Comparison of Drug Dosing Recommendations Based on Measured GFR and Kidney Function Estimating Equations

Article Outline

• Abstract
• Methods
  • Sources of Data
  • Measurements
  • GFR and Creatinine Clearance Estimation
  • Variables
  • Statistical Analyses
    • Assignment to FDA Guidance to Industry Kidney Function Category
    • Drug Simulation Study
• Results
  • Study Population
  • Assignment to FDA Guidance to Industry Kidney Function Category
    • Comparison With Measured GFR
    • Comparison to the MDRD Study Equation
  • Recommended Drug Dosages
    • Comparison to Measured GFR
    • Comparison to the MDRD Study Equation
• Discussion
• Acknowledgements
• References
• Copyright

Background

Kidney disease alters the pharmacokinetic disposition of many medications, requiring dosage adjustment to maintain therapeutic serum concentrations. The Cockcroft-Gault (CG) equation is used for pharmacokinetic studies and drug dosage adjustments, but the Modification of Diet in Renal Disease (MDRD) Study equation is more accurate and more often reported by clinical laboratories than the CG equation.

Study Design

Diagnostic test study.

Settings & Participants

Pooled data set for 5,504 participants from 6 research studies and 4 clinical populations with measured glomerular filtration rate (GFR).

Index Test

Estimated kidney function using the MDRD Study and CG equations incorporating actual (CG) or ideal body weight (CG_IBW) and standardized serum creatinine concentrations.

Reference Test

Measured GFR assessed by using iodine-125–iothalamate urinary clearance.

Outcome

Concordance of assigned kidney function categories designated by the Food and Drug Administration (FDA) Guidance for Industry for pharmacokinetic studies and recommended dosages of 15 medications cleared by the kidneys.

Results

Concordance of kidney function estimates with measured GFR for FDA-assigned kidney function categories was 78% for the MDRD Study equation compared with 73% for the CG equation (P < 0.001) and 66% for the CG_IBW equation (P < 0.001). Concordance between the MDRD Study equation and CG and CG_IBW equations was 78% and 75%, respectively (P < 0.001). Concordance of kidney function estimates with measured GFR for recommended drug dosages was 88% for MDRD Study equation compared with 85% for the CG equation (P < 0.001) and 82% for the CG_IBW equation (P < 0.001), with lower concordance when dosing recommendations for drugs included narrow GFR ranges. Concordance rates between the CG and CG_IBW equations and MDRD Study equation were 89% and 88%, respectively (P < 0.05).

Limitations

Results based on simulation rather than pharmacokinetic studies. Outcome was drug dosage recommendations, rather than observed drug efficacy and safety.

Conclusions

The MDRD Study equation can also be used for pharmacokinetic studies and drug dosage adjustments. As more accurate GFR-estimating equations are developed, they should be used for these purposes.

Index Words: MDRD Study equation, Cockcroft Gault equation, drug dose recommendation

Impairment of kidney function alters the pharmacokinetics of many medications prescribed in both the acute and chronic settings. The Guidance for Industry: Pharmacokinetics in Patients With Impaired Renal Function—Study Design, Data Analysis, and Impact on Dosing and Labeling from the US Food and Drug Administration (FDA), published in 1998 and herein referred to as the FDA Guidance for Industry, recommends that pharmaceutical companies use the Cockcroft-Gault (CG) equation to estimate kidney function, which is incorporated in the design of pharmacokinetic studies and the development of drug dosing guidelines.¹ The rationale for the use of the CG equation is that it was the most commonly used method for assessment of kidney function in clinical practice at the time.

The Modification of Diet in Renal Disease (MDRD) Study equation is now widely recognized as providing more accurate estimates of glomerular filtration rate (GFR) than the CG equation and has been reexpressed for use with standardized serum creatinine values, enabling consistent performance across clinical laboratories after standardization of serum creatinine assays, anticipated to be implemented in all US clinical laboratories by the end of 2009.², ³, ⁴, ⁵, ⁶, ⁷, ⁸, ⁹, ¹⁰, ¹¹, ¹² International and national organizations now recommend that clinical laboratories report estimated GFR (eGFR) when serum creatinine testing is ordered, and the latest surveys from the College of American Pathologists suggest that 70% of clinical laboratories in the United States are now reporting eGFR by using the MDRD Study equation.¹³, ¹⁴, ¹⁵, ¹⁶, ¹⁷, ¹⁸, ¹⁹ Using these readily available GFR estimates likely would facilitate drug dosing decisions. However, many clinicians are reluctant to use them for this purpose because the FDA Guidance for Industry, and consequently dosing adjustments listed in product labels for most medications, recommends using the CG equation.

