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CLINICAL RESEARCH: RENAL FUNCTION IN ACUTE CORONARY SYNDROME

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

Chiara Melloni, MD, MHS^_*, Eric D. Peterson, MD, MPH^_*, Anita Y. Chen, MS^_*, Lynda A. Szczech, MD, MSCE^_*, L. Kristin Newby, MD, MHS^_*, Robert A. Harrington, MD^_*, W. Brian Gibler, MD, E. Magnus Ohman, MD^_*, Sarah A. Spinler, PharmD, FCCP, Matthew T. Roe, MD, MHS^_* and Karen P. Alexander, MD^_*,_*

^_* Division of Cardiology and Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina
Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio
Philadelphia College of Pharmacy, University of the Sciences in Philadelphia, Philadelphia, Pennsylvania.

Manuscript received May 29, 2007; revised manuscript received October 5, 2007, accepted November 8, 2007.

^_* Reprint requests and correspondence: Dr. Karen P. Alexander, 2400 Pratt Street, Duke Clinical Research Institute, Durham, North Carolina 27705. (Email: alexa019{at}mc.duke.edu).

	Abstract

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Objectives: Our purpose was to compare formulae for estimating glomerular filtration rate (GFR) in non–ST-segment elevation acute coronary syndromes (NSTE ACS) patients.

Background: Assessment of GFR is important for antithrombotic dose adjustment in NSTE ACS patients.

Methods: We assessed estimated glomerular filtration rate (eGFR) with Cockcroft-Gault (C-G) and Modification of Diet in Renal Disease (MDRD) formulae in 46,942 NSTE ACS patients from 408 CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the American College of Cardiology/American Heart Association Guidelines) hospitals. Formula agreement was shown continuously and by chronic kidney disease (CKD) stages. We determined in-hospital outcomes and the association between antithrombotic dose adjustment and bleeding for moderate CKD as determined by each formula.

Results: The median (interquartile range [IQR]) eGFR was 53.2 ml/min (34.7, 75.1 ml/min) by C-G and 65.8 ml/min (47.6, 83.5 ml/min) by MDRD. The mean eGFR was higher with MDRD (9.1 ml/min), but this difference was greater in age, weight, and gender subgroups. Chronic kidney disease classification differed in 20% of the population and altered when antithrombotic dose adjustment was required by C-G versus MDRD (eptifibatide: 45.7% vs. 27.3%; enoxaparin: 19.0% vs. 9.6%).

Conclusions: Important CKD disagreements occur in 20% of acute coronary syndrome patients, affecting dosing adjustments in those already susceptible to bleeding. Dosing based on C-G formula is preferable, particularly in the small, female, or elderly patient.

Abbreviations and Acronyms   ACC/AHA = American College of Cardiology/American Heart Association   BMI = body mass index   CI = confidence interval   CKD = chronic kidney disease   C-G = Cockcroft-Gault   eGFR = estimated glomerular filtration rate   GFR = glomerular filtration rate   GP = glycoprotein   MDRD = Modification of Diet in Renal Disease   NSTE ACS = non–ST-segment elevation acute coronary syndromes   OR = odds ratio

Neither formula was developed or validated in patients with cardiac disease; moreover, they differ in variables and coefficients. The C-G formula is recommended by the American College of Cardiology/American Heart Association (ACC/AHA) guidelines and the Food and Drug Administration and is easier to calculate at the bedside (8,9). The MDRD equation is recommended by the National Kidney Foundation as more accurate for estimating GFR (6,10) and is currently generated in many laboratory reports.

Despite its importance in the care of cardiac patients, eGFR formula comparisons in this population have not been performed. Therefore, using data from the CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the ACC/AHA Guidelines) quality improvement initiative, the agreement between formulae in determining eGFR and CKD stages was described. The magnitude and implication of formula disagreements were explored in relation to antithrombotic dose and dose-associated risk of bleeding. In so doing, we sought to determine the preferable formula for the assessment of eGFR in a cardiac population in relation to dosing and bleeding.

