The Old And New Methods Of Kidney Disease

Contacty: emily.li@wecistanche.com

Jessica L. Steffl, PharmD, William Bennett, MD, and Ali J. Olyaei, PharmD

Chronic kidney disease is a worldwide problem. Accurate assessment of kidney function is important for defining stages of kidney disease and assisting with drug dosing. Glomerular filtration rate (GFR) is a good index of the health of the kidney. Although measured GFR using an exogenous substance is the most accurate, it is difficult to obtain due to cost and resources. Equations calculating creatinine clearance and estimated GFR as a measure of kidney function have been developed using serum creatinine as a marker of kidney function. The Cockcroft-Gault, Modification of Diet in Renal Disease, and Chronic Kidney Disease Epidemiology Collaboration equations have been shown to have statistically significant differences in estimating GFR in various populations. Drug-dosing adjustments based on the various equations may differ. However, without clinical outcome data, it is yet to be determined whether these differences are clinically significant.

Keywords Chronic kidney disease; pharmacokinetic; drugs; dosage

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Chronic kidney disease (CKD) is a worldwide health problem. In the United States alone, approximately 26 million people are affected. It is estimated that up to 13% of the US population has impaired kidney function, many of whom are undiagnosed.1 In 2007, more than 35 billion dollars were spent on the US Medicare end­stage renal disease (ESRD) program. A disproportionate amount of health care dollars is spent on those with ESRD. The prevalence of CKD and its consequences have led to an increase in awareness and importance placed on early diagnosis to prevent progression to ESRD.²

Numerous risk factors can lead to the development of CKD, such as hypertension, diabetes mellitus, smoking, hyperlipidemia, and obesity.3,4 Patients at high risk for developing CKD can be identified early, and potential modifications in therapy can prevent worsening of kidney function and progression toward disease. Kidney disease progression is associated with significant morbidity and mortality.5­7 Patients with kidney disease often suffer from anemia, malnutrition, bone disease, neuropathy, calcium and phosphorous imbalances, and cardiovascular disease. Management of diseases associated with CKD may improve outcomes. For example, better control of hypertension in CKD has led to a 15% to 40% decrease in stroke, myocardial infarction, and heart failure. However, cardiovascular disease remains the leading cause of death in ESRD. Stratifying patients in high­risk categories may provide benefits for long­term outcomes.⁸

The National Kidney Foundation defines CKD as the presence of kidney damage for greater than or equal to 3 months, defined by structural or functional abnormalities, irrespective of decreased glomerular filtration rate (GFR),⁹ or alternately, a GFR less than or equal to 60 mL/min/1.73m2 for greater than or equal to 3 months with or without kidney damage. Once CKD has been diagnosed, further staging is reliant on kidney function as defined by GFR. Accurate estimation of GFR can be essential for the evaluation and treatment of CKD patients. Individuals are categorized into 5 stages of kidney disease (Table I). As the stage increases, it is associated with lower GFR and worsening kidney function. Generally, CKD can be a silent disease. Signs and symptoms of kidney disease often do not present until stage 3. Diagnosing individuals in earlier stages of CKD can identify patients in whom progression toward ESRD can be slowed or halted. Although a continuous scale, the stages are used to assist in the classification of individuals with CKD and determination of treatment modalities such as the potential need for hemodialysis or transplantation.¹⁰

The GFR is a good index of the health of the kidney. The GFR correlates well with anatomical, functional, and pathological features of kidney function. Therefore, it is important to estimate GFR accurately with a high level of precision and bias. The gold standard for assessment of kidney function is a measured GFR using an exogenous substance for a filtration marker (inulin, iohexol, and I125 iothalamate). Inulin provides a marker for measured GFR as it is filtered by the glomerulus and is not secreted, reabsorbed, or metabolized by the kidney.¹¹ However, measured GFR using an exogenous substance is costly and not available for routine monitoring. Serum creatinine has been used as the primary method for determining estimated GFR. Creatinine is an endogenous substance filtered through the glomerulus and undergoes an additional about 10% to 20% tubular secretion. For patients with normal kidney function, creatinine clearance overestimates GFR by less than 10% to 30%. However, in patients with advanced kidney disease, the extent of overestimation may increase as much as 30%. The National Kidney Foundation recommends estimating GFR using equations developed using serum creatinine. The development of equations for predicting and estimating GFR has been essential for identifying individuals with kidney disease. Most clinical pharmacokinetic studies, dosage adjustments, and recommendations are driven by using creatinine clearance. In these studies, small numbers of patients with reduced kidney function, with or without the disease, were enrolled. The information resulting from these studies cannot be directly extrapolated to dosage adjustment using estimated GFR. The correlation between estimated GFR and CrCl for drug dosing has not been validated for most drugs in patients with CKD.

