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Chronic Kidney Disease |
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* Department of Physiology and Biophysics, Georges Pompidou Hospital (AP-HP); INSERM U652 and IFR 58; Department of Nephrology, Georges Pompidou Hospital (AP-HP); René Descartes Medical School, Paris V University; and || Paris VI University, Paris, France
Address correspondence to: Dr. Marc Froissart, Department of Physiology and Biophysics, Georges Pompidou European Hospital, 20 rue Leblanc, 75015 Paris, France. Phone: +33-1-5609-3973; Fax: +33-1-5609-2675; E-mail: marc.froissart{at}egp.aphp.fr
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Abstract |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The formulas that are most widely used to estimate kidney function and that are recommended in adults by the K/DOQI guidelines (5) are the Cockcroft-Gault (CG) formula (8) and the recently developed (9) and later simplified (10) Modification of Diet in Renal Disease (MDRD) formula. The CG formula is an estimate of creatinine clearance originally developed in a population of 236 Canadian patients, 209 of which were male. The MDRD formulas have been developed as an estimation of 125I-Iothalamate renal clearance–based GFR measurement in a population of 1628 patients with previously diagnosed CKD (9–11). The mean GFR in this population was 39.8 ± 21.2 ml/min per 1.73 m2, and the mean age of the cohort was 50.6 ± 12.7 yr.
The K/DOQI CKD guidelines have established a five-stage classification of patients with CKD that is based solely on kidney function. These stages are defined by GFR 
90 ml/min per 1.73 m2 (stage 1), 60 to 89 ml/min per 1.73 m2 (stage 2), 30 to 59 ml/min per 1.73 m2 (stage 3), 15 to 29 ml/min per 1.73 m2 (stage 4), and <15 ml/min per 1.73 m2 (stage 5) (5). The guidelines state that the stage of kidney disease should be determined for each CKD patient and that a clinical action plan should be developed on the basis of the stage of disease (5). Thus, inaccurate estimation of kidney function may be responsible for misclassification of some patients and lead to inappropriate evaluation or treatment of these patients (12). However, so far, few studies have assessed the applicability of the MDRD and CG formulas to large cohorts of subjects with wide ranges of renal function. One study compared various formulas with 125I-iothalamate GFR in a cohort of 1703 blacks with presumed hypertensive nephrosclerosis and mean serum creatinine levels of 1.85 ± 0.88 mg/dl (13). All other studies focused on much smaller cohorts of subjects with or without CKD (14–18). Furthermore, with one exception (15), no particular attention was paid to calibration of serum creatinine measurements, although this has been shown to be of critical importance for individuals with normal or near normal serum creatinine values (19,20).
In this study, we compare renal clearance of 51Cr-EDTA (measured GFR) with GFR estimated by the CG formula (CG GFR) or the MDRD equation (MDRD GFR) in a cohort of 2095 European subjects. Our findings support the preferential use of the MDRD formula but raise caution regarding its usage in some subgroups of individuals, such as young adults with normal renal function or stage 2 CKD or underweight individuals.
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Materials and Methods |
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GFR Measurements
Renal clearance of 51Cr-EDTA was determined as described previously (21–23). Briefly, 3.5 MBq of 51Cr-EDTA (Amersham Health SA, Pantin, France) was injected intravenously as a single bolus. The injected dose was reduced to 1.8 MBq in patients with an estimated GFR derived from the CG formula of <30 ml/min and in case of body weight <40 kg. After allowing 1 h for distribution of the tracer in the extracellular fluid, urine was collected and discarded. Then, average renal 51Cr-EDTA clearance was determined on five consecutive 30-min clearance periods. Blood was drawn at the midpoint of each clearance period and up to 300 min after injection. The radioactivity measurements in 1-ml plasma and urine samples were carried out on a Packard Cobra 3-inch crystal 
-ray well counter (Boston, MA). When timed urine samples could not be obtained, plasma clearance of 51Cr-EDTA was calculated according to a simplified method described by Brochner-Mortensen (24). This was performed in 219 (10.5%) patients. In our hands, the coefficients of variation of renal clearance of 51Cr-EDTA and plasma clearance of 51Cr-EDTA were 8.4 ± 5.0 and 9.0 ± 5.3%, respectively, whereas the coefficient of variation of inulin clearance was 9.1 ± 6.3% in the same 22 patients. When compared with inulin renal clearance, the mean bias of EDTA renal clearance was 4.0 ± 4.9 ml/min per 1.73 m2 (Froissart et al., manuscript in preparation).
