Applicability of Chronic Kidney Disease Epidemiology Collaboration equations in a Chinese population

Nephrology Dialysis Transplantation, Mar 2014

Background Accurate estimated glomerular filtration rates (eGFR) is an important step in the diagnosis of chronic kidney disease (CKD). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, based on creatinine alone (eGFRcr), was developed to improve on the Modification of Diet in Renal Disease equation, in particular by addressing the systematic underestimation of high GFR. Whether the CKD-EPI equation, based on cystatin C alone (eGFRcys), or the combined creatinine-cystatin C CKD-EPI equation (eGFRcr-cys C), actually perform better than the CKD-EPI equation based on creatinine (eGFRcr) remains unknown, especially in Asians including Chinese populations, where eGFR equations may overestimate true GFR.

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Applicability of Chronic Kidney Disease Epidemiology Collaboration equations in a Chinese population

Nephrol Dial Transplant Applicability of Chronic Kidney Disease Epidemiology Collaboration equations in a Chinese population Min Zhang 0 2 3 Yunshuang Chen 0 2 3 Li Tang 0 2 3 Jie Zhang 0 2 3 Shuwen Liu 0 2 3 Sihe Wang 0 1 2 Ribao Wei 0 2 3 Jianhui Zhou 0 2 3 Xueying Cao 0 2 3 Weiguang Zhang 0 2 3 Jinping Zhang 0 2 3 Yang Yang 0 2 3 Guangyan Cai 0 2 3 Xuefeng Sun 0 2 3 Xiangmei Chen 0 2 3 0 The Author 2013. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved 1 Department of Clinical Pathology, Cleveland Clinic , Cleveland, OH , USA 2 College , Beijing , China 3 State Key Laboratory of Kidney Diseases, Department of Nephrology, Chinese PLA General Hospital and Military Medical Postgraduate chinese population; CKD-EPI equation; glomerular filtration rate A B S T R AC T Background. Accurate estimated glomerular filtration rates (eGFR) is an important step in the diagnosis of chronic kidney disease (CKD). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, based on creatinine alone (eGFRcr), was developed to improve on the Modification of Diet in Renal Disease equation, in particular by addressing the systematic underestimation of high GFR. Whether the CKDEPI equation, based on cystatin C alone (eGFRcys), or the combined creatinine-cystatin C CKD-EPI equation (eGFRcrcys C), actually perform better than the CKD-EPI equation based on creatinine (eGFRcr) remains unknown, especially in Asians including Chinese populations, where eGFR equations may overestimate true GFR. Methods. A standard dual plasma sampling method (DPSM) of estimating 99mTc-diethylene triamine penta-acetic acid clearance was used to determine the reference or measured GFR (mGFR). Linear regression analysis, Bland–Altman analysis, bias, absolute bias and accuracy (P30) were used to compare the performance of the combined creatinine-cystatin C equation (eGFRcr-cys) and equations based on each marker alone (eGFRcr and eGFRcys) in Chinese subjects, including both patients with CKD and healthy individuals. Results. We enrolled 617 Chinese participants (49.11% female, 47.11 ± 17.25 years old), with a mean mGFR of 73.80 ± 37.55 mL/min/1.73 m2. The predictive abilities (r), the accuracy (P15, P30, P50), bias and absolute bias of the eGFRcr-cys equation were superior to eGFRcr equation and the eGFRcys equation in overall samples. Bland–Altman analysis also demonstrated a consistent result. When compared in subgroups, the accuracy (P30) of all three equations exceeded 90% at mGFR ≥90 mL/ min/1.73m2; the eGFRcr-cys equation had the highest accuracy (P30: 95.56%). At mGFR 60–89 mL/min/1.73 m2, the accuracies (P30) of the eGFRcr-cys and eGFRcr equations exceeded the acceptable level (≥70%), and there was no significant difference between them (P = 0.58). At mGFR <60 mL/min/1.73 m2, the accuracy (P30) of all three equations was below 70%, but the eGFRcr-cys equation had the greatest precision. Conclusions. The performances of the eGFRcr-cys and eGFRcr equations were similar to superior to that of the eGFRcys equation at higher GFR levels in an Asian population, especially in normal and mild to moderate kidney disease. Further improvement is needed for these equations at 2 GFR <60 mL/min per 1.73 m . I N T R O