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It is important to accurately estimate kidney function to improve drug dosing in CKD. Inappropriate drug dosing may adversely affect patient care. Patients with CKD are at risk for overexposure, undertreatment, and many drug and disease interactions. There are multiple variables that affect drug pharmacokinetics and must be taken into consideration when drugs are adjusted for CKD. Pharmacokinetics is the mathematical study of drug action in the body.¹² CKD affects every aspect of drug pharmacokinetics (Table II). There is very limited research on different aspects of pharmacokinetics among those with CKD. Acute or chronic kidney insufficiency significantly alters the pharmacokinetic and pharmacodynamic properties of most commonly used drugs. Kidneys play an important role in the excretion of active drugs and their pharmacologically active metabolites. However, most studies emphasize only the kidney elimination portion of pharmacokinetics without much interest in drug absorption, protein binding, volume distribution, or drug metabolism.¹³

Several equations have been created for the classification and assessment of kidney function. The equations assist in predicting estimated GFR by using both serum creatinine and other individual characteristics. Adequate assessment of kidney function plays an important role in drug dosing and treatment of concomitant disease states. Inappropriate dosing of medications based on kidney function may have detrimental effects on patient outcomes.

SERUM CREATININE

Creatinine is a product of the metabolism of muscle.¹⁴ Therefore, the excretion of creatinine remains fairly constant in the absence of other factors such as acute kidney injury, changes in diet, and the presence of medications affecting the excretion of creatinine. For the measurement of GFR, serum creatinine is the primary method for the assessment of kidney function. The use of serum creatinine-based estimated GFR equations is recommended by the National Kidney Foundation. As above, creatinine is filtered through the glomerulus and on average undergoes only minimal tubular secretion. This allows serum creatinine to be the most accurate and readily available biomarker for the estimation of GFR when the individual has stable kidney function. The National Health and Nutrition Examination Survey found that serum creatinine increased with age regardless of gender or race.¹⁵

The measurement of serum creatinine is the most practical laboratory value for estimating GFR; however, creatinine lacks characteristics of the ideal marker of kidney function. Because creatinine is derived from muscle breakdown of creatine, it can be largely affected by diet, age, gender, and race differences in body mass.¹⁶ It has been shown that the average serum creatinine-based estimates among Mexican Americans are lower and in non­Hispanic blacks higher compared with the general US population.¹⁷ In addition, age may affect serum creatinine values due to the presence of lower body mass and possible malnutrition in the elderly. The decrease in muscle mass in the elderly and malnourished patients has a profound effect on serum creatinine. Values will likely be falsely low compared with actual glomerular filtration. The effect of diet on serum creatinine values may also create misinterpretation of GFR. For example, individuals who eat a primarily vegetarian diet may have lower serum creatinine values. These extrinsic effects on the measurement of serum creatinine can alter the assessment of kidney function. Cimetidine and trimethoprim inhibit the tubular secretion of creatinine and may cause transient increases in serum creatinine without affecting the actual GFR.¹⁸ The increase in serum creatinine will result in a false estimated GFR. Finally, certain medications such as cimetidine and trimethoprim lead to inaccurate estimated GFR based on increased serum creatinine.