Creatinine Assay
All creatinine measurements were performed in the same laboratory. Blood samples were obtained simultaneously with the GFR measurement. A modified kinetic Jaffé colorimetric method was used with a Bayer RA-XT and a Konelab 20 analyzer. A five-point calibration was applied in each assay. Before measurement, ultrafiltration of plasma through a 20-kD cutoff membrane (MPS-1; Amicon, Beverly, MA) was performed to discard chromogens that were linked to albumin and other heavy proteins. In the absence of an international standard for creatinine assay, the linearity of the measurements was verified by using plasma samples from normal subjects in which increasing amounts of desiccated creatinine hydrochloride (MW 149.6; Sigma Chemicals, Perth, Australia) had been added.
Linear regression analysis showed that the slope of the relationship between measured and expected creatinine concentrations was 1.008 ± 0.006 (95% confidence interval, 0.997 to 1.020) and that the Y-intercept was 0.014 ± 0.013 (95% confidence interval, –0.013 to 0.041; Figure 1). Squared Spearman rank coefficient of correlation was 0.998. Internal quality controls showed a coefficient of variation of 2.3% during the period. An indirect evaluation of the stability of the measurement was obtained from the ratiometric expression of MDRD/GFR values over time. No clear shift was observed during the entire study period, supporting the absence of variation in creatinine calibration (data not shown). Calibration of our creatinine measurements [HEGPcr.] to the ones of the MDRD laboratory [MDRDcr.] Dr F. Van Lente showed a linear relationship defined by the following equation:
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Creatinine-Based Estimation of GFR
The two formulas that we studied to predict GFR from serum creatinine were the one proposed by Cockcroft and Gault (8):
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and the simplified form of the MDRD formula (10):
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where PCr is plasma creatinine concentration.
A correction for body surface area (BSA) was necessary for the CG formula. This was performed using estimated BSA according to Du Bois (25):
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Statistical Analyses
Demographic data were expressed as mean ± SD or median and interquartile range, as appropriate. Estimated and measured GFR are statistically dependent variables. To compare the creatinine-based estimations of GFR with the renal clearance of 51Cr-EDTA, we used Bland and Altman recommendations for such evaluations (26). The mean difference between estimated and measured GFR values directly estimates the global bias. The width of the SD of the mean difference is an estimation of precision; a large width means a low precision.
The absolute of the difference between estimated and measured GFR was used to estimate the accuracy of the creatinine-based formulas. It was expressed either in ml/min per 1.73 m2 or in percentage of GFR values and was represented in percentiles (50th, 75th, and 90th), allowing to draw absolute and relative boundaries for the lack of accuracy. The accuracy was also measured as the percentage of results that did not deviate >15, 30, and 50% from the measured GFR.
The combined root mean square error (CRMSE) was examined. CRMSE is calculated as the square root of [(mean difference between estimated and measured GFR)2 + (SD of the difference)2]. It measures both bias and precision (27). Statistical analyses were performed using Statview 5.0 software (SAS, Cary, NC).
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Results |
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60 versus <60 ml/min per 1.73 m2).
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65 yr had lower GFR values than younger ones (45.2 ± 24.3 versus 67.4 ± 33.4 ml/min per 1.73 m2; P < 0.0001). However, no significant interaction between gender and age was observed (P = 0.2880).
Relationships between Creatinine-Based Estimations of GFR and Measured GFR
The relationships between measured GFR and MDRD GFR or CG GFR are depicted in Figures 2 and 3, respectively. As shown in Figures 2Aand 3A, standard regression analyses of these relationships showed a good global agreement between the two variables (r = 0.910 and 0.894, respectively). However, as extensively studied by Bland and Altman, the measurement of agreement between two methods should be preferentially expressed using bias plots of the difference against the average (26,28,29). Such a plot showed a mean difference of –0.99 ml/min per 1.73 m2 between MDRD GFR and measured GFR (Figure 2B), which corresponds to a statistically significant (P = 0.001) but limited bias of the MDRD equation. Similarly, when applied to CG GFR, the Bland and Altman plot showed a mean difference of 1.94 ml/min per 1.73 m2 (Figure 3B), which is highly statistically significant (P < 0.0001) but has limited clinical implications. However, for both formulas, the biases were not uniform over the whole range of GFR values (Table 2
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Accuracy is a global indicator of the performance of a formula that takes into account its bias and its precision. We tested the accuracy of both formulas in subjects with measured GFR and <60 ml/min per 1.73 m2 by calculating CRMSE and by determining the percentage of subjects who did not deviate >15, 30, and 50% from measured GFR (accuracy within in Table 3). In all cases and with both measurements of accuracy, the MDRD formula had better performances than the CG one (Table 3).
