
report development and validation of a new equation and
compare it with the MDRD Study equation for estimating
measured GFR and U.S. prevalence of chronic kidney disease.
METHODS
The CKD-EPI (Chronic Kidney Disease Epidemiol-
ogy Collaboration) is a research group established by the
National Institute of Diabetes and Digestive and Kidney
See also:
Print
Editors’ Notes .............................605
Web-Only
Appendix
Appendix Tables
Appendix Figures
Conversion of graphics into slides
Annals of Internal MedicineArticle
604 © 2009 American College of Physicians
Diseases. The institutional review boards of all participat-
ing institutions approved the study. The Appendix (avail-
able at www.annals.org) provides details about study selec-
tion and analytical methods.
Data Sources
Investigators collaborating with CKD-EPI provided
data from research studies and clinical populations (here-
after referred to as “studies”). In brief, we identified studies
from the MEDLINE database and through investigators’
and collaborators’ contacts (Appendix Figure 1, available
at www.annals.org). Key inclusion criteria were measure-
ment of GFR on the basis of exogenous filtration markers
and ability to calibrate serum creatinine assay. We re-
stricted ourselves to studies that used urinary clearance of
iothalamate for development and internal validation of
equations, but included studies that used iothalamate and
other filtration markers for external validation. We ran-
domly divided 10 studies (6 research studies and 4 clinical
populations) (3, 9 –15), comprising 8254 participants, into
separate data sets for development (5504 participants) and
internal validation (2750 participants) (Appendix Table 1,
available at www.annals.org). We used 16 other studies (6
research studies and 10 clinical populations) (13, 16–28),
comprising 3896 participants, for external validation (Ap-
pendix Table 2, available at www.annals.org).
Laboratory Methods
For all studies, we recalibrated serum creatinine values
to standardized creatinine measurements by using the
Roche enzymatic method (Roche–Hitachi P-Module in-
strument with Roche Creatininase Plus assay, Hoffman-La
Roche, Basel, Switzerland) at the Cleveland Clinic Re-
search Laboratory (Cleveland, Ohio) as described elsewhere
(29, 30). We compared new equations with the MDRD
Study equation (estimated GFR ⫽ 175 ⫻ standardized
S
cr
⫺1.154
⫻ age
⫺0.203
⫻ 1.212 [if black] ⫻ 0.742 [if fe
-
male]), in which GFR is expressed as mL/min per 1.73 m
2
of body surface area and S
cr
is expressed in mg/dL (4).
Analyses in the Development Data Set
We prespecified a process for developing equations
that uses transformations of continuous variables and the
inclusion of additional variables and interactions to de-
velop a large number of candidate equations. We used
least-squares linear regression to relate measured GFR to
serum creatinine and clinical characteristics available in all
databases. Predictor variables included serum creatinine,
age, race (black vs. white and other), and sex in all models,
as in the MDRD Study equation, and additional variables
in some models (diabetes [yes/no], previous organ trans-
plantation [yes/no], and weight, as assigned by the individ-
ual studies). We fit regression models to all patients in the
pooled development data set without accounting for study
in the models. We transformed GFR and serum creatinine
to natural logarithms to reflect their multiplicative (in-
verse) relationship and to stabilize variance across the range
of GFR.
We determined appropriate transformations of log serum
creatinine and age by first fitting nonparametric smoothing
splines to characterize the shape of the relationship of these
factors with mean log measured GFR and then creating piece-
wise linear splines to correspond to observed nonlinearity (Ap-
pendix Table 3, available at www.annals.org) (31). We in-
cluded additional variables and pairwise interactions between
them if they were significant (P ⬍ 0.010 for additional vari-
ables and P ⬍ 0.001 for interactions) and improved model
performance (relative reduction in root mean square error
ⱖ2%) (Appendix Table 4, available at www.annals.org).
Analyses in the Internal Validation Data Set
We verified the statistical significance of predictor vari-
ables and interactions for all models and the relative rank-
ing of performance among models. We derived final co-
efficients for each model by combining the development
and internal validation data sets.
Analyses in the External Validation Data Set
We compared performance of the multiple models de-
veloped in the development data set with each other as well
as with the MDRD Study equation by using a prespecified
process. We performed comparisons in the overall data set
and in subgroups defined by estimated GFR, clinical char-
acteristics, and type of filtration marker (iothalamate vs.
noniothalamate). We ranked equations on performance
and ease of application. We performed sensitivity analyses
for all steps to evaluate the robustness of results across
studies. We selected a single model as the best equation for
general use, referred to here as the “CKD-EPI equation.”