Many studies have compared drug dosing recommendations based on the CG equation with those based on the MDRD Study equation,²⁰, ²¹, ²², ²³, ²⁴ but none have compared these recommendations with those based on measured GFR in a large clinically diverse population. The 2 objectives of this study are to: (1) compare kidney function categories defined by the FDA Guidance for Industry using kidney function estimates based on the MDRD Study equation and CG equation using actual and ideal body weight (CG_IBW) with measured GFR, and (2) compare differences in hypothetical recommended dosing of 15 medications that are cleared by the kidneys in 5,504 patients from 6 research and 4 clinical populations with diverse clinical characteristics.

Back to Article Outline

Methods 

Sources of Data 

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) is a research group formed to develop and validate improved estimating equations for GFR by pooling data from research studies and clinical populations (hereafter referred to as “studies”), which include individuals with diverse clinical characteristics with and without kidney disease across a wide range of GFRs. Methods for identification of and inclusion criteria for these studies have been previously described.² The population described in this study includes people whose measurements were used for equation development.

Measurements 

All studies measured GFR by using urinary clearance of iothalamate. Serum creatinine assays were calibrated to the creatinine reference standard by using the Roche enzymatic method (Roche-Hitachi Module-P instrument with Roche Creatinine Plus assay; Roche Diagnostics, Indianapolis, IN) at the Cleveland Clinic Research Laboratory.²

GFR and Creatinine Clearance Estimation 

Kidney function was estimated by using the equations listed in Box 1. The MDRD Study equation was expressed for use with creatinine values standardized by using isotope-dilution mass spectrometry (IDMS). The CG equation cannot be reexpressed for use with IDMS-standardized creatinine values because, to our knowledge, the original serum creatinine samples are not available for calibration. Measured GFR and the MDRD Study equations are adjusted for body surface area (BSA) and generally are reported in milliliters per minute per 1.73 m².²⁷ We converted these BSA-adjusted values by multiplying by each individual's BSA and dividing by 1.73 m² so that all were expressed in units of milliliters per minute, the units of GFR that are expressed in the majority of FDA-approved drug dosing labels. Values for eGFR were rounded to the nearest whole number.

Box 1. Equations Used in This Study

 

The IDMS-traceable 4-variable MDRD Study equation¹¹:

The CG equation²⁵:

The CG_IBW equation:

For CG_IBW, IBW was calculated as 50 kg + [2.3 kg × (height in inches − 60)] for men and 45.5 kg + [2.3 kg × (height in inches − 60)] for women. If ACT was less than IBW, then ACT was used or if ACT exceeded IBW by > 30%, ABW was used according to the following formula²⁶:

Abbreviations: ABW, adjusted body weight; ACT, actual body weight; CG, Cockcroft-Gault; CG_IBW, Cockcroft-Gault equation using ideal body weight; IDMS, isotope-dilution mass spectrometry; MDRD, Modification of Diet in Renal Disease; SCr, serum creatinine.

Variables 

To assess the consistency of results among clinically relevant subgroups, comparisons also were performed according to subgroups. Clinical characteristics were categorized as follows: age (<40, 40 to 65, or >65 years), sex, race (African American or other), diabetes (yes or no), prior organ transplant (yes or no), and weight (<60, 60 to 90, or >90 kg). Classification of race, diabetes status, and transplant status were based on the definitions used in each study.

Statistical Analyses 

Data were expressed using standard descriptive statistics (mean ± SD or median and interquartile range, as appropriate). Analyses were computed using Excel (Microsoft Office Excel 2003; Microsoft Corp, Redmond, WA) and SAS software (version 9.1; SAS Institute, Cary, NC).

Percentages of participants assigned to the kidney function categories recommended by the FDA Guidance to Industry (>80, 50 to 80, 30 to 49, or <30 mL/min) were calculated based on measured GFR and kidney function estimates from the 3 equations.¹ Concordance and discordance for assignment of categories between measured GFR and each estimate were calculated, as was concordance and discordance between the MDRD Study equation–derived estimates with the other 2. The significance of differences in concordance for kidney function categories was tested by using McNemar and Jonckheere-Terpstra tests for binary and categorical data with more than 2 categories, respectively.

A simulation study was used to compare drug dosage recommendations for 15 medications based on measured GFR and estimated kidney function. We did not include medications that are dosed primarily according to serum levels or evidence of toxicity. The 15 medications were selected for inclusion in the simulation study because they are used commonly in clinical practice, are cleared by the kidneys, and either have narrow therapeutic windows or are associated commonly with dosing errors or adverse drug events. The recommended doses of each of the 15 medications were determined from the dosing recommendations in the package insert for measured GFR and the 3 estimates (Table 1). The percentage that was in the recommended drug dosing categories for each drug was calculated. The significance of differences in recommended drug dosing was tested by using the sign test.

Table 1. Individual Drug Dosing Recommendations by Kidney Function

Note: Creatinine clearance given in mL/min; unit for conversion to mL/s, ×0.0167.

The institutional review boards of all participating institutions approved inclusion of the data in the pooled data set for these analyses.