D U C T I O N Chronic kidney disease (CKD) has also been identified as a growing burden in China [1]. For the people of China, early diagnosis of CKD is likely to be the most cost-effective strategy. The glomerular filtration rate (GFR) is considered the best overall index of kidney function in health and disease. Thus, accurate measurement of GFR plays an important role in the clinical management of various renal diseases. A new equation based on serum creatinine (Scr) developed in 2009 by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) has been shown to be as accurate as the modification of diet in renal disease (MDRD) equation in 2 subjects with estimated GFR (eGFR) <60 mL/min/1.73 m , and more accurate than the MDRD-4 equation in those with eGFR ≥60 mL/min/1.73 m2 [2–4]. The validity of the CKDEPI equation has been demonstrated in healthy adult Indians [5], in African populations [6] and in Australians [7]. However, some studies have suggested that the CKD-EPI equation does not, in fact, provide greater accuracy [8]. In Caucasians, the use of the CKD-EPI equation yields higher eGFR results in young people and lower estimates in elderly individuals than the MDRD Study equation [9]. Some studies have suggested that serum cystatin C (s-CysC), a novel serum marker, may be a better marker than Scr and could even replace creatinine in GFR evaluations in some groups [10]. For example, GFR equations based on s-CysC were more accurate than Scr-based measurements in patients with type 1 diabetes [11]. Recently, a study in Spain found that the s-CysCbased equation was superior to the CKD-EPI equation [12]. In 2012, the CKD-EPI developed estimation equations based on cystatin C alone and in combination with creatinine, and demonstrated that the combined creatinine-cystatin C equation performed better than equations based on either of the markers alone [13]. However, the applicabilities of the eGFRcr-cys, eGFRcr and eGFRcys equations in Chinese populations are unknown. The present study used dual plasma sampling method (DPSM), which was accepted as the standard GFR method at the 21st International Annual Meeting of the Society of Nuclear Medicine Europe [14], as the reference GFR to compare the performance of the three equations in Chinese subjects, including patients with CKD and healthy individuals. M AT E R I A L S A N D M E T H O D S Participants The present study was conducted with a single data set, at the Chinese PLA General Hospital, Beijing, China, from October 2008 to October 2010. We included 617 consecutive Chinese subjects, of which 310 were inpatients with CKD and 307 were healthy individuals. CKD was diagnosed and classified using the kidney disease outcomes quality initiative (K/DOQI) criteria [15]. Patients with acute renal failure, edema, pleural or abdominal effusion, amputation, heart failure, liver disease, extreme adiposity or ketoacidosis, and those who were pregnant or menstruating, using renal replacement therapy, had thyroid disease, or who were currently taking high-dose steroids or cimetidine were excluded. Inclusion criteria for healthy individuals was based on the SENIEUR draft criteria [16] and the following: (i) no history of or on-going symptomatic cardiac, brain, pulmonary or hepatic disease, (ii) blood pressure <140/90 mmHg, (iii) normal electrocardiogram, thoracic radiographs, abdominal ultrasound and echocardiography results and (iv) good nutritional status, as demonstrated by total serum protein >60 g/L, serum albumin >35 g/L, prealbumin >0.2 g/L, transferring >2 g/L and body mass index (BMI) >18.5 kg/m2. Total body muscle mass was calculated using Lee’s equation [17]. Ethics The study was performed in accordance with the Declaration of Helsinki II and was approved by the Medical Science Ethics Committee of the Chinese PLA General Hospital. All participants provided written informed consent after receiving a clear explanation of the potential risks of the study. Laboratory methods Serum assays: Scr levels were measured using a Roche enzymatic assay (Hitachi, Tokyo, Japan; reagents from Roche Diagnostics, Mannheim, Germany). s-CysC levels were measured by immunonephelometry (N Latex Cystatin C from Siemens Healthcare Diagnostics