	Methods

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The CRUSADE initiative is an observational registry of high-risk patients with NSTE ACS admitted to U.S. hospitals since November 2001. Criteria for inclusion in CRUSADE include acute ischemic symptoms lasting for 10 min within 24 h of hospital arrival and 1 or more high-risk features: ST-segment depression 0.5 mm, transient ST-segment elevation 0.5 to 1.0 mm (lasting for <10 min), and/or positive cardiac markers (elevated troponin I or T and/or creatine kinase-MB greater than the upper limit of normal for the local laboratory). Patients are ineligible for CRUSADE if they transfer into a participating hospital >24 h after the last episode of ischemic symptoms. Details of CRUSADE data collection are described elsewhere (11).

Study population.   The analysis population included patients admitted to 408 U.S. hospitals from January 2004 through March 2005. From 49,595 high-risk NSTE ACS patients, we excluded 1,625 (3.3%) with missing serum creatinine, 1,000 (2.1%) with missing data for key variables (age, gender, weight, or race) used to calculate eGFR, and 28 (0.06%) with serum creatinine values >15 mg/dl, leaving a final study population of 46,942 patients. For the analysis of in-hospital outcomes, 5,724 (12.2%) transfer-out patients were excluded. For the major bleeding analysis, 4,775 (10.2%) patients who underwent coronary artery bypass graft surgery and 1,289 (2.7%) with baseline hematocrit <26% were excluded.

Categorization of CKD.   Serum creatinine, lean body weight, race, gender, and age were used to determine eGFR by C-G (4) and abbreviated MDRD (5) formula across the entire population. Cockcroft-Gault calculates creatinine clearance as CrCl (ml/min) = ([{140 – age in years} x body weight in kg]/{72 x Cr in mg/dl}) x 0.85 (female gender), and the abbreviated MDRD formula estimates GFR as (ml/min/1.73 m² of body surface area) = 186 x (serum creatinine in mg/dl)^–1.154 x (age in years)^–0.203 x (0.742 if female gender) x (1.210 if African American). Cockcroft-Gault formula was adjusted for lean body weight to improve glomerular filtration rate (GFR) estimation (12). Both formulae provide estimated GFR (ml/min). Patients were classified into CKD stages: normal/mild CKD (eGFR 60 ml/min), moderate CKD (eGFR 30 to 59 ml/min), and severe CKD and kidney failure (eGFR <30 ml/min) (6).

Statistical analysis.   The median difference between eGFR formulae was determined on a per-patient basis overall and among subgroups by age (<75 vs. 75 years), gender, and body mass index (BMI) (<25 kg/m², 25 to 30 kg/m², >30 kg/m²). Spearman correlation was used to evaluate the relationship between eGFR by C-G versus MDRD formulae. A scatterplot showing the correlation between eGFR determined by C-G and MDRD with parallels to 60 ml/min on each axis demonstrates 4 groups of relative agreement. The fitted line on the plot was obtained from a generalized estimating equations spline regression model of MDRD on C-G (13). Next, we summarized the prevalence of CKD stages by each formula. Because most disagreements were in moderate CKD, we chose "moderate CKD agreement" as the reference group for comparison with "moderate CKD by MDRD only" (eGFR 30 to 59 ml/min by MDRD but 60 ml/min by C-G), and "moderate CKD by C-G only" (eGFR 30 to 59 ml/min by C-G but 60 ml/min by MDRD). To understand the implications of disagreement, we compared baseline characteristics and adverse in-hospital outcomes of death and major bleeding by moderate CKD agreement groups. Major bleeding was defined as any intracranial hemorrhage, red blood cell transfusion (2 U), or absolute drop in hematocrit of at least 12%.

We then evaluated the proportion of patients who required dose adjustment for glycoprotein (GP) IIb/IIIa inhibitors (eptifibatide and tirofiban) and low-molecular-weight heparin (enoxaparin) if eGFR were estimated by each formula (8,9). An excess dose of GP IIb/IIIa inhibitor was considered as a full dose of tirofiban if eGFR <30 ml/min or of eptifibatide if eGFR <50 ml/min. An excess dose of enoxaparin was considered to be 1 mg/kg every 12 h if eGFR <30 ml/min. For those treated with GP IIb/IIIa inhibitors or enoxaparin, major bleeding was shown for "no excess dose" and "excess dose" according to the categorization produced by each formula. In a validation analysis, rates of bleeding were consistent across BMI subgroups, emphasizing that anthropomorphic considerations did not alter these results. Adjusted rates of major bleeding for excess and no excess groups by each formula were determined. Adjustment was performed for age, renal insufficiency, gender, congestive heart failure, and systolic blood pressure (14).