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The serum creatinine value may also not reflect when acute kidney injury is present or resolving. When an acute insult to the kidney occurs, serum creatinine will not adequately reflect the change in the GFR, as it has not had time to accumulate in the body. Alternately, when the resolution of a kidney injury occurs, the serum creatinine values lag behind the improvement in kidney function. It is important in acute kidney injury to consider the time course and trends in the serum creatinine value rather than a single laboratory measurement. The trend of serum creatinine combined with the measurement of urine output may be more useful in acute situations.

In individuals with stable kidney function, estimating GFR using serum creatinine and creatinine clearance remains the most practical laboratory assessment. However, the use of various equations estimating creatinine clearance or GFR assists in the diagnosis and guiding treatment of each patient. Since all the equations for estimating kidney function use serum creatinine, all these equations are subject to a lack of complete accuracy. It is vital to emphasize that all equations only estimate kidney function and do not measure GFR in individual patients.

COCKCROFT-GAULT

The Cockcroft­Gault (CG) equation was developed in 1976 as a measurement of creatinine clearance. Since its publication and until recently, the CG equation was the primary method for assessment of kidney function outside of a 24 ­hour urine collection or measured GFR.¹⁹ The equation was derived from a study including 249 patients, all males with good kidney function. A 24­ hour creatinine clearance was assessed compared to the derived equation. The equation includes patient weight as a measure of body mass, age, and serum creatinine.

Although developed from a homogenous population, the CG equation has been validated in many populations. Previously, the Food and Drug Administration released Guidance for industry: Pharmacokinetics in Patients with impaired Renal Function—Study Design, Data Analysis, and Impact on Dosing and Labeling in 1998, recommending the CG equation as one method for assessing kidney function in pharmacokinetic studies. However, a more recent update in 2010 recommends the use of either the CG equation or the Modification of Diet in Renal Disease (MDRD) study equation.²⁰

MDRD EQUATION

The MDRD equation was derived from the 1628 patients involved in the MDRD study group. Study participants underwent GFR estimation using 24­hour creatinine clearance urine collection and a single measurement of serum creatinine.²¹ Multiple variables (ie, weight, height, sex, ethnicity, diabetes, etc) were assessed to determine the most accurate assessment of GFR. The final equations included patient characteristics of age, serum creatinine, blood urea nitrogen, albumin, race, and gender:

GFR = 170 × [Pcr]^-0.999 × [Age]^-0.176
× [0.762 if patient is female]
× [1.180 if patient is black]

× [SUN]^-0.170 × [Alb]^0.318,

where Pcr = serum creatinine concentration, SUN =serum urea nitrogen concentration, and Alb = serum albumin concentration. The MDRD6 equation was further abbreviated to a 4­variable equation including age, sex, ethnicity, and serum creatinine.

GFR = 186 × Scr^-1.154 × age^-0.203
× 0.742 [if female]

× 1.21 [if black].

The National Kidney Disease Education Program (NKDEP) initiated a program for the standardization of serum creatinine assays.²² The original MRDR equation was created based on serum creatinine measurements using the kinetic alkaline picrate assay. The NKDEP recommended standardizing serum creatinine assays against the isotope dilution mass spectrometry. Once the conversion was made, the 4-­variable MDRD was reexpressed as²³

GFR = 175 × Scr^-1.154 × age^-0.203
× 1.212 [if black]

× 0.742 [if female].

A study further analyzed the MDRD study group and compared the 6­variable MDRD and 4­variable MDRD study equations with recalibrated serum creatinine values. The percentages of estimated GFR within 30% of the measured GFR were 90% and 91% for the 4­-variable and 6­variable equations, respectively. The 4­-variable equation was shown to be similar to the 6­-variable equation in the MDRD study group. Both the 4-­variable and 6-­variable MDRD equations had similar rates for detecting GFR <60 mL/min/1.73 m2, 96%, and 97%, respectively. The accuracy of the 4­-variable equation compared with the 6-­variable equation allows for a simpler calculation to determine the estimated GFR. In addition, the 4-­variable equation may be less prone to errors based on fewer laboratory measurements included within the equation.²⁴