Products GmbH, Germany) using a special protein analyzer (BN Prospec System, Siemens, Germany). To ensure that our Scr values were calibrated equally to the CKD-EPI study, we randomly selected 44 fresh-frozen plasma samples in our laboratory from our specimens and analyzed them in both our laboratory and the Cleveland Clinic Laboratory. The Scr value measured in our laboratory could be calibrated to that measured by the Cleveland Clinic Laboratory, which used a Roche/Hitachi 912/MODULAR analyzer, using the Deming regression equation: CCF Scr (µmol/L) = −7.9923 + 0.9653 × China Scr (µmol/L) (R = 0.9994). We also recalibrated s-CysC values by dry mass determination to the standardized s-CysC at the Cleveland Clinic Laboratory using the Roche/ Hitachi MODULAR P analyzer, using the Deming regression equation: CCF s-CysC = 0.0197 + 0.9479 × China s-CysC (R = 0.9903). The radioactive tracer used 99mTc-diethylene triamine pentaacetic acid (99mTc-DTPA; Chinese Atomic Scientific Academy, Beijing, China), with a radiochemical purity >95%. 2 The average reference GFR (mGFR, mL/min/1.73 m ) was measured by estimating 99mTc-DTPA clearance. 99mTc-DTPA plasma clearance was measured using the DPSM and calculated using the following formula [18]: mGFR ¼ ½D1nðP1=P2Þ where D represents the dosage (/mL/min) of drug injected, T1 and T2 (min) represent the collection times of the first and second blood samples (∼2 and 4 h postinjection, respectively), P1 and P2 (/mL/min) represent plasma activity at T1 and T2, and body surface area (BSA; m2) = 0.007184 × body weight (kg)0.425 × body height (cm)0.725, as described by the DuBois equation [19]. The following correction was used: corrected radioactivity ¼ radioactivity after decay expðln½2 interval=6:02Þ The equations evaluated are shown in Appendix 1. Calibrated CCF Scr and s-CysC values were put into the equations: eGFRcr equation [4], eGFRcys equation [13], eGFRcr-cys equation [13]: Statistical analysis Least-squares linear regression and Pearson’s correlation were used to assess the relationship between eGFR obtained by each equation versus mGFR. A Bland–Altman analysis was performed and plotted to compare eGFR with mGFR intuitively. The precision was represented as the width between the 95% limits of agreement (LOA). The smaller the LOA, the greater precision. Bias (median difference) was median difference between eGFR and mGFR. Precision was expressed as IQR (P75-P25). Absolute bias was the median difference in measurements |(eGFR–mGFR)|. Accuracy was expressed as the percentages of eGFR within 15% of mGFR (P15), 30% of mGFR (P30) and 50% of mGFR (P50). Confidence intervals were computed by bootstrap methods (2000 bootstraps). The Wilcoxon test and McNemar test were used to compare differences between the equations evaluated. P values <0.05 were considered to indicate statistical significance. Microsoft Office Excel 2003 (Microsoft Corporation, Redmond, WA, USA), SPSS (ver. 17.0; SPSS, Inc., Chicago, IL, USA), and Medcalc (ver. 4.3 for Windows; Medcalc Software, Mariekerke, Belgium) were used for data analyses. R E S U L T S General clinical characteristics We enrolled 617 consecutive Chinese participants (314 males, 303 females): 310 had CKD and 307 were healthy. Causes of CKD included IgA nephropathy (104 cases; 33.55%), diabetic nephropathy (47 cases; 15.16%), tubulointerstitial nephritis (26 cases; 8.39%), hypertensive nephropathy (24 cases; 7.74%) and others. The causes of CKD are listed in Appendix 2. For all participants, the average mGFR was 73.80 ± 37.55 mL/min/1.73 m2, the average age 47.11 ± 17.25 years, the average s-CysC and Scr were 1.36 ± 1.04 and 1.52 ± 1.72 mg/dL, respectively. According to the level of mGFR, participants were divided into four subgroups: mGFR ≥90, mGFR 60–89, mGFR 30–59 and mGFR <30 mL/min/1.73 m2. Laboratory data are presented in Table 1. Applicability of the equations in the overall sample Least-squares linear regression and Pearson’s correlation showed that the eGFRcr-cys equation had similar Pearson’s correlation coefficients with eGFRcr equation (r = 0.887 and r = 0.868, respectively, P = 0.157) but correlated significantly better with mGFR than the eGFRcys equation (r = 0.848, P = 0.006) using