Back to Article Outline

Results 

Study Population 

Clinical characteristics of the 5,504 participants included in the study population are listed in Table 2. Mean age of the cohort is 47 ± 15 years. Approximately a third of the cohort was African American, a similar number had diabetes, and 5% were kidney transplant recipients. Mean measured GFR was 75 ± 44 mL/min; eGFR from the MDRD Study equation and estimated creatinine clearances from the CG and CG_IBW equations were 69 ± 38, 75 ± 42, and 62 ± 36 mL/min, respectively. All pairwise comparisons between values for eGFR and measured GFR were significantly different from each other (P < 0.001). Table 2 lists values for measured GFR and estimated kidney function by using the 3 equations across subgroups.

Table 2. Clinical Characteristics

Name	Total	Measured and Estimated Kidney Function(mL/min)
		mGFR	MDRD Study	CG	CGIBW
All	5,504(100)	75±44	69±38	75±42	62±36
Age(y)	47±15	—	—	—	—
<40	2,058(37)	100±46	91±41	102±43	87±37
40-65	2,751(50)	65±36	60±31	64±33	51±25
>65	695(13)	45±26	45±23	42±20	33±15
Sex
Women	2,391(43)	72±42	65±36	74±41	60±34
Men	3,113(57)	77±45	73±39	76±42	65±37
Race
African American	1,740(32)	65±32	62±30	62±30	49±30
White or other	3,764(68)	80±47	73±41	82±45	69±38
Weight(kg)	82±20	—	—	—	—
<60	590(11)	64±40	58±35	58±34	54±33
60-90	3,296(60)	76±44	70±39	74±41	63±36
>90	1,618(29)	78±43	72±37	84±43	64±34
Diabetes
Yes	1,581(29)	100±48	91±44	100±46	86±41
No	3,923(71)	65±37	61±32	65±35	53±28
Transplant
Yes	251(5)	51±27	52±27	59±31	48±24
No	5,253(95)	76±44	70±38	76±42	63±36
Mean body surface area(1.73 m2)	1.93±0.24	—	—	—	—
Mean serum creatinine(mg/dL)	1.65±1.15	—	—	—	—
Mean body mass index(kg/m2)	28±6	—	—	—	—

Note: Values expressed as number (percent) or mean ± SD. Conversion factors for units: GFR from mL/min to mL/s, ×0.0167; serum creatinine in mg/dL to μmol/L, ×88.4.

Abbreviations: CG, Cockcroft-Gault equation using actual body weight; CG_IBW, Cockcroft-Gault equation using ideal body weight; MDRD, Modification of Diet in Renal Disease Study equation; mGFR, measured glomerular filtration rate.

Assignment to FDA Guidance to Industry Kidney Function Category 

Table 3 lists concordance and discordance between each estimating equation and measured GFR with respect to the assigned kidney function categories defined in the FDA Guidance to Industry. The MDRD Study equation showed the highest (78%) and the CG_IBW showed the lowest (66%) concordance with measured GFR (P < 0.001). The direction of discordance was different for the 3 equations. The CG equation assigned a higher kidney function category compared with measured GFR for 16% of people compared with 5% for the CG_IBW equation and 8% for the MDRD Study equation. Conversely, CG_IBW assigned a lower kidney function category in 29% of people compared with 12% for the CG equation and 14% for the MDRD Study equation.

Table 3. Concordance Between Kidney Function Categories Assigned Using mGFR Versus Estimated Kidney Function

Abbreviations: CG, Cockcroft-Gault equation using actual body weight; CG_IBW, Cockcroft-Gault equation using ideal body weight; MDRD, Modification of Diet in Renal Disease Study equation; mGFR, measured glomerular filtration rate.

The MDRD Study equation has a greater rate of concordance with measured GFR for all subgroups tested (Fig 1). Other than for transplant recipients, the CG equation had a greater rate of concordance with measured GFR than the CG_IBW equation. Large differences in concordance rates among equations were observed in many subgroups.

• 
  • View Large Image
  • Download to PowerPoint

• Figure 1. 

  Concordance (Conc) of Modification of Diet in Renal Disease (MDRD) Study, Cockcroft-Gault (CG), and CG adjusted for ideal body weight (CG_IBW) equations with measured glomerular filtration rate (GFR) for assignment of kidney function categories by patient subgroup. Each bar indicates the percentage of concordance to measured GFR for each of the 43 different equations. (A) Age (<40, 40 to 65, or >65 years), (B) sex, (C) race (African American or other), (D) weight (<60, 60 to 90, or >90 kg), (E) presence or absence of diabetes, and (F) presence or absence of kidney transplant. The rate of concordance to measured GFR for the CG and CG_IBW equations was significantly different (P < 0.001) from concordance to measured GFR for the MDRD Study equation for all subgroups except weight of 60 to 90 kg (CG), weight greater than 90 kg (CG_IBW), diabetes (CG), and transplant recipients (CG_IBW).