The 4­variable MDRD equation has been adopted by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF­KDOQI) as the primary equation for determining the estimated GFR. The use of the 4­variable MDRD is now the primary method for CKD staging according to the KDOQI guidelines. Although the MDRD has been proven accurate for individuals with CKD, a study assessing the performance of the 4­variable MDRD equation showed significant underestimation of the GFR in individuals with GFR >90 mL/min/17.3 m2. The percentage of estimated GFR within 30% of the measured GFR in the CKD series (individuals being evaluated for known or suspected kidney disease) and healthy series (individuals being evaluated for potential kidney donation) were 75% and 54%, respectively. The MDRD equation may underestimate GFR in healthy individuals.²⁵

CHRONIC KIDNEY DISEASE EPIDEMIOLOGY COLLABORATION EQUATION

The Chronic Kidney Disease Epidemiology Collaboration (CKD­EPI) equation was developed in 2009 using data from 10 studies (8254 people) and validated using data from 16 studies (3896 people).²⁶ The goal for the CKD­EPI equation was to create an equation more accurate than the MDRD, particularly in patients with higher GFR. The equation used the variables of standardized serum creatinine, age, sex, and race. From the data, the equation created is:

eGFR = 141 × min(Scr/k, 1)^a
× max(Scr/k, 1)^-1.209 × 0.993^Age

× 1.018 [if female] × 1.159 [if black],

where Scr is serum creatinine, k is 0.7 for females and 0.9 for males, a is -0.329 for females and -0.411 for males, min indicates the minimum of Scr/k or 1, and max indicates the maximum of Scr/k or 1.

The equation was created in comparison to measured GFR using iothalamate as the exogenous filtration marker. When validated, the study populations measured GFR using iothalamate and other markers. In addition, CKD­EPI compared the new equation to the standard 4­variable MDRD equation. The CKD-EPI equation was shown to have less bias in individuals with GFR >60 mL/min/1.73 m2 when compared with MDRD (3.5 vs 10.6 mL/min/1.73m2). Overall, the CKD­EPI equation estimated GFR to be within 30% of measured GFR in 84% of all individuals included in the study.

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COMPARISON OF CG, MDRD, AND CKD-EPI EQUATIONS

The development of GFR­estimating equations has been crucial for the identification of patients with CKD or at risk for the development of disease. The creation of the CG equation took a large step forward in assessing kidney function by incorporating patient characteristics in addition to serum creatinine. It has been further improved with the MDRD and CKD­EPI equations, which included the more specific variable of race in addition to age, gender, and serum creatinine. Although accuracy in GFR assessment has improved, one equation has yet to be developed to be the most accurate in all populations.

A study was conducted comparing CG, MDRD, and CKD­EPI equations and measured GFR using the I­iothalamate filtration marker. Mean bias was defined as the mean difference between estimated GFR and measured GFR and accurately represented the percentage of estimated GFR within 30% of the measured GFR. Overall, the mean bias was smallest with MDRD compared with CKD­EPI and CG (0.8 vs 4.5 and 9.9 mL/min/1.73m2).²⁷ Accuracy was highest with the CKD­EPI equation at 84.5% versus 81.2% and 74.2% for MDRD and CG but not statistically significant from MDRD. In patients with GFR ≥90

mL/min/1.73 m2, accuracy with the CKD­EPI equation was higher but not, however, statistically significant compared with both the MDRD and CG equations. Overall, MDRD and CKD­EPI did not differ in most populations aside from patients with higher GFR (see Figure 1). In the elderly and those with low weight, the bias between equations was not significantly different. In these populations, the CG equation may still be a viable option for estimating kidney function.