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Biases of the MDRD and CG formulas with respect to gender and in two different age groups are shown in Figure 4. A cutoff age of 65 yr was chosen, because data from the United States Renal Data System show that the incident rates of ESRD are more than twofold higher in individuals who are 65 yr than in younger ones (1). The bias of the MDRD formula was very small in all subgroups, except for women who were younger than 65 yr (bias, –3.1 ± 17.2 ml/min per 1.73 m2), whereas the biases of the CG formula were always significantly larger (P < 0.0001).
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65 yr and had a measured GFR <60 ml/min per 1.73 m2.
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30 kg/m2 (obese, 279 subjects). ANOVA analysis showed that each BMI class was associated with statistically different GFR values (55.1 ± 32.0, 64.3 ± 32.9, 60.9 ± 32.2, and 52.2 ± 31.5 ml/min per 1.73 m2 from underweight to overweight subjects, respectively; P < 0.0001). As shown in Figure 6, the MDRD formula largely overestimated kidney function in underweight subjects; the bias observed for this subgroup (12.2 ± 24.8 ml/min per 1.73 m2) was significantly higher than the one observed for all other classes of BMI (P < 0.0001 by ANOVA test). In all other subgroups, the MDRD formula was less biased, more precise, and more accurate than the CG one (Figure 6).
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). This analysis was based solely on results of GFR determinations, and all 2095 subjects were considered, regardless of whether they had kidney damage. For subjects with GFR 90 ml/min per 1.73 m2, the CG formula was slightly more accurate than the MDRD one, but for all other GFR levels, more subjects were classified in the proper stage by the MDRD formula than by the CG one (Table 8). Overall, only 70.8 and 67.6% of subjects were classified in the correct stage by the MDRD and CG formulas, respectively. Using the average values of both formulas to estimate GFR did not improve the accuracy of the prediction (Table 8). The consequences of the limitations of the formulas can also be depicted by a figure plotting prediction intervals of measured GFR as a function of estimated GFR (Figure 7).
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Discussion |
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An important characteristics of our cohort is that it included subjects whose measured GFR ranged from 2.3 to 166.4 ml/min per 1.73 m2 (interquartile range, 33.6 to 87.3 ml/min per 1.73 m2), with similar numbers of subjects having measured GFR values and <60 ml/min per 1.73 m2 (1044 and 1051 subjects, respectively). Thus, the performances of the CG and MDRD formulas could be assessed over a wide range of kidney function. Furthermore, because the vast majority of patients included in this study were European, the performances of the MDRD and CG formulas could be assessed in a group of subjects whose anthropometric characteristics are slightly different from those of Americans. For example, when compared with the MDRD cohort (9,11), the mean weight of our study population was 11.2% lower (70.7 ± 15.3 versus 79.6 ± 16.8 kg) and the mean BSA was 6.3% lower (1.79 ± 0.21 versus 1.91 ± 0.23 kg/m2), whereas, on average, our patients were only 2.2 yr older than those included in the MDRD cohort (52.8 ± 16.5 versus 50.6 ± 12.7 yr) and a similar percentage of subjects were male in both cohorts (59 versus 60%).
Recent studies have emphasized the importance of careful calibration of serum creatinine measurements to estimate reliably GFR in patients with normal or near-normal renal function, using creatinine-based equations (19,20). In the absence of an international standard, we used plasma samples supplemented with precise amounts of creatinine hydrochloride to calibrate our assay. Analysis of the relationship between expected and measured creatinine concentration strongly suggests that our assay reliably measures creatinine concentrations. The relationship between measured and expected creatinine concentrations was linear over a wide range of values and not different from the identity line. Furthermore, in our population, the ratio of MDRD GFR over measured GFR did not vary over time, which suggests that no calibration bias occurred over time. This careful calibration of plasma creatinine measurements may explain that, for subjects with normal or near-normal kidney function, we found much less difference between estimated and measured GFR than in other studies (14,16,18,31).