Metrics for Equation Performance
We compared measured and estimated GFR for
each patient graphically by plotting measured GFR and
Context
The MDRD (Modification of Diet in Renal Disease) Study
equation is commonly used to estimate glomerular filtra-
tion rate (GFR), but it is imprecise and underestimates
GFR at higher values.
Contribution
These researchers pooled data from studies to develop and
validate a new equation, the CKD-EPI (Chronic Kidney
Disease Epidemiology Collaboration) equation, to predict
GFR. The CKD-EPI equation was somewhat more precise
and accurate than the MDRD Study equation, especially at
higher GFRs. Using the new equation could decrease
false-positive results—the mislabeling of people with high
GFR as having poor kidney function.
Caution
The sample used to develop the CKD-EPI equation in-
cluded few elderly and nonwhite persons. Evaluation of
the equation in these populations is needed.
—The Editors
ArticleDevelopment and Comparison of a New Equation to Estimate GFR
www.annals.org 5 May 2009 Annals of Internal Medicine Volume 150 • Number 9 605
the difference (measured GFR ⫺ estimated GFR)
against estimated GFR. We assessed bias as the median
difference, with positive values indicating an under-
estimation of measured GFR. We assessed precision as
interquartile range (IQR) for the differences. We as-
sessed accuracy as root mean square error, relative to
measured GFR and the percentage of estimates within
30% of the measured GFR (P
30
), which takes into ac
-
count greater errors at higher values and the absolute
values of the difference between measured and estimated
GFR. We calculated CIs by bootstrap methods (2000
bootstraps) (32) for median differences and IQR of the
differences and by the binomial method for P
30
.We
computed receiver-operating characteristic (ROC)
curves for measured GFR less than 90, 75, 60, 45, 30,
and 15 mL/min per 1.73 m
2
. We defined GFR stages as
greater than 90, 60 to 89, 30 to 59, 15 to 29, and less
than 15 mL/min per 1.73 m
2
(1). We compared sensi
-
tivity, specificity, and concordance between estimated
and measured GFR among equations by using the Mc-
Nemar test. We compared concordance of estimated
GFR stages among equations by using the sign test.
We used R, version 2 (Free Software Foundation, Bos-
ton, Massachusetts), and SAS, version 9.1 (SAS Institute,
Cary, North Carolina), to compute all analyses.
Estimation of U.S. Prevalence
The NHANES (National Health and Nutrition Exami-
nation Survey) is a cross-sectional, multistage, stratified, clus-
tered probability sample of the civilian, noninstitutionalized
population of the United States conducted by the National
Center of Health Statistics and appropriate for estimates of
prevalence of chronic conditions in the United States. We
analyzed data from the 1999 to 2000, 2001 to 2002, 2003 to
2004, and 2005 to 2006 surveys. We limited our study pop-
ulation to 16 032 participants (3754 from 1999 to 2000,
4297 from 2001 to 2002, 4017 from 2003 to 2004, and
3964 from 2005 to 2006) who were 20 years or older, had
completed the examination in the mobile examination center,
were not pregnant or menstruating, were not missing serum
creatinine measurements, and did not have an estimated GFR
less than 15 mL/min per 1.73 m
2
. Our methods are similar to
those used in previous studies (7).
The NHANES did not measure GFR. We measured
serum creatinine by using a kinetic-rate Jaffe method and
recalibrated results to standardized creatinine measure-
ments obtained at the Cleveland Clinic Research Labora-
tory (33). We estimated GFR by using the MDRD Study
equation and the newly developed CKD-EPI equation. We
truncated estimates that exceeded 200 mL/min per 1.73
m
2
at that level. Methods for collection, analysis, and re
-
porting for albuminuria are described elsewhere (7, 34).