Continuous variables are reported as medians (interquartile range [IQR]) compared using the Wilcoxon 2-sample test. Categorical variables are reported as frequencies compared using chi-square tests. A p value <0.05 was considered statistically significant. All analyses were performed by using SAS software version 8.2 (SAS Institute, Cary, North Carolina).

	Results

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Comparison between C-G and MDRD formulae.   Although there was generally good correlation between C-G and MDRD estimates of GFR (r = 0.89; p < 0.0001), the C-G formula calculated a lower median (IQR) eGFR for the population (C-G median 53.2 ml/min [IQR 34.7, 75.1 ml/min] vs. MDRD median 65.8 ml/min [IQR 47.6, 83.5 ml/min]). The difference between eGFRs (MDRD – C-G) on a per-patient basis was median 9.1 ml/min (IQR 0.9, 18.1 ml/min); however, elderly and female subgroups had an even lower eGFR by C-G, making subgroup differences more notable (Fig. 1). The younger (<75 years) male subgroup demonstrated the highest correlation between formulae (median 1.1 ml/min [IQR –5.3, 8.7 ml/min]) (Fig. 1). Older (75 years) male subjects had a larger median difference (median 12.5 ml/min [IQR 7.1, 19.4 ml/min]) similar to younger (<75 years) female subjects (median 12.4 ml/min [IQR 5.0, 20.2 ml/min]). The largest difference was in the older (75 years) female subjects (median 18.6 ml/min [IQR 11.9, 26.6 ml/min]). The difference between formulae was also greater with smaller BMI <25 kg/m² (median 11.3 ml/min [IQR 3.5, 19.9 ml/min]) and narrowed for the obese, BMI >30 kg/m² (median 6.3 ml/min [IQR –1.7, 15.3 ml/min]) (Fig. 2).

View larger version (11K): [in this window] [in a new window] [Download PPT slide]   Figure 1 Median Differences in eGFR (ml/min) Between MDRD and C-G Formulae Overall and by Subgroups Data are truncated at –30 and 30. C-G = Cockcroft-Gault; eGFR = estimated glomerular filtration rate; MDRD = Modification of Diet in Renal Disease.

View larger version (11K): [in this window] [in a new window] [Download PPT slide]   Figure 2 Median Differences in eGFR (ml/min) Between MDRD and C-G Formulae by BMI Categories Data are truncated at –30 and 30. BMI = body mass index; other abbreviations as in Figure 1.

Different CKD classification occurred in 20% of the population. Modification of Diet in Renal Disease identified 1.2% (n = 568) with moderate CKD who were classified as normal/mild CKD by C-G, and C-G identified 18.9% (n = 8,885) with moderate CKD who were classified by MDRD as normal/mild CKD. When both formulae had eGFR below 60 ml/min, disagreements occurred when MDRD classified moderate but C-G classified severe CKD (n = 4,435) (Fig. 3).

View larger version (29K): [in this window] [in a new window] [Download PPT slide]   Figure 3 Scatterplot of the Agreement Between C-G and MDRD Formulae in the Estimation of GFR Data in the figure represent 1% of a simple random sample of 46,942 patients. Lines drawn at 60 ml/min and 30 ml/min to emphasize categorical chronic kidney disease cut points as well as the linear relationship between formulae. Line of agreement shown. CrCl = creatinine clearance; other abbreviations as in Figure 1.

Actual dose adjustment and major bleeding.   Among patients treated with GP IIb/IIIa inhibitors (n = 17,632), excess dose by C-G eGFR occurred in 27.3% (n = 4,804), and excess dose by MDRD eGFR occurred in 12.6% (n = 2,221). Major bleeding occurred in 17.8% who received excess dose determined by C-G and 21.8% who received excess dose determined by MDRD. Major bleeding occurred in 7.4% who received no excess by C-G and 8.5% who received no excess by MDRD. The adjusted odds ratios (ORs) for major bleeding with GP IIb/IIIa excess dose based on C-G or MDRD were 1.31 (95% confidence interval [CI] 1.12 to 1.54) and 1.57 (95% CI 1.35 to 1.84), respectively.