The CG equation was concordant with the MDRD Study equation in 78% of cases, whereas the CG_IBW had a slightly lower rate of concordance at 75% (P < 0.001). When discordance was observed with the MDRD Study equation, the CG and CG_IBW equations differed in their patterns. Relative to the MDRD Study equation, the CG equation was more likely to predict assignment to a higher kidney function category (16%) than a lower kidney function category (6%). By contrast, the CG_IBW equation was more likely to predict assignment to a lower kidney function category (22%) than a higher kidney function category (3%). Among subgroups, the CG and CG_IBW equations both showed variable rates of concordance with the MDRD Study equation (71% to 86% and 55% to 89%, respectively; Fig 2).

• 
  • View Large Image
  • Download to PowerPoint

• Figure 2. 

  Concordance (Conc) of the Cockcroft-Gault (CG) and CG adjusted for ideal body weight (CG_IBW) equations with the Modification of Diet in Renal Disease (MDRD) Study equation for assignment of kidney function categories by patient subgroups. Each bar indicates the percentage of concordance to the MDRD Study equation for the 2 equations. (A) Age (<40, 40 to 65, or >65 years), (B) sex, (C) race (African American or other), (D) weight (<60, 60 to 90, or >90 kg), (E) presence or absence of diabetes, and (F) presence or absence of kidney transplant. *P < 0.0001 for comparisons of each equation to the MDRD Study equation for each subgroup.

Recommended Drug Dosages 

Compared with measured GFR, the average concordance rate for the specific drug dosing recommendations was 88% for the MDRD Study equation compared with 85% for the CG equation (P < 0.001) and 82% for the CG_IBW equation (P < 0.001; Table 4). Concordance for all equations was lower for drugs that have a greater number of kidney function categories for dose adjustment. As observed with the FDA-assigned kidney function categories, use of the CG equation was most likely to translate into greater recommended drug dosages, whereas the CG_IBW equation was most likely to translate into lower recommended drug dosages.

Table 4. Concordance Between Drug Dosing Recommendations Using Measured Versus Estimated Kidney Function

Drug	MDRD Study			CG			CGIBW
	Concordant	Discordant		Concordant	Discordant		Concordant	Discordant
		< mGFR	> mGFR		< mGFR	> mGFR		< mGFR	> mGFR
2 Dosing levels
Enoxaparin	95	2	3	93	1	5	93	4	3
Eptifibatide	93	4	3	90	4	7	87	11	2
Ranitidine	93	4	3	90	4	7	87	11	2
Average	94	3	3	91	3	6	89	9	2
3 Dosing levels
Acylovir	95	2	3	93	1	6	94	3	3
Atenolol	91	4	6	88	2	10	85	12	3
Cefazolin	92	3	5	90	2	8	90	6	4
Digoxin	91	5	4	88	4	8	85	12	3
Levofloxacin	88	5	7	84	4	12	82	12	6
Tenofovir	86	5	9	82	4	14	79	14	8
Tramadol	93	2	4	92	2	7	92	4	4
Average	91	4	5	88	3	9	87	9	4
4 Dosing levels
Allopurinol	79	14	6	77	11	12	69	27	4
Gabapentin	84	9	8	79	7	14	74	19	6
Sotolol	85	8	7	81	7	12	77	18	5
Average	83	10	7	79	8	13	73	21	5
5 Dosing levels
Disopyramide	75	15	10	72	11	17	65	27	8
Lamivudine	85	7	8	80	5	15	77	16	7
Average	80	11	9	76	8	16	71	22	8
Overall average	88	6	6	85	5	10	82	13	4

Note: Units are mL/min; conversion factor from mL/min to mL/s, ×0.0167.

Abbreviations: CG, Cockcroft-Gault equation using actual body weight; CG_IBW, Cockcroft-Gault equation using ideal body weight; MDRD, Modification of Diet in Renal Disease Study equation; mGFR, measured glomerular filtration rate.

The concordance rate between the MDRD Study and CG equations was 89%, with the MDRD Study equation recommending lower drug dosages in 9% of the study population. The concordance rate between the MDRD Study and CG_IBW equations was 88%, with the MDRD Study equation recommending higher drug dosages in 10% of the study population. Concordance was lower for drugs with a greater number of kidney function categories (ranging from 90% for drugs with 2 dosing levels to 81% with 5 dosing levels).

Back to Article Outline

Discussion 

Accurate estimates of kidney function are essential for optimal dosing of drugs cleared by the kidney. Overestimates of kidney function may lead to administration of inappropriately large doses and possible toxicity, and conversely, underestimates may lead to subtherapeutic dosing, treatment failures, and prolonged illness. In this study, we showed that the MDRD Study equation had the greatest rate of concordance with measured GFR for both assignment of kidney function categories recommended by the FDA Guidance for Industry and adjustment of specific drug dosing. For specific drug dosing, concordance rates among equations were high, with lower concordance for drugs with a greater number of dosing levels.

