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Published ahead of print on June 8, 2006
Clin J Am Soc Nephrol 1: 787-795, 2006
© 2006 American Society of Nephrology
doi: 10.2215/CJN.00140106
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Epidemiology and Outcomes

Evaluation of GFR Estimating Equations in the General Community: Implications for Screening

William F. Clark, Jennifer J. Macnab, Salina J. Chen, Rita Suri, Louise Moist, and Amit X. Garg

University of Western Ontario and London Health Sciences Centre, London, Ontario, Canada

Address correspondence to: Dr. William F. Clark, 800 Commissioners Road E., London, Ontario, Canada N6A 4G5. Phone: 519-685-8361; Fax: 519-685-8047; E-mail: William.clark{at}lhsc.on.ca


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
The Kidney Disease Outcomes Quality Initiative has recommended the use of GFR estimating equations to detect silent chronic kidney disease (CKD) in the community. The benefit of general reporting of CKD must be balanced with the harm of mislabeling people who do not have CKD. The popular Cockcroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) GFR estimating equations were compared with the recently devised Rule equation in a representative community population sample (2166) divided into subsamples with (385) and without (1781) previous renal impairment. The prevalence of CKD was CG > MDRD >> Rule estimates. The magnitude of difference in prevalence of CKD as detected by the MDRD and CG versus the Rule equation increases markedly when the subsamples with (30.8 and 29.7 versus 17.5%) and without (12 and 11.3 versus 3.0%) previous kidney impairment are compared. General demographic and potential or known risk factors were used in a logistic regression model to assess the association with CKD. The MDRD estimates note female gender (odds ratio 2.19; 95% confidence interval 1.63 to 2.95) and both MDRD and the Rule equations identify hypertension and diabetes as significant CKD risk factors. All estimating equations identify age to be associated with CKD. The annualized serial decline in GFR was CG > MDRD > Rule estimates. Only the Rule GFR estimates detected a greater decline in renal impaired versus unimpaired populations. The calibrated Rule equation seems to perform better than CG and MDRD (CKD 3 versus 11.3 to 12%) but lacks validation against gold standards for community-based screening.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Early detection and treatment of chronic kidney disease (CKD) may delay or prevent the development of end-stage kidney disease, cardiovascular morbidity, and mortality (1–5). In an attempt to improve the early detection of CKD, the Kidney Disease Outcomes Quality Initiative (K/DOQI) of the National Kidney Foundation recently published clinical practice guidelines recommending the use of estimating equations of GFR on the basis of serum creatinine determinations (6). These guidelines have defined CKD as an estimated GFR (eGFR) of <60 ml/min per 1.73 m2 for at least 3 mo. Several centers now are reporting the eGFR when a serum creatinine is ordered using either the abbreviated Modification of Diet in Renal Disease (MDRD) or the Cockcroft-Gault (CG) equation, both of which were developed in individuals with CKD (7–10).

Many studies have compared MDRD and CG equations with a variety of clearance techniques in various specialized population samples (11–17). Both equations frequently underestimate true GFR in people with clearances >60 ml/min per 1.73 m2 (14–17). Rule et al. (17) devised a new GFR estimating equation that is based on the serum creatinine in a healthy transplant donor as well as a CKD population in a step toward more accurately determining GFR when the diagnosis of kidney disease is unknown. The objective of this study was simply to compare the application of the MDRD, CG, and Rule GFR equations on estimates of prevalence of CKD in a general community cohort and to discuss which formula may be best to use in that setting.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
The study was funded by a research grant from the Ministry of Health and Long-Term Care of the province of Ontario and was approved by the Human Ethics Committee of the University of Western Ontario.

Participant Sample
In May 2000, Walkerton, a rural community of 5000 residents in Ontario, Canada, suffered bacterial contamination of its municipal water supply (18). In February 2002, we established a clinic (funded by the Ontario Ministry of Health and Long-Term Care) to screen the population to characterize the long-term burden of illness attributable to the water contamination and to coordinate health services for individuals who were at risk for complications. Residents from Walkerton and the surrounding area were invited to attend the clinic regardless of whether they drank the water and regardless of whether they were ill at the time of the outbreak. For the purposes of this comparative study of GFR equations, we report on participants who were older than 14 yr as of May 2000 and enrolled in the study (approximately two thirds of the community; Table 1). The whole sample is representative of the general population (Figure 1). It is divided into two subsamples, A and B. Subsample A are those people who have no history of renal impairment by chart, self-report, or laboratory audit (serum creatinine ≥104 µmol/L if female and ≥137 µmol/L if male or urine albumin/creatinine ratio ≥3.39 or >0.15 g protein/24 h and/or Albustix ≥0.3 g/L albumin and/or blood in urine with male individuals). Subsample B are those who have a confirmed history of renal impairment (serum creatinine ≥104 µmol/L if female and ≥137 µmol/L if male or urine albumin/creatinine ratio ≥3.39 or >0.15 g protein/24 h urine and/or Albustix ≥0.3 g/L albumin by medical or laboratory record (Figure 1).