Among patients treated with enoxaparin (n = 2,778), excess dose based on C-G occurred in 18.4% (n = 511), and excess dose by MDRD occurred in 12.6% (n = 351). Major bleeding occurred in 17.5% who received excess by C-G, 16.1% who received excess by MDRD, 8.5% who received no excess by C-G, and 9.3% who received no excess by MDRD. The adjusted ORs of in-hospital major bleeding for enoxaparin excess based on C-G and MDRD were 1.54 (95% CI 1.04 to 2.28) and 1.50 (95% CI 1.06 to 2.14), respectively.

Across groups defined by BMI, the rate of major bleeding was consistently higher when an excess dose of GP IIb/IIIa inhibitors was determined by MDRD, with those in the lowest BMI group having higher rates of bleeding (data not shown).

	Discussion

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In a high-risk NSTE ACS population, CKD is prevalent by either the C-G or MDRD formula. However, the median eGFR of the population is higher by MDRD, and categorization of moderate-to-severe CKD is less frequent. C-G calculates a lower eGFR, particularly in age, gender, and weight subgroups. More patients requiring dose adjustments are identified by C-G, particularly among subgroups already at high risk for bleeding.

CKD determination.   Chronic kidney disease is common and strongly predicts adverse in-hospital outcomes (15,16). Accurate delineation of CKD may help target aggressive treatments and limit the risks of therapy (3). Several studies (17,18) using reference indicators to compare C-G and MDRD formulae concluded that MDRD measures eGFR more precisely below 60 ml/min but overestimates eGFR in subgroups (e.g., elderly), a finding supported by our data. Whether eGFR should be adjusted for body size is less clear (17,19), yet the relationship between the formulae and outcomes remained consistent across BMI groups in our data. Although these formulae are well correlated, their disagreement is almost exclusively in the moderate CKD range and in female, elderly, and lower BMI patients.

Clinical implications.   Several studies have shown a linear relationship between eGFR and risk of bleeding (2,3). We found that patients with moderate CKD only by C-G had a rate of major bleeding similar to when there was CKD agreement. This suggests that C-G identifies more patients overall and that the additional patients classified only by C-G are at similarly high risk.

The high risk of bleeding may be related to renal dysfunction, but also to excess dosing of antithrombotic therapy (14,20). The MDRD formula identified one-half as many patients for dose adjustment as the C-G formula, particularly excluding those already at high risk of bleeding (14). This shift of 20% of the population across lines for dose adjustment appears to alter the safety of drugs in community practice. Guidelines already state that dose adjustments for antithrombotic therapy should be based on the C-G formula (8,9). Thus, MDRD identifies fewer patients for dose adjustment and increases the likelihood of dose-associated bleeding. Although calculating an eGFR is an important first step, C-G is more conservative and indicates the need for dose adjustment more often in at-risk subgroups. From a safety perspective, our results support current recommendations for use of C-G formula for antithrombotic dosing in an NSTE ACS population.

Study limitations.   Only baseline creatinine was available, so variation in creatinine during the hospital stay was not evaluated. Lack of calibration of serum creatinine measurement across hospitals could represent an additional source of variation. Excess dose only considered the initial dose, so subsequent adjustments were not considered. This analysis did not compare eGFR with a gold standard. Moreover, we do not know if eGFR was calculated before dosing or which formula was used at the point of care. Finally, clinical events were not adjudicated.

	Conclusions

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Whereas MDRD and C-G formulae are highly correlated, their categorical estimations of CKD differ in 20% of an acute coronary syndrome population. Most disagreement occurs in moderate CKD, particularly among older, smaller, and female subgroups. These differences translate into more antithrombotic dose adjustment by C-G and a lower risk of bleeding. Therefore, safety is enhanced by dosing based on C-G formula, particularly in the elderly, small, or female patients.

	Acknowledgments

 
The authors would like to thank the doctors and nurses participating in the CRUSADE initiative. They would also like to thank David Z. Bynum for his editorial assistance.

	Footnotes

 
The CRUSADE initiative is a National Quality Improvement Initiative of the Duke Clinical Research Institute. The CRUSADE initiative is funded by the Schering-Plough Corporation. The Bristol-Myers Squibb/Sanofi Pharmaceuticals Partnership provides additional funding support. Millennium Pharmaceuticals, Inc., funded part of this work. This work is also supported, in part, by a grant from the National Institute on Aging (R01 AG025312-01A1, PI Peterson). More information on CRUSADE can be found at http://www.crusadeqi.com.