The CG equation, published in 1976, estimates creatinine clearance and therefore overestimates measured GFR because of creatinine secretion. Even after correcting for this overestimation, substantial imprecision remains.³⁶ Modifications of the CG equation, such as the use of ideal body weight, were developed in an attempt to overcome the imprecision with the use of measured body weight. However, as shown here and previously, this modification results in substantially worse performance compared with measured GFR.³⁷, ³⁸, ³⁹ Use of standardized serum creatinine values leads to another source of error for the CG equation. In previous analyses of these same data, we showed that the CG equation kidney function estimates were 11.4% greater than measured GFR with standardized creatinine compared with 2% greater with nonstandardized values.² Serum samples are not available to enable reexpression of the CG equation for standardized serum creatinine. Altogether, these considerations do not support continued sole reliance on the CG equation for estimating kidney function for drug dosing adjustments.

Our finding of 11% to 29% discordance between the MDRD Study and CG equations overall and in subgroups is consistent with some previous studies that showed discordance rates of approximately 20% to 40% between the equations.²⁰, ²¹, ²², ²³, ²⁴ Possible explanations for the variation in reported discordance rates may be related to true differences in the accuracy of equations among study populations included in the different reports or to variations in the methods used to estimate kidney function (eg, actual versus ideal body weight for the CG equation), units of kidney function (ie, adjustment versus no adjustment for BSA), or the presence or absence of calibration of the creatinine assay. Our results also are consistent with 1 study that compared carboplatin doses determined by means of nuclear imaging of the kidneys to doses calculated using estimates based on the MDRD Study and CG equations and showed that the MDRD Study equation resulted in more accurate dosing.⁴⁰

Strengths of our approach include a large and diverse population; inclusion of measured GFR determined by means of urinary clearance of iodine-125–iothalamate as the gold standard, calibrated serum creatinine in all studies, and standard units for all equations; and inclusion of medications representative of those commonly used in both inpatient and outpatient settings and that have narrow therapeutic windows or are associated commonly with dosing errors or adverse drug events.

There are several limitations. First, results presented here may not be fully applicable to other populations with characteristics that are different from the present study population. For example, in populations with a lower prevalence of chronic kidney disease, we would expect greater concordance rates because drug dosing adjustment is relevant only to patients with kidney disease. As such, our findings of differences among patients of different characteristics may reflect differences in levels of GFR of study participants, rather than patient characteristics per se. However, this data set is more diverse than prior studies, and performance of the equations here may be more representative of their performance when applied in clinical practice than prior studies. Second, we have not considered the contribution of tubular reabsorption or secretion to renal clearance of drugs. However, pharmacokinetic studies do not measure tubular reabsorption or secretion directly, and tubular handling of creatinine is not likely to reflect tubular handling of many drugs that are secreted actively by a number of transporters primarily along the renal proximal tubule.⁴¹ Third, our results are based on simulation rather than pharmacokinetic studies, and we included only a sample of commonly used drugs. Finally, we used drug dosage recommendations as an outcome, rather than observed drug efficacy and safety.

All 3 equations are based on serum creatinine and therefore all have the same irremediable limitations of creatinine as a filtration marker. Serum level of creatinine is determined by factors other than GFR, in particular, tubular secretion, muscle mass, and diet, leading to bias in some populations and imprecision for all.⁴² This is particularly relevant for populations with reduced muscle mass, including the frail elderly, critically ill, or patients with cancer.⁴³ Finally, kidney function must be at a steady state to use any endogenous filtration markers; thus, estimates must be used cautiously in hospitalized patients.

Adjustment of drug dosages is the most common use of kidney function estimates, and these results have implications for prescriptions of both new and existing drugs. The MDRD Study equation is used commonly as a clinical tool for detection and stratification of kidney disease, is widely available to most clinicians, and currently provides the best approximation of measured GFR.¹⁶ Using the same estimate for drug dosing and detection and evaluation of kidney disease likely would facilitate clinical decision making and improve care. The stated intent of the FDA Guidance for Industry is to use measures of kidney function that are “used widely in patient care settings” because such measures are “more practical than other alternatives.”^1(p6) For new drug development, we propose that pharmaceutical manufacturers use the MDRD Study equation for pharmacokinetic studies and in dosing recommendations listed in the product label. For drugs currently in use, it is neither practical nor feasible for pharmacokinetic studies to be repeated using the MDRD Study equation. We propose that either the MDRD Study or CG equation using actual body weight be used for determination of drug dosage. If more accurate equations replace the MDRD Study equation for GFR estimation by clinical laboratories, these equations should be used instead and the FDA Guidance for Industry should incorporate this flexibility in its recommendations.