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  		Table 1. Characteristics of the (WHS) cohort compared with the population distribution at the time of the outbreaka

 

Figure 1
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  		Figure 1. Flowchart. aGeneral population sample included all participants. bSubsample A was defined as no previous confirmed renal impairment. cSubsmaple B was defined as having confirmed history of renal impairment. *Renal impairment was defined as kidney failure and/or hemolytic uremic syndrome and/or elevated serum creatinine (≥104 µmol/L if female and ≥137 µmol/L if male) and/or random urine protein ≥0.3 g/L and/or 24-h urine protein ≥0.15 g and/or albumin:creatinine ratio ≥3.39 mg/mmol and/or blood in random urine sample for male individuals.

 
Study Protocol
Information was collected on all participants by means of a computer-assisted health interview. The annual screening protocol includes the assessment of height, weight, and BP; an interview-assisted health questionnaire; and laboratory protocol for blood and urine testing. The urine albumin-to-creatinine ratio was calculated on a random specimen using clean-catch techniques and sterile containers (19). A 24-h urine sample was collected for measurement of urine protein using a Vitros 950 autoanalyzer (Rochester, NY) by the pyrocatechol dye procedure with a coefficient of variation for the low control of 2.93% and the high of 3.50%. Serum and urine creatinine were measured by the modified kinetic method of JAFFE using a Vitros 950 autoanalyzer, with an interassay coefficient of <4%. The reference normal range for serum creatinine was 59 to 117 µmol/L for male adults and 51 to 95 µmol/L for female adults. Serum creatinine determinations were calibrated to creatinine levels measured at the Cleveland Clinic in 1992, the primary laboratory for the MDRD study. Duplicate samples were submitted to both our laboratory and the Cleveland Clinic laboratory for 144 patients. Serum creatinine samples from Walkerton were reading on average 3 µmol/L higher compared with the Cleveland Clinic. Coresh et al. outlined the importance of calibration and random variation of the serum creatinine assay and reported that the serum creatinine assays in 2000 for MDRD samples were 7 µmol/L higher than the results in 1992 (20). To correct for calibration differences in creatinine level, we subtracted 10 µmol/L from each creatinine determination carried out in the Walkerton Health Study to reduce calibration error in using the MDRD equation (8). To compare our results with other studies, we used a serum creatinine that was both calibration-corrected and -uncorrected for all three estimating equations (Table 2). The eGFR is calculated using the abbreviated MDRD GFR equation for adults (GFR [ml/min per 1.73 m2] = 186 x serum creatinine [mg/dl]–1.154 x age [years]–0.203 x 0.742 [if female] x 1.21 [if black]), the Rule quadratic equation (GFR [ml/min per 1.73 m2] = exp(1.911 + 5.249/serum creatinine [mg/dl] – 2.114/serum creatinine2 – 0.00686 x age [years] – 0.205 [if female]), and the CG equation (creatinine clearance [ml/min] = (140 – age [years]) x weight [kg]/(72 x serum creatinine [mg/dl]) x 0.85 [if female]) (9,10,17). The estimated creatinine clearance as calculated by the CG equation then was corrected by DuBois and DuBois (21) body surface area (body surface area [m2] = 0.20247 x height [m]0.725 x weight [kg]0.425) to the same unit as MDRD eGFR and Rule quadratic eGFR (ml/min per 1.73 m2).