In this study, GFR was measured by renal clearance of 51Cr-EDTA, whereas renal clearance of 125I-iothalamate has been used by studies in North America. However, the performance of our method is similar to what has been reported for iothalamate clearance (32).
Analysis of bias, a measure of systematic error, in the entire study population showed a very good global agreement between estimated and measured GFR for each of the two formulas. On average, estimated GFR was only 1.0 ml/min per 1.73 m2 lower than measured GFR with the MDRD formula and 1.9 ml/min per 1.73 m2 higher with the CG formula. A similar bias was observed when the CG formula was compared with GFR measured by 125I-iothalamate clearance in all patients who were screened for the African-American Study of Kidney Disease and Hypertension; the mean difference between estimated and measured GFR was –2.7 ml/min per 1.73 m2 (13). In contrast, in the MDRD cohort, the CG formula was shown largely to overestimate measured GFR (9). The reasons for this discrepancy are not clear, but it may be due to differences in patient characteristics.
When estimating the performance of a formula, precision is probably more important than bias. Our study showed that both the MDRD and the CG formulas largely lack precision. Previous studies that focused on patients with or without CKD have already highlighted the global lack of precision of these two formulas (13–16,31). However, in our analysis, their performances were different in various subgroups of subjects. The greatest lack of precision was observed for subjects who were younger than 65 yr and had measured GFR 60 ml/min per 1.73 m2 for underweight subjects and, in the case of the CG formula, for obese subjects.
Analysis of the ability of a formula to classify patients into different subgroups depends on the characteristics of the population. In particular, it depends on the proportion of patients who happen to be near the boundaries of the subgroups. In our series, analysis of the performance of both formulas to classify patients according to the K/DOQI CKD classification showed that only 70.8% of subjects were classified in the proper category when using the MDRD formula and 67.6% when using the CG one, which clearly highlights the limitations of both formulas. For example, when using the CG and the MDRD formulas, 28.8 and 16.7% of stage 4 CKD patients were misclassified as stage 3 CKD patients, respectively, which could introduce undue delays in the preparation for renal replacement therapy. By contrast, approximately 20% of subjects with measured GFR 60 ml/min per 1.73 m2 were classified as having stage 3 CKD with both formulas, which could lead to unnecessary assessment of CKD-related complications. Use of the average of the two formulas did not decrease the misclassification rate, which answers to one of the K/DOQI research recommendations (5). So as not to be misled by the use of the formulas when taking care of individual CKD patients, it is probably important to keep in mind the width of the prediction interval for GFR associated with each value of estimated GFR (Figure 7).
In conclusion, in a study population of 2095 European subjects, the MDRD formula provided more reliable estimations of kidney function than the CG formula. However, both formulas lacked precision, and using either one of them for defining the stage of disease according to the K/DOQI CKD classification would have led to inappropriate staging of approximately 30% of subjects.
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Acknowledgments |
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We gratefully thank Dr. Van Lente for measuring plasma creatinine samples at the Cleveland Clinic Foundation.
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Footnotes |
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References |
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B. B. Newsome, D. G. Warnock, W. M. McClellan, C. A. Herzog, C. I. Kiefe, P. W. Eggers, and J. J. Allison Long-term Risk of Mortality and End-Stage Renal Disease Among the Elderly After Small Increases in Serum Creatinine Level During Hospitalization for Acute Myocardial Infarction Arch Intern Med, March 24, 2008; 168(6): 609 - 616. [Abstract] [Full Text] [PDF]