	References

Top Abstract Methods Results Discussion Conclusions References

 
1. Sarnak MJ, Levey AS, Schoolwerth AC, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention Circulation 2003;108:2154-2169.[Free Full Text]

2. Gibson CM, Dumaine RL, Gelfand EV, et al. Association of glomerular filtration rate on presentation with subsequent mortality in non–ST-segment elevation acute coronary syndrome; observations in 13307 patients in five TIMI trials Eur Heart J 2004;25:1998-2005.[Abstract/Free Full Text]

3. Santopinto JJ, Fox KAA, Goldberg RJ, et al. Creatinine clearance and adverse hospital outcomes in patients with acute coronary syndromes: findings from the global registry of acute coronary events (GRACE) Heart 2003;89:1003-1008.[Abstract/Free Full Text]

4. Cockcroft D. Prediction of creatinine clearance from serum creatinine Nephron 1976;16:31-41.[Web of Science][Medline]

5. Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation Ann Intern Med 1999;130:461-470.[Abstract/Free Full Text]

6. 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;139:137-147.[Abstract/Free Full Text]

7. Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function—measured and estimated glomerular filtration rate N Engl J Med 2006;354:2473-2483.[Free Full Text]

8. U.S. Department of Health and Human ServicesFood and Drug AdministrationCenter for Drug Evaluation and Research (CDER)Center for Biologics Evaluation and Research (CBER) Guidance for Industry: Pharmacokinetics in Patients with Impaired Renal Function — Study Design, Data Analysis, and Impact on Dosing and Labelingwww.fda.gov/cder/guidance/1449fnl.pdf 2006Accessed August 8, 2007.

9. Braunwald E, Antman EM, Beasley JW, et al. ACC/AHA 2002 guideline update for the management of patients with unstable angina and non–ST-segment elevation myocardial infarction—summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on the Management of Patients With Unstable Angina) J Am Coll Cardiol 2002;40:1366-1374.[Free Full Text]

10. Brosius III FC, Hostetter TH, Kelepouris E, et al. Detection of chronic kidney disease in patients with or at increased risk of cardiovascular disease: a science advisory from the American Heart Association Kidney and Cardiovascular Disease Council; the Councils on High Blood Pressure Research, Cardiovascular Disease in the Young, and Epidemiology and Prevention; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: developed in collaboration with the National Kidney Foundation Circulation 2006;114:1083-1087.[Abstract/Free Full Text]

11. Hoekstra JW, Pollack Jr. CV, Roe MT, et al. Improving the care of patients with non–ST-elevation acute coronary syndromes in the emergency department: the CRUSADE initiative Acad Emerg Med 2002;9:1146-1155.[CrossRef][Web of Science][Medline]

12. Lim WH, Lim EM, McDonald S. Lean body mass-adjusted Cockcroft and Gault formula improves the estimation of glomerular filtration rate in subjects with normal-range serum creatinine Nephrology 2006;11:250-256.[CrossRef][Medline]

13. Liang K. Longitudinal data analysis using generalized linear models Biometrika 1986;73:13-22.[Abstract/Free Full Text]

14. Moscucci M, Fox KAA, Cannon CP, et al. Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE) Eur Heart J 2003;24:1815-1823.[Abstract/Free Full Text]

15. Al Suwaidi J, Reddan DN, Williams K, et al. Prognostic implications of abnormalities in renal function in patients with acute coronary syndromes Circulation 2002;106:974-980.[Abstract/Free Full Text]

16. Anavekar NS, McMurray JJV, Velazquez EJ, et al. Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction N Engl J Med 2004;351:1285-1295.[Abstract/Free Full Text]

17. Lamb E. Estimating kidney function in adults using formulae Ann Clin Biochem 2005;42:321-345.[CrossRef][Web of Science][Medline]

18. 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;51:1012-1017.[CrossRef][Web of Science][Medline]

19. Smilde TDJ, van Veldhuisen DJ, Navis G, Voors AA, Hillege HL. Drawbacks and prognostic value of formulas estimating renal function in patients with chronic heart failure and systolic dysfunction Circulation 2006;114:1572-1580.[Abstract/Free Full Text]

20. Alexander KP, Chen AY, Roe MT, et al. Excess dosing of antiplatelet and antithrombin agents in the treatment of non–ST-segment elevation acute coronary syndromes JAMA 2005;294:3108-3116.[Abstract/Free Full Text]

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