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  		Table 2. Percentage and 95% CI of GFR <60 ml/min per 1.73 m2 after 3 yr of screeninga

 
Statistical Analyses
All analyses were done using SPSS version 12.0 (SPSS, Inc., Chicago, IL). Age and gender distribution of the Walkerton Health Study cohort were compared with the population distribution at the time of the water contamination (Table 1). The percentage and 95% confidence intervals (CI) of participants with an eGFR of <60 ml/min per 1.73 m2 were calculated for the three population samples and the three different estimating equations (Table 2). Serum creatinine determinations were available for all 3 yr of testing in 1277 participants and were used to calculate the mean annual decline in GFR by the three different estimating equations (Table 1, Figure 2). Odds ratios (OR) were used to summarize the association among CKD (GFR <60 ml/min per 1.73 m2), common demographic data (gender, age after 3-yr screening), and potential or known risk factors (self-report or by audit of medical records for previous diagnosis or medication use for both hypertension and diabetes or by self-report alone for smoking and elevated cholesterol) were included in the logistic regression model, which was used to compute the adjusted OR (Table 3) (22–25).


Figure 2
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  		Figure 2. Average estimated GFR and 95% confidence interval over years: Comparison of Modification of Diet in Renal Disease GFR (MDRD GFR), Cockcroft-Gault GFR (CG GFR), and Rule quadratic GFR (Q-GFR).

 

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  		Table 3. Risk factors for renal impairment (general population sample): Comparison of MDRD GFR, CG GFR, and Rule quadratic GFR using calibrated serum creatinine and uncalibrated serum creatinine

 

   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The age and gender distribution of our study sample is compared with the reference population of the town of Walkerton on the basis of data from the Statistics Canada 2001 census (Table 1). The Walkerton study cohort consisted of 2166 Walkerton area residents who were 15 yr or older and enrolled in the study. The age distribution shows a slight underrepresentation of teenagers and those older than 65 yr and an overrepresentation of the middle aged. Female individuals were more likely to participate in the study than male individuals; however, overall, the study sample is representative of the reference population (Table 1) Ninety-nine percent of the 2166 participants consented to an audit of their medical records, including physicians’ charts and laboratory data. Eighty-four percent had laboratory data. Our study sample for comparative purposes was described as a general population sample of 2166 with a subsample A of 1781 with no previous kidney impairment either self-reported or noted in medical or laboratory records and a subsample B of 385 with confirmed renal impairment (Figure 1, Table 1). The percentage of the general population sample and subsamples that had CKD estimated by the calibration-corrected and -uncorrected MDRD, CG, and Rule quadratic equations were significantly different (Table 2). The pattern of difference was similar in all three populations with the percentage of CKD detected by the CG > MDRD >> Rule quadratic equation. As expected, the prevalence of CKD was greatest in subsample B and least in subsample A using all three estimating equations. However, the magnitude of the difference in burden of CKD detected by the MDRD and CG versus the Rule equation increases markedly from subsample B (renal impairment 30.8 and 29.7 versus 17.5%) to the general population sample (14.7 and 15.1 versus 5.6%) to subsample A (no previous renal impairment 11.3 and 12.0 versus 3%; Table 2).

Calibration correction of the serum creatinine reduces the prevalence of CKD by approximately one third in all populations but does not alter the gradient pattern noted among the various populations (Table 2). Again, the CKD prevalence as detected by the MDRD and CG versus the Rule estimates is approximately 1.5 magnitudes greater in the subsample of those with previous renal impairment, 2.5 in the general population sample, and three- to four-fold greater in the population subsample without previous renal impairment (Table 2).

Gender, age, smoking, elevated cholesterol, diabetes, and hypertension were used in a logistic regression model that compared these as potential risk factors for CKD in the general population sample (Table 3). The MDRD equation found female gender to be associated with CKD (calibrated serum creatinine OR 1.61 [95% CI 1.12 to 2.32]; uncalibrated serum creatinine OR 2.19 [95% CI 1.63 to 2.95]). Previous hypertension and diabetes are noted by both MDRD and Rule equations to be significant risk factors for CKD (Table 3). All the estimating equations identify age as a significant risk factor for CKD. The MDRD identifies this association in the general population as early as the 45- to 54-yr age group using both a calibrated and an uncalibrated serum creatinine (Table 3). The CG equation detects this association in the general population as early as the MDRD equation using an uncalibrated but not a calibrated serum creatinine. The Rule equation does not associate age with CKD by either calibrated or uncalibrated serum creatinine until the 55- to 64-yr age group (Table 3).