Further comparisons have been made between MDRD and CKD­EPI in individuals with higher GFR. Prior to the development of the CKD­EPI equation, a study was conducted comparing MDRD equations in CKD and healthy individuals. In the healthy series, the mean measured GFR was 101 ml/min/1.73 m2 compared to 72 ml/min/1.73 m2 estimated by the MDRD equation. Because of the known underestimation of the MDRD equation, laboratories report GFR as >60 ml/min/1.73 m2 for individuals with good kidney function. In the original CKD­EPI study, the equation was compared to MDRD. The CKD­EPI equation was as accurate as the MDRD study equation. Overall, estimated GFR with the MDRD study equation and CKD­EPI correlated in CKD staging with measured GFR 64% and 69%, respectively. At GFR values of 30 to 59 mL/min/1.73 m2 and higher, the CKD­EPI reclassified patients to higher values compared with MDRD (P < .001). For individuals classified to different stages of CKD, as defined by KDOQI guidelines, by CKD­EPI or MDRD study equations, the CKD­EPI equation was correct more often than the MDRD study (63% vs 34% P < .001). According to this study, calculation of estimated GFR using the CKD­EPI equation would lead to a 1.6% lower estimate of the prevalence of CKD in the United States.²²

The Atherosclerosis Risk in Communities Study compared the risk implications of using the MDRD study equation and the new CKD­EPI equation. The study followed 13 905 individuals without cardiovascular disease for a median follow­up of 16.9 years.²⁸ Reclassification to a higher estimated GFR category occurred when using the CKD­EPI equation compared with the MDRD study equation. In the ranges of 60 to 89 mL/min/1.73 m2 and 30 to 59 mL/min/1.73 m2, 44.9% and 43.5%, respectively, were reclassified to a higher category. The study measured incident risk ratios for each group for the following outcomes: ESRD, all­cause mortality, coronary heart disease, and stroke. Participants reclassified to a higher GFR category according to CKD­EPI equation had a lower risk for all outcomes compared with those who remained in the lower category based on both equations. Overall, the CKD­EPI equation was shown to have a significantly positive net reclassification improvement. Similar findings were reported in the Australian Diabetes, Obesity and Lifestyle Study.²⁹ Individuals reclassified to higher CKD groups, both male and female, had originally been classified as stage 3a CKD (45­59 mL/min/1.73m2). The Australian prevalence of CKD is estimated at 13.4% using the MDRD study equation compared with 11.5% using the CKD­EPI equation. The median 10­year cardiovascular disease risk was significantly higher in individuals ≥65 years with stage 3a CKD and ≥65 years without CKD compared with the reclassified group (P < .001). In addition, adjusted hazard ratios for all­cause mortality were 1.01 for the reclassified group, 1.26 for stage 2, and 2.3 for stage 3b (30­-44 mL/min/1.73 m2) compared to those with no CKD. Statistical significance was reached in the stage 3b group only. The decrease in prevalence could have a significant impact on clinical practice.

DRUG DOSING

The CKD­EPI, MDRD, and CG equations have all been validated as estimations of kidney function. Pharmacokinetic studies completed prior to the standardization of serum creatinine assays and the use of newer equations may lead to discrepancies in dosing. It is uncertain which provides the most accurate medication dosing recommendations. It is essential for medication dosing to be assessed according to measurements of kidney function. Overestimations of kidney function may lead to overdosing on medications. The potential risk for accumulation and thus increased risk for side effects and toxicity could be detrimental to patient care. Alternately, underestimations of kidney function can lead to underdosing of medications and inadequate treatment. Currently, the Food and Drug Administration (FDA) suggests that all pharmacokinetic studies be assessed using the CG equation. It has also been suggested in an update that the MDRD study equation be used as a measure of kidney function.20 With the development of more accurate estimations of GFR with the MDRD and CKD­EPI study equations, it may be necessary to change practice in the industry and pharmacokinetic studies. Meanwhile, in the clinical setting, it is important to use clinical judgment and treat the patient and not focus on calculated numbers. There are no outcome studies to show if any of the equations are linked to a better efficacy or lower incidence of adverse drug reactions when used to dose medications. In particular, in patients with a GFR less than 60 mL/min/1.73m2, using CG, MDRD, or CKD­EPI equations results in a similar GFR with nonsignificant differences in dosing discordance.