data for all participants (Table 2). Table 2 also lists the performance of the three equations in the overall sample, determined by calculating the accuracy, bias, and absolute bias. The biases of the eGFRcr-cys equation were 2.614, which was smaller than eGFRcr and eGFRcys equation (5.399 and 4.417, respectively, P < 0.0001, P = 0.003), but the latter were not statistically significantly different (P = 0.683). The differences in absolute bias also demonstrated consistent results (10.004, 10.874, and 12.258, P = 0.033, P = 0.002), but the latter had statistically significantly difference (P < 0.0001). The accuracy (P30) of the three equations all exceeded 70%: 76.66, 72.61 and 72.12% (eGFRcr-cys: eGFRcys P = 0.008), (eGFRcr-cys: eGFRcr P = 0.013), respectively. The accuracy (P50) of the eGFRcr-cys equation was also significantly higher than those of the other two equations (eGFRcys: P = 0.005, eGFRcr P = 0.0003). Presion IQR (P75-P25) indicated that eGFRcr-cys had the highest precision (P75-P25 = 20.078) and eGFRcys had the lowest precision (P75-P25 = 23.87). Bland–Altman analysis (Figure 1A–C) also demonstrated a consistent result that the eGFRcr-cys had the highest precisions; the gaps between the 95% LOA were 69.7 mL/min/1.73 m2, while the precision of the eGFRcys was the lowest (LOA = 81.4 mL/min/1.73 m2). mGFR (mL/min per 1.73m2) (mean ± SD) IQR (P25, P75) 20.636 (−4.122, 16.514) 23.87 (−6.374, 17.496) 20.078 (−5.516, 14.562) 24.47 (−13.384, 10.886) 28.223 (−13.851, 14.372) 27.702 (−14.807, 12.895) 25.673 (3.646, 22.027) 27.208 (−1.932, 25.276) 21.502 (0.090, 21.412) 26.248 (2.053, 28.301) 23.157 (−5.444, 17.713) 22.28 (−3.797, 18.483) 9.9 (−5.327, 4.573) 9.388 (−3.002, 6.386) 8.096 (−4.978, 3.118) *P < 0.05, compared with the CKD-EPI Scr C equation in the same subgroup. **P < 0.05, compared with the CKD-EPI cys C equation in the same subgroup. Applicability of equations in the subgroups At mGFR ≥90 mL/min/1.73 m2, the eGFRcr-cys had the greatest accuracy (P30 = 95.56%), statistically significantly higher than that of the eGFRcys (P30 = 91.56%, P = 0.020). The Pearson’s correlation coefficient (r) of eGFRcr (r = 0.312) and eGFRcr-cys (r = 0.274) higher than the eGFRcys (r = 0.184), but there were not statistically significantly different (P = 0.151, P = 0.667, P = 0.314). The bias of three equations had no significant differences (P = 0.072, P = 0.222, P = 0.195, Table 2). The absolute bias of the eGFRcr is smallest (12.465, Table 2) of all three equations. Presion IQR (P75-P25) demonstrated that the eGFRcys showed the lowest precision (P75-P25 = 28.223), lower than the eGFRcr-cys and the eGFRcr (P75-P25 = 27.702, P75-P25 = 24.47, respectively). At mGFR 60–89 mL/min per 1.73 m2, the three equations showed similar Pearson’s correlation coefficients (P = 0.573, P = 0.244, P = 0.547). The accuracy (P30) of eGFRcr-cys (P30 = 75.84%) and eGFRcr (P30 = 74.16%) is similar (P = 0.578). The accuracy (P30) of eGFRcr-cys was higher than eGFRcys (P30 = 67.98%, P = 0.008). The bias and the absolute bias of the eGFRcr-cys were the smallest (bias = 9.490, absolute bias = 12.247), but the absolute bias of the eGFRcr-cys and eGFRcr were significantly lower than that of the eGFRcys equation (P < .0001, P = 0.039). Presion IQR (P75-P25) demonstrated that the eGFRcys showed the lowest precision (P75-P25 = 27.208), lower than the eGFRcr-cys and the eGFRcr (P75-P25 = 21.502, P75-P25 = 25.673, respectively). At mGFR <60 mL/min per 1.73 m2, the accuracy (P30) of all the equations also failed to reach 70% and no significant difference was found among them. Presion IQR (P75-P25) demonstrated that the eGFRcr-cys C equation had the highest precision (Table 2). The performance of the three equations by GFR category is shown in Figure 2. D I S C U S S I O N The CKD-EPI equation based on creatinine has been applied widely in clinical practice and its applicability has been investigated extensively. In the present study, for the first time, we compared the performance of the eGFRcr-cys equation and the CKD-EPI equations based on either marker alone in a Chinese population. Our data demonstrated the performance of the eGFRcr-cys equation was the best in all subjects. eGFRcr equation, which