A total of 1277 participants who had serum creatinine measurements in each of 3 subsequent years had similar characteristics to our entire population sample and the town of Walkerton (Table 1). The mean age of these participants was 49 (±16). Figure 2 displays the average eGFR over 3 yr using the three different GFR estimating equations with calibrated and uncalibrated serum creatinine determinations for the general sample and subsamples A (no previous kidney impairment) and B (previous kidney impairment). The eGFR of subsample A was higher than the that in the general population, which was higher than subsample B population using all three equations, and a decline in eGFR was seen over the 3 yr using all three formulas (Figure 2). The CG decline in eGFR was greater than that of the MDRD, which was greater than that of the Rule quadratic equation. The pattern of GFR decline was different. CG and MDRD formulas with creatinine calibration estimate a greater rate of decline in those without renal impairment, whereas the Rule equation shows the opposite. Calibration of serum creatinine did reduce the rate of decline (Figure 2).


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
This study provides a direct comparison of the prevalence of CKD as detected by three different GFR estimating equations (with and without calibration-corrected serum creatinine determinations) in a community population sample. This representative sample (age and gender) is separated into a general (whole sample), no previous kidney impairment (subsample A), and previous kidney impairment (subsample B) samples (Figure 1, Table 1). A consistent pattern emerges when we apply the MDRD, CG, and Rule quadratic estimating equations to the three population samples (Table 2). The Rule equation detects fewer cases of CKD than the other two equations, and this difference becomes greater by order of magnitude when we compare those who had previous kidney impairment with those who had no previous history of kidney impairment. A similar pattern is noted when we assess the annual rate of loss of GFR detected by the three equations. The Rule equation measures the least decline in eGFR in the various population samples. The rate of decline in the calibration-corrected Rule eGFR is greater in the subsample with previous kidney impairment than the subsample without a previous history of kidney impairment (Figure 2). The opposite pattern is reported for MDRD and CG estimates. Calibration correction of the serum creatinine reduces the prevalence of CKD by approximately 30 to 40% and alters the gradient pattern observed among the three formulas and the three populations.

The baseline variables that are associated with CKD are different, depending on which formula is used (Table 3). Female gender is associated with an increased risk for CKD using both calibrated and uncalibrated creatinine with the MDRD eGFR. A history of hypertension and diabetes was associated with increased risk for CKD using both MDRD and Rule equations. Increased age was associated with CKD by all three eGFR equations. However, the Rule and, to a lesser extent, the CG eGFR detect this association in an age group 10 to 20 yr older than the MDRD formula (Table 3).

The Rule equation was derived recently to provide a more accurate estimate of GFR in both a healthy transplant donor and a CKD population. Both the MDRD and CG equations were derived in CKD populations only (8–10). The Rule estimated results were compared with iothalamate clearance and were correlated more closely in both the CKD and healthy transplant donors than MDRD calculated GFR (17). The MDRD calculations systematically underestimate GFR in the transplant donor population. A similar pattern of underestimation was reported for the CG formula (14). Our study reflects this systematic underestimation of GFR by both MDRD and CG equations. It also identifies a large increase in prevalence of CKD when using these equations compared with the Rule quadratic estimates in patients with renal impairment (1.5), the general population (2.5), and patients with no renal impairment (three- to four-fold) (Table 2). This propensity of both MDRD and CG estimating equations to identify large segments of the population as having CKD has been reported by Froissart et al. (15). They noted in a referred population for GFR estimation that as many as 20% of patients with measured (Cr51 EDTA) GFR >60 ml/min per 1.73 m2 were labeled as having CKD (15). Identifying large numbers of people with no previous CKD as having CKD will adversely affect them and the health care system. At the individual level, there will be psychologic distress and depression, a negative impact on insurability, and the risk of inappropriate medication dosing. The health care system will be affected by increasing demands for nephrology consultation, testing, and follow-up for potentially nonexistent CKD (26,27).

The 8.3% burden of CKD as detected by the calibration-corrected MDRD GFR in our general population sample is greater than the 5% noted by Coresh et al. (28) for non-Hispanic white patients derived from the National Health and Nutrition Examination Survey (NHANES). We included calibration-corrected and -uncorrected serum creatinine to address the issue of calibration. We do suspect that the majority of centers that now report eGFR are not using a calibration-corrected GFR (MDRD or CG estimates) and are noting that a larger segment of their population has CKD (9.8 to 15.1%) (11,29,30). All three estimating equations detect a decline in GFR with aging, but the onset of CKD as detected by the Rule equation in the 55- to 64-yr age group is more in keeping with a longitudinal study of the effects of aging on loss of renal function (31). Our study indicates the MDRD estimates have a female gender bias for CKD. This gender bias is not consistent with the slight preponderance of male individuals who initiate ESRD programs in the United States, Canada, or Japan (23,32,33). Both Rule quadratic and MDRD equations identify previous hypertension and diabetes as significant risk factors for CKD in a general population, in keeping with research on known risk factors for chronic renal disease (22).