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M. Cirillo, M. P. Lanti, A. Menotti, M. Laurenzi, M. Mancini, A. Zanchetti, and N. G. De Santo Definition of Kidney Dysfunction as a Cardiovascular Risk Factor: Use of Urinary Albumin Excretion and Estimated Glomerular Filtration Rate Arch Intern Med, March 24, 2008; 168(6): 617 - 624. [Abstract] [Full Text] [PDF]
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 P. J. Roderick, R. J. Atkins, L. Smeeth, D. M. Nitsch, R. B. Hubbard, A. E. Flectcher, and C. J. Bulpitt Detecting chronic kidney disease in older people; what are the implications? Age Ageing, March 1, 2008; 37(2): 179 - 186. [Abstract] [Full Text] [PDF]
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O. Moranne, L. Watier, J. Rossert, B. Stengel, and The GN-Progress Study Group Primary glomerulonephritis: an update on renal survival and determinants of progression QJM, March 1, 2008; 101(3): 215 - 224. [Abstract] [Full Text] [PDF]
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B. G. Jaar, R. Khatib, L. Plantinga, L. E. Boulware, and N. R. Powe Principles of Screening for Chronic Kidney Disease Clin. J. Am. Soc. Nephrol., March 1, 2008; 3(2): 601 - 609. [Full Text] [PDF]
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 J. Coresh, L. A. Stevens, and A. S. Levey Determining Prevalence of Chronic Kidney Disease Using Estimated Glomerular Filtration Rate--Reply JAMA, February 13, 2008; 299(6): 631 - 632. [Full Text] [PDF]
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N. Bassilios, P. Martel, V. Godard, M. Froissart, J.-P. Grunfeld, B. Stengel, and on behalf of the Reseau Nephropar Monitoring of glomerular filtration rate in lithium-treated outpatients--an ambulatory laboratory database surveillance Nephrol. Dial. Transplant., February 1, 2008; 23(2): 562 - 565. [Abstract] [Full Text] [PDF]
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C. Mariat, N. Maillard, M. Phayphet, L. Thibaudin, S. Laporte, E. Alamartine, and F. Berthoux Estimated glomerular filtration rate as an end point in kidney transplant trial: where do we stand? Nephrol. Dial. Transplant., January 1, 2008; 23(1): 33 - 38. [Full Text] [PDF]
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M. Essig, B. Escoubet, D. de Zuttere, F. Blanchet, F. Arnoult, E. Dupuis, C. Michel, F. Mignon, F. Mentre, C. Clerici, et al. Cardiovascular remodelling and extracellular fluid excess in early stages of chronic kidney disease Nephrol. Dial. Transplant., January 1, 2008; 23(1): 239 - 248. [Abstract] [Full Text] [PDF]
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M. Chonchol, M. Cigolini, and G. Targher Association between 25-hydroxyvitamin D deficiency and cardiovascular disease in type 2 diabetic patients with mild kidney dysfunction Nephrol. Dial. Transplant., January 1, 2008; 23(1): 269 - 274. [Abstract] [Full Text] [PDF]
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M. Tidman, P. Sjostrom, and I. Jones A Comparison of GFR estimating formulae based upon s-cystatin C and s-creatinine and a combination of the two Nephrol. Dial. Transplant., January 1, 2008; 23(1): 154 - 160. [Abstract] [Full Text] [PDF]
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R. J Belloto Jr On Statins, Strokes, Meta-Analyses, Competing Risks, and the Onward March of Science Ann. Pharmacother., December 1, 2007; 41(12): 2055 - 2057. [Abstract] [Full Text] [PDF]
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 N. Kasitanon, D.M. Fine, M. Haas, L.S. Magder, and M. Petri Estimating renal function in lupus nephritis: comparison of the Modification of Diet in Renal Disease and Cockcroft Gault equations Lupus, November 1, 2007; 16(11): 887 - 895. [Abstract] [PDF]
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 I. Gouin-Thibault, E. Pautas, I. Mahe, C. Descarpentries, V. Nivet-Antoine, J.-L. Golmard, and V. Siguret Is Modification of Diet in Renal Disease Formula Similar to Cockcroft Gault Formula to Assess Renal Function in Elderly Hospitalized Patients Treated With Low-Molecular-Weight Heparin? J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2007; 62(11): 1300 - 1305. [Abstract] [Full Text] [PDF]
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E. J. Lamb, M. C. Webb, and S. E O'Riordan Using the modification of diet in renal disease (MDRD) and Cockcroft and Gault equations to estimate glomerular filtration rate (GFR) in older people Age Ageing, November 1, 2007; 36(6): 689 - 692. [Full Text] [PDF]