Table 1. Patient Characteristics
Characteristic Development
Data Set (
n
ⴝ 5504)
Internal Validation
Data Set (
n
ⴝ 2750)
External Validation
Data Set (
n
ⴝ 3896)
P
Value*
Mean age (SD), y 47 (15) 47 (15) 50 (15) ⬍0.001
Age, n (%) ⬍0.001
⬍40 y 2058 (37) 1018 (37) 1136 (29)
41–65 y 2751 (50) 1403 (51) 2192 (56)
⬎65 y 695 (13) 329 (12) 568 (15)
66–70 y 476 (9) 220 (8) 254 (7)
71–75 y 150 (3) 66 (2) 185 (5)
76–80 y 41 (0) 30 (1) 92 (2)
⬎80 y 28 (0) 13 (0) 37 (0)
Women, n (%) 2391 (43) 1215 (44) 1767 (45) 0.084
Race, n (%) 0.001
Black 1728 (32) 857 (31) 384 (10)
Hispanic 247 (5) 106 (4) 67 (2)
Asian 62 (1) 38 (1) 67 (2)
White or other 3467 (63) 1749 (64) 3378 (87)
Kidney donor, n (%) 694 (13) 336 (12) 608 (16) ⬍0.001
Transplant recipient, n (%) 241 (4) 119 (4) 1134 (29) ⬍0.001
Diabetes, n (%) 1581 (29) 825 (30) 1089 (28) 0.173
Mean height (SD), cm 170 (10) 170 (10) 170 (10)† 0.90
Mean weight (SD), kg 82 (20) 82 (20) 79 (18) ⬍0.001
Mean body mass index (SD), kg/m
2
28 (6) 28 (6) 27 (6)† ⬍0.001
Mean body surface area (SD), m
2
1.93 (0.20) 1.93 (0.20) 1.90 (0.23)† ⬍0.001
Mean GFR (SD), mL/min per 1.73 m
2
‡
68 (40) 67 (40) 68 (36) 0.70
Mean serum creatinine level (SD) ⬍0.001
mol/L 146 (106) 148 (106) 134 (88)
mg/dL 1.65 (1.20) 1.67 (1.20) 1.52 (1.00)
GFR ⫽ glomerular filtration rate.
* For comparison of the combined development and internal validation data sets vs. the external validation data set.
† The sample size is 3875 because of missing data.
‡ To convert GFR from mL/min per 1.73 m
2
to mL/s per m
2
, multiply by 0.0167.
Article Development and Comparison of a New Equation to Estimate GFR
606 5 May 2009 Annals of Internal Medicine Volume 150 • Number 9 www.annals.org
We defined albuminuria as an albumin-to-creatinine ratio
greater than 30 mg/g. We used repeated measurements,
obtained approximately 2 weeks after the original exami-
nation in a subset of 1241 participants in NHANES from
1988 to 1994, to estimate the persistence of albuminuria
(34). The NHANES data do not include accurate diag-
noses of the causes of kidney disease. We defined chronic
kidney disease as persistent albuminuria or estimated GFR
less than 60 mL/min per 1.73 m
2
(1). We classified
chronic kidney disease according to our previously defined
estimated GFR stages. We compared distributions of esti-
mated GFR, estimated GFR stages, and prevalence of
chronic kidney disease for both equations.
We performed the analyses by using Stata, version 10.0
(StataCorp, College Station, Texas), and incorporated the
sampling weights from the complex NHANES sampling de-
sign to obtain unbiased estimates. We obtained standard er-
rors for all estimates by using the Taylor series (linearization)
method and followed NHANES-recommended procedures
and weights (35–37). We used bootstrap methods imple-
mented in Stata to derive CIs for the prevalence estimates for
chronic kidney disease stages, incorporating persistence data
on albuminuria. We applied prevalence estimates to the 2000
U.S. Census data to estimate the number of persons with
chronic kidney disease in the United States.
Role of the Funding Source
The study was funded by a cooperative agreement
with the National Institute of Diabetes and Digestive and
Kidney Diseases, which allows them substantial involve-
ment in the design of the study and in the collection,
analysis, and interpretation of the data. The funding source
was not required to approve publication of the finished
manuscript.
RESULTS
Selection of Studies and Clinical Characteristics
Table 1 shows the clinical characteristics of the partic-
ipants in each data set. In the development data set, mean
measured GFR was 68 mL/min per 1.73 m
2
(SD, 40) and
ranged from 2 to 190 mL/min per 1.73 m
2
. The external
validation data set had similar mean measured GFR, sex,
and proportion of diabetes to the development and inter-
nal validation data sets but differed in age; body size; and
the proportion of ethnic and racial minorities, kidney do-
nors, and organ transplant recipients.