A study conducted using data for 5504 participants assessed the 4­variable MDRD equation compared with the CG and CG using ideal body weight (CGIBW). In this study, compared to the measured GFR using I­iothalamate, the MDRD study equation showed the largest concordance at 78% compared with CG_IBW at 66%. Assessment of drug­dosing recommendations based on each of the equations was conducted with 15 common renally adjusted medications. The highest concordance of drug dosing compared with measured GFR was found using the MDRD study equation. The concordance rate for the MDRD study equation was statistically significant compared with the CG and CGIBW (88% compared with 85% and 82%, respectively, P > .001).³⁰ For all equations, concordance was lower for medications with a larger number of renal dosing categories.

A comparison of MDRD and CG equations in the dosing of several antimicrobials was assessed in a tertiary medical center using 207 patients without normal kidney function.³¹ The study compared the 4­variable MDRD study equation with several versions of CG equations, adjusted for standardized serum creatinine and body weight. Daily doses were calculated for 4 antimicrobials: cefepime, levofloxacin, meropenem, and piperacillin­tazobactam. Dosage adjustments were discordant between the MDRD equation (unadjusted for body surface area) and CG (using IBW and standardized serum creatinine) between 22.8% and 36.3% depending on the type of antimicrobial prescribed. In most cases, the MDRD study equation resulted in higher dosing recommendations when compared with the CG equation. In another study, doses of antimicrobial medications were assessed by comparing the CG equation and MDRD study equation in both acute kidney injury and CKD. Of the 325 patients receiving antimicrobials requiring renal adjustment, 116 had differing dosage recommendations when using the MDRD equation versus the CG equation. Of the 116 patients, 115 (99.1%) had higher doses recommended by the MDRD study equation. Renal dosage adjustments had a higher concordance rate in patients with acute kidney injury compared with recommendations in the CKD group (78.7% vs 60%, P = .003).³²

In addition, a study was conducted in 409 patients with CKD to determine differences in antimicrobial dosing between kidney function estimation equations.33,34 Originally, the study was conducted to assess the difference in dosing recommendations between the CG and MDRD equations. The MDRD study equation was normalized to body surface area to present the results in milliliters/minute. The mean difference in kidney function assessment between the 2 equations was 5.40 mL/min (P < .001). Antimicrobial dosing discordance rates were calculated for cefazolin, cefepime, daptomycin, gatifloxacin, levofloxacin, meropenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole. It was determined that overall discordance rates were 20% to 36% (P < .001). Most often, the MDRD equation would recommend no dose adjustment, when according to the CG equation, a dose adjustment would be necessary according to the manufacturer’s recommendations. In a follow­up analysis, Wargo et al^33,34 compared antibiotic dosing recommendations using the new CKD­EPI equation compared with the CG equation in the same population. The mean difference in kidney function for the 2 equations was 5.10 mL/min (P < .001). Overall discordance between the CKD­EPI and CG equations for antimicrobial dosing was 15% to 25%. Similar to the previous study, discordance occurred most frequently (88%­96%) based on the CKD­EPI equation recommending no adjustment, when the manufacturer would recommend adjustment according to the CG equation. Comparing the CKD­EPI equation with the MDRD equation, a discordance rate of only 7% to 12% was reported.

Although comparisons in dosing recommendations have been made between kidney function estimating equations, limited pharmacokinetic comparisons exist. With FDA recommendations to continue pharmacokinetic assessment using renal function assessment with the CG equation or MDRD study equation, it is important to consider potential adverse outcomes of changes in dosing based on alternate equations.

ELDERLY

Kidney function has been shown to decline on average by 1 mL/min/1.73 m2 in individuals older than 40 years.³⁵ With a decrease in muscle mass and changes in nutritional status, the elderly patient may present with serum creatinine values within the reference range but have decreased GFR. The recent development of new equations for the estimation of GFR has raised the question as to which equation may be most appropriate for assessing kidney function in elderly patients.