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J. K. Inrig, B. S. Gillespie, U. D. Patel, L. P. Briley, L. She, J. D. Easton, E. Topol, and L. A. Szczech Risk for Cardiovascular Outcomes among Subjects with Atherosclerotic Cardiovascular Disease and Greater-than-Normal Estimated Glomerular Filtration Rate Clin. J. Am. Soc. Nephrol., November 1, 2007; 2(6): 1215 - 1222. [Abstract] [Full Text] [PDF]
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J. Gill, R. Malyuk, O. Djurdjev, and A. Levin Use of GFR equations to adjust drug doses in an elderly multi-ethnic group a cautionary tale Nephrol. Dial. Transplant., October 1, 2007; 22(10): 2894 - 2899. [Abstract] [Full Text] [PDF]
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L. A. Stevens, J. Coresh, H. I. Feldman, T. Greene, J. P. Lash, R. G. Nelson, M. Rahman, A. E. Deysher, Y. Zhang, C. H. Schmid, et al. Evaluation of the Modification of Diet in Renal Disease Study Equation in a Large Diverse Population J. Am. Soc. Nephrol., October 1, 2007; 18(10): 2749 - 2757. [Abstract] [Full Text] [PDF]
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M. S. MacGregor How common is early chronic kidney disease?: A Background Paper prepared for the UK Consensus Conference on Early Chronic Kidney Disease Nephrol. Dial. Transplant., September 1, 2007; 22(suppl_9): ix8 - ix18. [Full Text] [PDF]
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K. Barraclough, M. Harris, V. Montessori, and A. Levin An unusual case of acute kidney injury due to vancomycin lessons learnt from reliance on eGFR Nephrol. Dial. Transplant., August 1, 2007; 22(8): 2391 - 2394. [Abstract] [Full Text] [PDF]
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 V Launay-Vacher, E Chatelut, S. Lichtman, H Wildiers, C Steer, and M Aapro Renal insufficiency in elderly cancer patients: International Society of Geriatric Oncology clinical practice recommendations Ann. Onc., August 1, 2007; 18(8): 1314 - 1321. [Abstract] [Full Text] [PDF]
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 V. Rigalleau, C. Lasseur, C. Raffaitin, M.-C. Beauvieux, N. Barthe, P. Chauveau, C. Combe, and H. Gin Normoalbuminuric Renal-Insufficient Diabetic Patients: A lower-risk group Diabetes Care, August 1, 2007; 30(8): 2034 - 2039. [Abstract] [Full Text] [PDF]
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M.-C. Beauvieux, F. Le Moigne, C. Lasseur, C. Raffaitin, C. Perlemoine, N. Barthe, P. Chauveau, C. Combe, H. Gin, and V. Rigalleau New Predictive Equations Improve Monitoring of Kidney Function in Patients With Diabetes Diabetes Care, August 1, 2007; 30(8): 1988 - 1994. [Abstract] [Full Text] [PDF]
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Y. Maaravi, M. Bursztyn, R. Hammerman-Rozenberg, and J. Stessman Glomerular filtration rate estimation and mortality in an elderly population QJM, July 1, 2007; 100(7): 441 - 449. [Abstract] [Full Text] [PDF]
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 R. Shadman, M. A. Allison, and M. H. Criqui Glomerular Filtration Rate and N-Terminal Pro-Brain Natriuretic Peptide as Predictors of Cardiovascular Mortality in Vascular Patients J. Am. Coll. Cardiol., June 5, 2007; 49(22): 2172 - 2181. [Abstract] [Full Text] [PDF]
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R. Dhingra, L. M. Sullivan, C. S. Fox, T. J. Wang, R. B. D'Agostino Sr, J. M. Gaziano, and R. S. Vasan Relations of Serum Phosphorus and Calcium Levels to the Incidence of Cardiovascular Disease in the Community Arch Intern Med, May 14, 2007; 167(9): 879 - 885. [Abstract] [Full Text] [PDF]
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 A. Gayet-Ageron, J. Ananworanich, T. Jupimai, P. Chetchotisakd, W. Prasithsirikul, S. Ubolyam, M. Le Braz, K. Ruxrungtham, J. F. Rooney, B. Hirschel, et al. No change in calculated creatinine clearance after tenofovir initiation among Thai patients J. Antimicrob. Chemother., May 1, 2007; 59(5): 1034 - 1037. [Abstract] [Full Text] [PDF]
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 B. Hellmich, O. Flossmann, W. L Gross, P. Bacon, J. Willem Cohen-Tervaert, L. Guillevin, D. Jayne, A. Mahr, P. A Merkel, H. Raspe, et al. EULAR recommendations for conducting clinical studies and/or clinical trials in systemic vasculitis: focus on anti-neutrophil cytoplasm antibody-associated vasculitis Ann Rheum Dis, May 1, 2007; 66(5): 605 - 617. [Abstract] [Full Text] [PDF]