Description of the CKD-EPI Equation
The CKD-EPI equation for estimating log GFR in-
cludes log serum creatinine (modeled as a 2-slope linear
spline with sex-specific knots at 62
mol/L [0.7 mg/dL] in
women and 80
mol/L [0.9 mg/dL] in men), sex, race,
and age on the natural scale. In comparison, the MDRD
Study equation includes log serum creatinine without a
spline, sex, race, and age on the log scale (Appendix Table
5, available at www.annals.org). The spline for log serum
creatinine in the CKD-EPI equation allows steeper and
identical slopes of GFR versus serum creatinine for men
and women at creatinine levels above the knots and less
steep and different slopes for men and women at creatinine
levels below the knots. The slope for the CKD-EPI equa-
tion is similar to that of the MDRD Study equation above
the knots but less steep below the knots, which leads to
higher estimated GFR at lower creatinine values. The co-
efficient for black persons is greater than 1.0 in both equa-
tions, which leads to a higher estimated GFR for blacks
than white persons at all levels of serum creatinine; how-
ever, the CKD-EPI equation yields a smaller difference in
estimated GFR between black persons and white persons
than does the MDRD Study equation. In the CKD-EPI
equation, the relationship between GFR and sex varies by
serum creatinine level. For example, the predicted female-
to-male ratio for estimated GFR varies from 0.83 to 0.92
when the serum creatinine level is between 44 and 71
mol/L (0.5 and 0.8 mg/dL) and is 0.75 when the serum
creatinine level is greater than 80
mol/L (⬎0.9 mg/dL).
However, it is constant at 0.74 for all serum creatinine
values in the MDRD Study equation. Estimated GFR is
inversely related to age in both equations, but at older ages,
the age term on the natural scale in the CKD-EPI equation
leads to lower estimated GFR for the same creatinine level
than does the log age term in the MDRD Study equation.
In the external validation data set, models with additional
variables for diabetes, organ transplantation, weight, or inter-
actions among variables did not substantially improve per-
Table 2. The CKD-EPI Equation for Estimating GFR on the
Natural Scale*
Race and Sex Serum
Creatinine
Level,
mol/L
(mg/dL)
Equation
Black
Female ⱕ62 (ⱕ0.7) GFR ⫽ 166 ⫻ (Scr/0.7)
⫺0.329
⫻ (0.993)
Age
⬎62 (⬎0.7) GFR ⫽ 166 ⫻ (Scr/0.7)
⫺1.209
⫻ (0.993)
Age
Male ⱕ80 (ⱕ0.9) GFR ⫽ 163 ⫻ (Scr/0.9)
⫺0.411
⫻ (0.993)
Age
⬎80 (⬎0.9) GFR ⫽ 163 ⫻ (Scr/0.9)
⫺1.209
⫻ (0.993)
Age
White or other
Female ⱕ62 (ⱕ0.7) GFR ⫽ 144 ⫻ (Scr/0.7)
⫺0.329
⫻ (0.993)
Age
⬎62 (⬎0.7) GFR ⫽ 144 ⫻ (Scr/0.7)
⫺1.209
⫻ (0.993)
Age
Male ⱕ80 (ⱕ0.9) GFR ⫽ 141 ⫻ (Scr/0.9)
⫺0.411
⫻ (0.993)
Age
⬎80 (⬎0.9) GFR ⫽ 141 ⫻ (Scr/0.9)
⫺1.209
⫻ (0.993)
Age
CKD-EPI ⫽ Chronic Kidney Disease Epidemiology Collaboration; GFR ⫽ glo-
merular filtration rate.
* Expressed for specified race, sex, and serum creatinine level. To convert GFR
from mL/min per 1.73 m
2
to mL/s per 1.73 m
2
, multiply by 0.0167. We derived
equation coefficients from pooled development and internal validation data sets.
The CKD-EPI equation, expressed as a single equation, is GFR ⫽ 141 ⫻
min(Scr/
,1)
␣
⫻ max(Scr/
,1)
⫺1.209
⫻ 0.993
Age
⫻ 1.018 [if female] ⫻ 1.159 [if
black], where Scr is serum creatinine,
is 0.7 for females and 0.9 for males,
␣
is
⫺0.329 for females and ⫺0.411 for males, min indicates the minimum of Scr/
or
1, and max indicates the maximum of Scr/
or 1. In this table, the multiplication
factors for race and sex are incorporated into the intercept, which results in dif-
ferent intercepts for age and sex combinations.
ArticleDevelopment and Comparison of a New Equation to Estimate GFR
www.annals.org 5 May 2009 Annals of Internal Medicine Volume 150 • Number 9 607
formance compared with the simpler models. Table 2
shows the CKD-EPI equation in a form that could be
implemented in clinical laboratories.