Our referent population includes a wide spectrum of age groups that are representative of a community sample with serial annual serum creatinine determinations unlike the transplant donor studies (Table 1) (13,15–17). Also, because we had access to the participants’ medical and laboratory records and their health survey questionnaire, we were able to separate our general population sample into a subsample A with no previous kidney impairment and a subsample B with previous kidney impairment. This separation clearly identifies a decreasing gradient in prevalence of CKD from renal-impaired to renal-unimpaired populations for all three estimating equations. It also shows a widening increase in the detection of CKD for MDRD and CG versus the Rule quadratic equation as we move from renal-impaired to renal-unimpaired populations. This increase clearly relates to the previously identified GFR underestimation bias of both the MDRD and CG equations in healthy transplant donor populations (14–17). A large number of our population sample had three serial annual serum creatinine determinations. This provided an opportunity to compare the effects of aging on the GFR as calculated by the three different formulas. All formulas demonstrate a decline in GFR. However the Rule, unlike the CG and MDRD calibrated estimates, reveals the expected pattern of a decrease in GFR decline when we compare renal-impaired with -unimpaired population samples. The annualized GFR reduction in the general population is less when comparing the Rule with the MDRD and CG estimates and is more in keeping with longitudinal studies on aging (31). A complete medical and laboratory record coupled with the study questionnaire allowed us to compare the association between CKD as detected by the three estimating equations and several demographic and known risk factors. The major weakness in our study is that we did not use a "gold standard method" for GFR measurement. The calibrated Rule equation seems to perform better, but our findings require validation with gold standard measurements of GFR. Another weakness of our population sample, like both MDRD and CG equations’ original referent populations, is that they are predominantly white (Table 1). Also, our calibration correction for the serum creatinine is based on a comparison of only 144 samples at one point in time with the Cleveland Clinic results and as such may have underestimated the calibration correction needed when using the MDRD formula. This could explain the difference between Coresh’s prevalence of chronic renal failure and ours but underlines the difficulty that is encountered when attempting to use the MDRD estimating equation. It also is possible that our general population sample has a slight increased risk for kidney impairment in the two thirds of the population who experienced gastroenteritis at the time of the water contamination (34). However, we did not note a significant association with self-reported gastroenteritis with the CG or MDRD estimates of CKD but adjusted our OR for this factor (Table 3). If differing severities of this factor are affecting our general and renal-unimpaired study samples as suggested by the Rule estimates, then it would be expected to reduce the gradient that we have noted between renal-impaired and renal-unimpaired population samples. In other words, the four-fold difference that we note between the burden of CKD in the renal-unimpaired population subsample using MDRD and CG versus Rule quadratic equations may be an underestimation of the true difference.

Bostom et al. (16) suggested on the basis of their comparison of GFR estimating equations (did not include the Rule quadratic equation) that they should not be used because of reduced accuracy. Stevens and Levy (7) argued that an estimating equation that uses serum creatinine for GFR, although imperfect, is superior to current physician interpretations using the serum creatinine values only. Until a better formula is developed, the close agreement of the Rule quadratic equation with the iothalamate GFR in transplant donor and CKD populations may make it a more suitable equation to use in general laboratory reporting and community screening programs (17). Our study, although lacking a direct gold standard GFR measure, has identified other findings that support that decision. The Rule estimates are without a gender bias and are more accurate estimates of the effect of age on annualized GFR decline, noting a greater rate of decline in renal-impaired versus renal-unimpaired participants. Age, hypertension, and diabetes (known risk factors for CKD) are strongly associated with Rule CKD estimates.

Adoption of the Rule equation to fulfill the K/DOQI guidelines is less likely to identify a large number of a community population as having CKD. Use of this GFR estimating equation dramatically reduces the number in our community who are labeled with a diagnosis of CKD and the potential emotional, financial, and interventional burden that accompanies that diagnosis (24,25).


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

Received January 11, 2006. Accepted April 19, 2006.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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  34. Garg AX, Moist L, Matsell D, Thiessen-Philbrook HR, Haynes RB, Suri RS, Salvadori M, Ray J, Clark WF; for the Walkerton Health Study Investigators: Risk of hypertension and reduced kidney function after acute gastroenteritis from bacteria-contaminated drinking water. CMAJ 173: 261–266, 2005[Abstract/Free Full Text]



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