Currently, no equation provides accurate estimates for all patients. Clinicians must use available estimates together with their best judgment to determine drug dosing for individual patients, particularly for medications with a narrow therapeutic index or high toxicity. For individual patients in whom kidney function estimates from different equations vary substantially, the dose should be determined by weighing the risk of toxicity with a greater dose versus the risk of subtherapeutic dose and treatment failure with a lower dose. If both risks are high, it may be prudent to measure GFR or creatinine clearance before administration of the medication. For such medications, it may be prudent to measure GFR or creatinine clearance in all patients at the extremes of muscle mass in whom all creatinine-based estimates are suspected to be inaccurate. For some drugs, monitoring serum concentrations can minimize errors caused by inaccurate dosage adjustment based on kidney function estimates (eg, aminoglycosides, phenytoin, and lithium). Implementation of computerized clinical decision support systems including automated drug dosing is becoming more common, making such individual assessments of kidney function feasible. Such systems also will easily incorporate more accurate equations as they become used in clinical practice and would facilitate conversion of the MDRD Study equation–derived estimates from units of milliliters per minute per 1.73 m² to units of milliliters per minute, as is recommended for drug dosing.

In conclusion, the MDRD Study equation had greater concordance with measured GFR for recommended drug dosage than the CG equation. Concordance among equations was greater in the context of specific medications. Either the MDRD Study or CG equation could be used for drug dosage adjustments in most circumstances. Patients for whom eGFR from creatinine is likely to be inaccurate require careful consideration. Greater education is needed for physicians, pharmacists, industry, and the public about chronic kidney disease and the interpretation of GFR estimates for use in drug dosing.

Back to Article Outline

Acknowledgements 

A list of the Investigators of CKD-EPI Aims 1 and 2, organized by institution, follows. Tufts Medical Center, Boston, MA: Andrew S. Levey, MD; Lesley A. Stevens, MD, MS; Christopher H. Schmid, PhD; and Yaping (Lucy) Zhang, MS. Cleveland Clinic Foundation, Cleveland, OH: Frederick Van Lente, PhD; and Liang Li, PhD. University of Utah, Salt Lake City, UT: Tom Greene, PhD. Johns Hopkins University, Baltimore, MD: Josef Coresh, MD, PhD, MHS; Jane Manzi, PhD; Brad Astor, PhD, MPH; and Elizabeth Selvin, PhD, MPH. University of Pennsylvania, Philadelphia, PA: Harold I. Feldman, MD, MSCE; J. Richard Landis, PhD; and Marshall Joffe, MD, MPH, PhD. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK): John W. Kusek, PhD; and Paul W. Eggers, PhD.

A list of the Collaborators of CKD-EPI Aim 1, organized by study, follows. African American Study of Kidney Disease and Hypertension (AASK): Gabriel Contreras, MD, MPH; and Julia B. Lewis, MD. Captopril in Diabetic Nephropathy Study (CSG): Roger A. Rodby, MD; and Richard D. Rohde, BS. Chronic Renal Insufficiency Cohort (CRIC): Harold I. Feldman, MD, MSCE; Lawrence J. Appel, MD, MPH; Jing Chen, MD, MS; Alan S. Go, MD; Lee Hamm, MD; Chi-yuan Hsu, MD; James P. Lash, MD; Akinlolu O. Ojo, MD; Mahboob Rahman, MD; Raymond R. Townsend, MD; Matthew R. Weir, MD; and Jackson T. Wright, MD. Cleveland Clinic Foundation (CCF): Phillip Hall, MD; and Emilio Poggio, MD. Diabetes Control and Complications Trial (DCCT): Saul Genuth, MD; and Michael W. Steffes, MD, PhD. Diabetic Renal Disease Study Group (DRDS): Robert G. Nelson, MD, PhD. Mayo Clinic: Andrew D. Rule, MD, MS; Timothy Larson, MD; and Fernando Cosio, MD. MDRD Study: Gerald Beck, PhD.

Support: CKD-EPI is funded by grants from the NIDDK as part of a cooperative agreement in which the NIDDK has substantial involvement in the design of the study and the collection, analysis, and interpretation of the data. The NIDDK was not required to approve publication of the finished manuscript. Dr Stevens receives research support from the NIDDK, the Paul Teschan Fund, Gilead, and the National Kidney Foundation (NKF). Dr Feldman receives research support from the NIDDK and Amgen. Dr Lewis receives research support from Hoffmann-LaRoche, Theravance Inc, Sanofi, and Bristol-Myers Squibb. Dr Townsend receives research support from the NIDDK. Dr Schmid receives research support from the NIDDK, Agency for Healthcare Research and Quality, the National Center for Research Resources, the National Heart, Lung and Blood Institute, the Centers for Disease Control and Prevention, and Pfizer. Dr Levey receives research support from the NIDDK, Amgen, and the NKF.