A small study was conducted on 52 individuals aged 68 years and older. Assessment of kidney function–estimating equations were compared with 51chromium ethylenediaminetetraacetic acid (51Cr EDTA) as the reference value.³⁶ The CG equation gave the lowest imprecision estimates compared with the MDRD study equation, 24­hour creatinine clearance, and Baracskay and Jelliffe formulas. Total misclassification errors in the >50 mL/min/1.73 m2 group were the lowest with the CG equation followed by 24­hour creatinine clearance and the MDRD study equation. Using regression analysis, the CG equation and MDRD study equation had the best fit (r2 of 0.84 for both equations). In a larger study of 9931 long­term care patients 65 years and older, a discrepancy was shown between CG and MDRD equations.³⁷ Low GFR (<30 mL/min/1.73 m2 for MDRD and <30 mL/min for CG) in the male population was significantly higher when using the CG equation compared with MDRD (10.3% vs 3.5%). In women, a similar discrepancy between calculated low GFR was reported (23.3% vs 4.0%). Rates of low GFR in this population would be reported much higher based on the CG equation compared with the MDRD study equation.

An age­ related subgroup analysis of a larger study comparing the new CKD­EPI, MDRD, and CG equations showed an age-influenced absolute bias of all equations. When compared with measured GFR, all formulas showed smaller absolute bias in higher age groups. No accuracy differences were found between the CKD­EPI equation and the MDRD equation in the oldest subgroup (>60 years). A small but not statistically significant difference was found with the CG equation.27

Further evaluation of estimated kidney function assessment in the elderly is necessary to determine the most accurate equation.

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CONCLUSION

Most studies have shown statistical differences but have not assessed the clinical differences between CG, MDRD, and CKD­EPI study equations. In the absence of clinical outcomes, such as therapeutic efficacy, or adverse drug reactions, it still is uncertain which formula should be used for adjustment of drugs in patients with CKD. It is important to note that elimination is only one of many factors that should be considered for dosage adjustment. Drug absorption, bioavailability, half­life, protein binding, volume distribution, and drug metabolism are equally important when considering a dosage adjustment in patients with CDK. Therefore, for most drugs, clinical judgment should inform health care providers about how and how much to adjust drugs in patients with CKD.

Financial disclosure: None declared.

From: ' The Old and New Methods of Assessing Kidney Function' by Jessica L. Steffl, PharmD, William Bennett, MD, and Ali J. Olyaei, PharmD

---J Clin Pharmacol 2012;52:63S-71S

REFERENCES

1. Albright A, Burrows NR, Jordan R, Williams DE. The Kidney Disease Initiative and the Division of Diabetes Translation at the Centers for Disease Control and Prevention. Am J Kidney Dis. 2009;53(3 suppl 3): S121­S125.
2. National Institute of Diabetes and Digestive and Kidney Diseases. Kidney and urologic diseases statistics for the United States. http://kidney.niddk.nih.gov/kudiseases/pubs/kustats/. Accessed 2011.
3. Guarnieri G, Zanetti M, Vinci P, Cattin MR, Pirulli A, Barazzoni R. Metabolic syndrome and chronic kidney disease. J Ren Nutr. 2010;20(5 supply): S19­S23.
4. Hahr AJ, Molitch ME. Diabetes, cardiovascular risk, and nephropathy. Cardiol Clin. 2010;28(3):467­475.
5. Seddon M, Curzen N. Coronary revascularisation in chronic kidney disease. Part 1: stable coronary artery disease. J Ren Care. 2010;36(suppl 1):106­117.
6. Seddon M, Curzen N. Coronary revascularisation in chronic kidney disease. Part II: acute coronary syndromes. J Ren Care. 2010;36(suppl 1):118­126.
7. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. n Engl J Med. 2004;351(13):1296­1305.
8. Wilson PW, Culleton BF. Epidemiology of cardiovascular disease in the United States. Am J Kidney Dis. 1998;32(5 suppl 3): S56­S65.
9. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 suppl 1): S1­266.
10. Levey AS, Coresh J, Balk E et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann intern Med. 2003;139(2):137­14.