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D. Singh, M. A. Whooley, J. H. Ix, S. Ali, and M. G. Shlipak Association of cystatin C and estimated GFR with inflammatory biomarkers: the Heart and Soul Study Nephrol. Dial. Transplant., April 1, 2007; 22(4): 1087 - 1092. [Abstract] [Full Text] [PDF]
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A. S. Levey, J. Coresh, T. Greene, J. Marsh, L. A. Stevens, J. W. Kusek, F. Van Lente, and for Chronic Kidney Disease Epidemiology Collaborat Expressing the Modification of Diet in Renal Disease Study Equation for Estimating Glomerular Filtration Rate with Standardized Serum Creatinine Values Clin. Chem., April 1, 2007; 53(4): 766 - 772. [Abstract] [Full Text] [PDF]
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V. Rigalleau, C. Lasseur, C. Raffaitin, C. Perlemoine, N. Barthe, P. Chauveau, C. Combe, and H. Gin The Mayo Clinic quadratic equation improves the prediction of glomerular filtration rate in diabetic subjects Nephrol. Dial. Transplant., March 1, 2007; 22(3): 813 - 818. [Abstract] [Full Text] [PDF]
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A. Blanchard, M. Azizi, S. Peyrard, N. Stern, F. Alhenc-Gelas, P. Houillier, and X. Jeunemaitre Partial Human Genetic Deficiency in Tissue Kallikrein Activity and Renal Calcium Handling Clin. J. Am. Soc. Nephrol., March 1, 2007; 2(2): 320 - 325. [Abstract] [Full Text] [PDF]
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B. R. Hemmelgarn, B. J. Manns, J. Zhang, M. Tonelli, S. Klarenbach, M. Walsh, B. F. Culleton, and for the Alberta Kidney Disease Network Association between Multidisciplinary Care and Survival for Elderly Patients with Chronic Kidney Disease J. Am. Soc. Nephrol., March 1, 2007; 18(3): 993 - 999. [Abstract] [Full Text] [PDF]
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B.R. Hemmelgarn, B.F. Culleton, and W.A. Ghali Derivation and validation of a clinical index for prediction of rapid progression of kidney dysfunction QJM, February 1, 2007; 100(2): 87 - 92. [Abstract] [Full Text] [PDF]
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R. A. Chudleigh, G. Dunseath, W. Evans, J. N. Harvey, P. Evans, R. Ollerton, and D. R. Owens How Reliable Is Estimation of Glomerular Filtration Rate at Diagnosis of Type 2 Diabetes? Diabetes Care, February 1, 2007; 30(2): 300 - 305. [Abstract] [Full Text] [PDF]
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K. E. Ensrud, L.-Y. Lui, B. C. Taylor, A. Ishani, M. G. Shlipak, K. L. Stone, J. A. Cauley, S. A. Jamal, D. M. Antoniucci, S. R. Cummings, et al. Renal Function and Risk of Hip and Vertebral Fractures in Older Women Arch Intern Med, January 22, 2007; 167(2): 133 - 139. [Abstract] [Full Text] [PDF]
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P. Delanaye, E. Cavalier, and J.-M. Krzesinski Estimated Glomerular Filtration Rate Ann Intern Med, January 2, 2007; 146(1): 74 - 74. [Full Text] [PDF]
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S. Jacob, M. Hery, J.-C. Protois, J. Rossert, and B. Stengel Effect of Organic Solvent Exposure on Chronic Kidney Disease Progression: The GN-PROGRESS Cohort Study J. Am. Soc. Nephrol., January 1, 2007; 18(1): 274 - 281. [Abstract] [Full Text] [PDF]
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J. H. Macdonald, S. M. Marcora, M. Jibani, G. Roberts, M. J. Kumwenda, R. Glover, J. Barron, and A. B. Lemmey Bioelectrical impedance can be used to predict muscle mass and hence improve estimation of glomerular filtration rate in non-diabetic patients with chronic kidney disease Nephrol. Dial. Transplant., December 1, 2006; 21(12): 3481 - 3487. [Abstract] [Full Text] [PDF]
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P. Gomez, L. M. Ruilope, V. Barrios, J. Navarro, M. A. Prieto, O. Gonzalez, L. Guerrero, M. A. S. Zamorano, C. Filozof, and on behalf of the FATH Study Group Prevalence of Renal Insufficiency in Individuals with Hypertension and Obesity/Overweight: The FATH Study J. Am. Soc. Nephrol., December 1, 2006; 17(12_suppl_3): S194 - S200. [Abstract] [Full Text] [PDF]
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