Financial Disclosure: None.

Back to Article Outline

References 

1. Food and Drug Administration. Guidance for Industry: Pharmacokinetics in Patients With Impaired Renal Function—Study Design, Data Analysis, and Impact on Dosing and Labeling. Rockville, MD: US Department of Health and Human Services; 1998;May
   • View In Article
2. Stevens LA, Manzi J, Levey AS, et al. Impact of creatinine calibration on performance of GFR estimating equations in a pooled individual patient database. Am J Kidney Dis. 2007;50:21–35
   • View In Article
   • Abstract
   • Full Text
   • Full-Text PDF (353 KB)
   • CrossRef
3. Coresh J, Stevens LA. Kidney function estimating equations: Where do we stand?. Curr Opin Nephrol Hypertens. 2006;15:276–284
   • View In Article
   • MEDLINE
4. Lewis JB, Agodoa L, Cheek D, et al. Comparison of cross-sectional renal function measurements in African Americans with hypertensive nephrosclerosis and of primary formulas to estimate glomerular filtration rate. Am J Kidney Dis. 2001;38:744–753
   • View In Article
   • Abstract
   • Full Text
   • Full-Text PDF (105 KB)
   • CrossRef
5. Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the Modification of Diet in Renal Disease and Cockcroft-Gault equations for estimating renal function. J Am Soc Nephrol. 2005;16:763–773
   • View In Article
   • MEDLINE
   • CrossRef
6. Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: Accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004;141:929–937
   • View In Article
7. Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol. 2005;16:459–466
   • View In Article
   • MEDLINE
   • CrossRef
8. Cirillo M, Anastasio P, De Santo N. Relationship of gender, age, and body mass index to errors in predicted kidney function. Nephrol Dial Transplant. 2005;20:1791–1798
   • View In Article
   • MEDLINE
   • CrossRef
9. Verhave JC, Fesler P, Ribstein J, du Cailar G, Mimran A. Estimation of renal function in subjects with normal serum creatinine levels: Influence of age and body mass index. Am J Kidney Dis. 2005;46:233–241
   • View In Article
   • Abstract
   • Full Text
   • Full-Text PDF (178 KB)
   • CrossRef
10. Levey AS, Coresh J, Greene T, et al. Expressing the MDRD Study equation for estimating GFR with standardized serum creatinine values. Clin Chem. 2007;53:766–772
    • View In Article
    • MEDLINE
    • CrossRef
11. Levey AS, Coresh J, Greene T, et al. Using standardized serum creatinine values in the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–254
    • View In Article
12. National Kidney Disease Education Program. Laboratory Professionals. http://nkdep.nih.gov/labprofessionals/index.htmAccessed December 19, 2008
    • View In Article
13. Myers GL, Miller WG, Coresh J, et al. Recommendations for improving serum creatinine measurement: A report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem. 2006;52:5–18
    • View In Article
    • MEDLINE
    • CrossRef
14. National Kidney Foundation. K/DOQI Clinical Practice Guidelines for Chronic Kidney Disease: Evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(suppl 1):S1–S266
    • View In Article
    • Full Text
    • Full-Text PDF (16 KB)
    • CrossRef
15. Australian and New Zealand Society of Nephrology (ANZSN). Kidney Check Australia Taskforce. www.nephrology.edu.auAccessed December 19, 2008
    • View In Article
16. Miller WG. Reporting estimated GFR: A laboratory perspective. Am J Kidney Dis. 2008;52:645–648
    • View In Article
    • Full Text
    • Full-Text PDF (72 KB)
    • CrossRef
17. La Caisse nationale d'assurance maladie des professions indépendantes: Avenant à la convention nationale des directeurs de laboratoire privé d'analyses médicales. http://www.admi.net/jo/20030227/SANS0320604X.htmlAccessed December 22, 2008
    • View In Article
18. Mathew TH. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: A position statement. Med J Aust. 2005;183:138–141
    • View In Article
    • MEDLINE
19. Siegel NJ. Renal Express. http://www.asn-online.org/newsletter/renal_express/2003/03-10_Rxpress.aspxAccessed October 3, 2003
    • View In Article
20. Rosborough TK, Shepherd MF, Couch PL. Selecting an equation to estimate glomerular filtration rate for use in renal dosage adjustment of drugs in electronic patient record systems. Pharmacotherapy. 2005;25:823–830
    • View In Article
    • MEDLINE
    • CrossRef
21. Wargo KA, Eiland EH, Hamm W, English TM, Phillippe HM. Comparison of the Modification of Diet in Renal Disease and Cockcroft-Gault equations for antimicrobial dosage adjustments. Ann Pharmacother. 2006;40:1248–1253