Comparison of Performance
The Figure and Table 3 show the performance of
both equations in the validation data set. (Appendix Table
6, available at www.annals.org, shows performance in the
development and internal validation data sets.) The CKD-
EPI equation yielded improved median difference (bias),
IQR, P
30
, and root mean square error (P ⬍ 0.001 for all).
The CKD-EPI equation was as accurate as the MDRD
Study equation in the subgroup with estimated GFR less
than 60 mL/min per 1.73 m
2
and substantially more ac
-
curate in the subgroup with estimated GFR greater than 60
mL/min per 1.73 m
2
. Results were consistent across studies
and subgroups defined by age, sex, race, diabetes, trans-
plant status, and body mass index (data not shown).
The ROC curves to detect GFR less than 90, 75, 60,
45, 30 and 15 mL/min per 1.73 m
2
did not differ between
the CKD-EPI and MDRD Study equations. The areas un-
der the ROC curves were 0.95, 0.96, 0.96, 0.97, 0.97, and
0.98, respectively, for both equations. For detection of
measured GFR less than 60 mL/min per 1.73 m
2
, the
estimated GFR value with highest combination of sensitiv-
ity and specificity was 59 mL/min per 1.73 m
2
for the
CKD-EPI equation and 55 mL/min per 1.73 m
2
for the
MDRD Study equation. The sensitivity and specificity of
estimated GFR less than 60 mL/min per 1.73 m
2
were
91% and 87% according to the CKD-EPI equation and
95% and 82% according to the MDRD Study equation
(P ⬍ 0.001 for both comparisons). Concordance of esti-
mated and measured GFR stages was 69% for the CKD-
EPI equation and 64% for the MDRD Study equation
(P ⬍ 0.001). Table 4 shows classification of GFR stages
estimated by the CKD-EPI and MDRD Study equations,
with significant (P ⬍ 0.001) reclassification to higher val-
ues by the CKD-EPI equation at values of 30 to 59
mL/min per 1.73 m
2
and higher. Among those classified
differently by the 2 equations, classification by the CKD-
EPI equation was correct more often than classification by
the MDRD Study equation (63% vs. 34%; P ⬍ 0.001).
Overall, our results indicate better classification by esti-
mated GFR with the CKD-EPI equation, primarily be-
cause of reduction in bias.
Comparison of Estimated GFR and Prevalence of Chronic
Kidney Disease in NHANES
The transformations and coefficients for variables in
the CKD-EPI equation translate into differences in the
estimated GFR distribution and prevalence of chronic kid-
ney disease among NHANES participants from 1999 to
2006 compared with the MDRD Study equation. Both
equations show a similar distribution at estimated GFR less
than 45 mL/min per 1.73 m
2
, but the CKD-EPI equation
leads to a shift to the right at higher levels of estimated
GFR (Appendix Figure 2, top, available at www.annals
.org). Mean estimated GFR (⫾SE) was 93.2 ⫾ 0.39 using
the CKD-EPI equation versus 86.3 ⫾ 0.40 mL/min per
1.73 m
2
using the MDRD Study equation (median, 94.5
mL/min per 1.73 m
2
[IQR, 79.7 to 108.1 mL/min per
1.73 m
2
] versus 85.0 mL/min per 1.73 m
2
[IQR, 72.9 to
98.5 mL/min per 1.73 m
2
]). Comparison of classification
of stages of estimated GFR showed reclassification to
higher values with the CKD-EPI equation at values of 30
Figure. Performance of the CKD-EPI and MDRD Study
equations in estimating measured GFR in the external
validation data set.
Both panels show the difference between measured and estimated versus
estimated GFR. A smoothed regression line is shown with the 95% CI
(computed by using the lowest smoothing function in R), using quantile
regression, excluding the lowest and highest 2.5% of estimated GFR. To
convert GFR from mL/min per 1.73 m
2
to mL/s per m
2
, multiply by
0.0167. CKI-EPD ⫽ Chronic Kidney Disease Epidemiology Collabora-
tion; GFR ⫽ glomerular filtration rate; MDRD ⫽ Modification of Diet
in Renal Disease.
Article Development and Comparison of a New Equation to Estimate GFR
608 5 May 2009 Annals of Internal Medicine Volume 150 • Number 9 www.annals.org