11. Olyaei AJ, Steffl JL. A quantitative approach to drug dosing in chronic kidney disease. Blood Purif. 2011;31(1­3):138­145.
12. Olyaei AJ, Bennett WM. Drug dosing in the elderly patients with chronic kidney disease. Clin Geriatr Med. 2009;25(3): 459­527.
13. Tsinalis D, Thiel GT. An easy to calculate equation to estimate GFR based on inulin clearance. Nephrol Dial Transplant. 2009;24(10):3055­3061.
14. Imai E, Horio M, Nitta K et al. Estimation of glomerular filtration rate by the MDRD study equation modified for Japanese patients with chronic kidney disease. Clin Exp Nephrol. 2007; 11(1):41­50.
15. Jones CA, McQuillan GM, Kusek JW et al. Serum creatinine levels in the US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 1998;32(6):992­999.
16. Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT. Performance of the Cockcroft­Gault, MDRD, and new CKD­EPI formulas in relation to GFR, age, and body size. Clin J Am Soc Nephrol. 2010;5(6):1003­1009.
17. Jones CA, McQuillan GM, Kusek JW, et al. Serum creatinine levels in the US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 1998;32(6):992­999.
18. Salive ME, Jones CA, Guralnik JM, Agodoa LY, Pahor M, Wallace RB. Serum creatinine levels in older adults: relationship with health status and medications. Age Ageing. 1995;24(2): 142­150.
19. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. nephron. 1976;16(1):31­41.
20. US Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research. Guidance for industry: Pharmacokinetics in Pregnancy—Study Design, Data Analysis, and Impact on Dosing and Labeling. Clinical Pharmacology. Rockville, MD: US Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research; March 2010. 21. 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(6): 461­470.
22. 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(1):5­18.
23. 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(4):247­254.
24. Van den Noortgate NJ, Janssens WH, Afschrift MB, Lameire NH. Renal function in the oldest­old on an acute geriatric ward. int urol nephrol. 2001;32(4):531­537.
25. 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(12):929­937.
26. Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate. Ann intern Med. 2009;150(9): 604­612.
27. Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT. Performance of the Cockcroft­Gault, MDRD, and new CKD­EPI formulas in relation to GFR, age, and body size. Clin J Am Soc Nephrol. 2010;5(6):1003­1009.
28. Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new CKD Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD Study equation for estimated GFR: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2010;55(4):648­659.
29. White SL, Polkinghorne KR, Atkins RC, Chadban SJ. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD­EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity, and Lifestyle) Study. Am J Kidney Dis. 2010;55(4):660­670.
30. Stevens LA, Nolin TD, Richardson MM, et al. Comparison of drug dosing recommendations based on measured GFR and kidney function estimating equations. Am J Kidney Dis. 2009;54(1):33­42.
31. Golik MV, Lawrence KR. Comparison of dosing recommendations for antimicrobial drugs based on two methods for assessing kidney function: Cockcroft­Gault and modification of diet in renal disease. Pharmacotherapy. 2008;28(9):1125­1132.
32. Hermsen ED, Maiefski M, Florescu MC, Qiu F, Rupp ME. Comparison of the Modification of Diet in Renal Disease and Cockcroft­Gault equations for dosing antimicrobials.Pharmacotherapy. 2009;29(6):649­655.
33. Wargo KA, Eiland EH III, Hamm W, English TM, Phillippe HM. Comparison of the modification of diet in renal disease and Cockcroft­Gault equations for antimicrobial dosage adjustments. Ann Pharmacother. 2006;40(7­8):1248­1253.
34. Wargo KA, English TM. Evaluation of the chronic kidney disease epidemiology collaboration equation for dosing antimicrobials. Ann Pharmacother. 2010;44(3):439­446.
35. Lindeman RD, Tobin J, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc. 1985;33(4):278­285.
36. Lamb EJ, Webb MC, Simpson DE, Coakley AJ, Newman DJ, O’Riordan SE. Estimation of glomerular filtration rate in older patients with chronic renal insufficiency: is the modification of diet in renal disease formula an improvement? J Am Geriatr Soc. 2003;51(7):1012­1017.
37. Garg AX, Papaioannou A, Ferko N, Campbell G, Clarke JA, Ray JG. Estimating the prevalence of renal insufficiency in seniors requiring long­term care. Kidney int. 2004;65(2):649­653.