    • View In Article
    • MEDLINE
    • CrossRef
22. Melloni C, Peterson ED, Chen AY, et al. Cockcroft-Gault versus Modification of Diet in Renal Disease: Importance of glomerular filtration rate formula for classification of chronic kidney disease in patients with non-ST-segment elevation acute coronary syndromes. J Am Coll Cardiol. 2008;51:991–996
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (205 KB)
    • CrossRef
23. Gill J, Malyuk R, Djurdjev O, Levin A. Use of GFR equations to adjust drug doses in an elderly multi-ethnic group—A cautionary tale. Nephrol Dial Transplant. 2007;22:2894–2899
    • View In Article
    • CrossRef
24. Golik M, Lawrence K. Comparison of dosing recommendations for antimicrobial drugs based on two methods for assessing kidney function: Cockcroft-Gault and Modification of Diet in Renal Disease. Pharmacotherapy. 2008;28:1125–1132
    • View In Article
    • CrossRef
25. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16:31–41
    • View In Article
    • MEDLINE
    • CrossRef
26. Spinler SA, Awarskas JJ, Boyce EG, et al. Predictive performance of ten equations for estimating creatinine clearance in cardiac patients. Ann Pharmacother. 1998;32:1275–1283
    • View In Article
    • MEDLINE
    • CrossRef
27. Mosteller RD. Simplified calculation of body-surface area. N Engl J Med. 1987;317:1098
    • View In Article
    • MEDLINE
    • CrossRef
28. Thomson Healthcare Staff. Physicians' Desk Reference. (ed 62). Montvale, NJ: Physicians' Desk Reference Inc; 2008;
    • View In Article
29. AstraZeneca Pharmaceuticals. Product Information: Tenormin^®, atenolol. Wilmington, DE: AstraZeneca Pharmaceuticals; 2005;
    • View In Article
30. SmithKline Beecham. Product Information: Ancef^®, cefazolin. Philadelphia, PA: SmithKline Beecham; 2004;
    • View In Article
31. Aronoff GR, Berns JS, Brier ME, et al. Drug Prescribing in Renal Failure (Dosing Guidelines for Adults). (ed 4). Philadelphia, PA: American College of Physicians; 1999;
    • View In Article
32. Ortho-McNeil Pharmaceutical Inc. Product Information: Ultram^®, tramadol hydrochloride. Raritan, NJ: Ortho-McNeil Pharmaceutical Inc; 2007;
    • View In Article
33. Pfizer Inc. Product Information: Neurontin^®, gabapentin. New York, NY: Pfizer Inc; 2007;
    • View In Article
34. Berlex Laboratories. Product Information: Betapace^®, sotalol HCl. Wayne, NJ: Berlex Laboratories; 2001;
    • View In Article
35. Searle Pharmaceuticals. Product Information: Norpace^®, disopyramide. Chicago, IL: Searle Pharmaceuticals; 1995;
    • View In Article
36. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation (Modification of Diet in Renal Disease Study Group). Ann Intern Med. 1999;130:461–470
    • View In Article
    • MEDLINE
37. Gonwa TA, Jennings L, Mai ML, Stark PC, Levey AS, Klintmalm GB. Estimation of glomerular filtration rates before and after orthotopic liver transplantation: Evaluation of current equations. Liver Transpl. 2004;10:301–309
    • View In Article
    • MEDLINE
    • CrossRef
38. O'Meara E, Chong KS, Gardner RS, Jardine AG, Neilly JB, McDonagh TA. The Modification of Diet in Renal Disease (MDRD) equations provide valid estimations of glomerular filtration rates in patients with advanced heart failure. Eur J Heart Fail. 2006;8:63–67
    • View In Article
    • MEDLINE
    • CrossRef
39. Rigalleau V, Lasseur C, Perlemoine C, et al. Cockcroft-Gault formula is biased by body weight in diabetic patients with renal impairment. Metabolism. 2006;55:108–112
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (636 KB)
    • CrossRef
40. de Lemos ML, Hsieh T, Hamata L, et al. Evaluation of predictive formulae for glomerular filtration rate for carboplatin dosing in gynecological malignancies. Gynecol Oncol. 2006;103:1063–1069
    • View In Article
    • MEDLINE
    • CrossRef
41. Launay-Vacher V, Izzedine H, Karie SHJ, Baumelou A, Deray G. Renal tubular drug transporters. Nephron Physiol. 2006;103:97–106
    • View In Article
42. Stevens LA, Coresh J, Feldman HI, et al. Evaluation of the Modification of Diet in Renal Disease Study equation in a large diverse population. J Am Soc Nephrol. 2007;18:2749–2757
    • View In Article
    • CrossRef
43. Poggio ED, Nef PC, Wang X, et al. Performance of the Cockcroft-Gault and Modification of Diet in Renal Disease equations in estimating GFR in ill hospitalized patients. Am J Kidney Dis. 2005;46:242–252
    • View In Article
    • Abstract
    • Full Text
    • Full-Text PDF (205 KB)
    • CrossRef