was similar to eGFRcr-cys equation and superior to the eGFRcys equation with mGFR ≥90 mL/min per 1.73 m2 and at mGFR 60–89 mL/min per 1.73 m2. However, at mGFR <60 mL/min per 1.73 m2, the accuracy (P30) of all three equations was lower than the acceptable level of P30 accuracy, at least 70%, required by the K/DOQI criteria [15]. Determination of the GFR is an important first step in assessing kidney function in routine medical practice. Inulin clearance and other renal clearances of exogenous radiolabeled markers were used as markers of GFR evaluation, but were time-consuming, complex and expensive [20–22]. For these reasons, the use of inulin and some radiolabeled markers is limited. As a result, in clinical practice, prediction equations from creatinine were developed in routine clinical use. At the same time, cystatin C, a small, nonglycosylated 13-kDa basic protein, may have advantages over Scr for estimating GFR and has been proposed as an improved alternative filtration marker to creatinine for GFR evaluation [23]. In 2012, the study demonstrated the CKD-EPI equation combined creatinine F I G U R E 1 : Bland–Altman analysis (A–C) of the three equations in the overall sample. cystatin C performed better than equations based on each marker alone and also suggested that estimated GFR, based on serum cystatin C, could be used as a confirmatory test for CKD because the combination of creatinine and cystatin C provided more precise GFR estimates, although cystatin C should not replace creatinine in general practice [13]. Inker et al. [13] reported that the eGFRcr-cys equation was more precise, and Peralta et al. [12, 24, 25] found that cystatin C could confirm a high risk in people in whom the creatinine equation was used to estimate decreased GFR. F I G U R E 2 : The performance of three equations by GFR category. In our study, we found that the eGFRcys equation did not perform better; indeed, it had greater bias, lower accuracy and lower precision in both the total samples and in subgroups, compared with the CKD-EPI equation based on Scr. Additionally, cystatin C testing can increase laboratory costs. For these reasons, we suggest that cystatin C should not replace creatinine as a marker for evaluation and confirmation of high GFR. Similar to the study of Inker et al., our data suggest that estimated GFR based on cystatin C could be used as a confirmatory test for CKD because the eGFRcr-cys equation showed better precision. However, further improvements are needed for these equations in severe kidney disease. These results may be related to the variability of the mGFR and/or the markers (creatinine and cystatin) or steady state of the patients (influence of disease states leading to outliers). We know that mGFR computed from the clearance of injected exogenous markers is associated with little bias and usually is used as the gold standard for GFR. However, there may be occasions where mGFR may be imprecise because of limitations in the accurate measurement of urine volumes and times, exogenous markers concentration and incomplete bladder emptying. In addition, mGFR reflects a short-term clearance period, which differs from the person’s average GFR because of physiologic day-to-day and diurnal fluctuation. Furthermore, urinary clearance methods require both urine and blood samples, which may introduce imprecision because of random error in collection and measurement of samples. Imprecise mGFRs do not account for all errors in the estimation equations. Such remaining errors in eGFR equations may reflect random variation in non-GFR determinants of SCr and SCysC. In the present study, although the mGFR was measured only once, the procedure of GFR measurement and the marker measurement were standardized. So that the imprecision of the mGFR was minimized. The reason that we did not calibrate was that this will not change the trend of the estimation equation. We know the limitation of our study, so further study will be carried out. Two mGFR or three mGFR will be measured within the specified time. The average of them will be used as the gold standard [26]. This study has several strengths. First, to avoid or reduce the occurrence of systemic errors, Scr and s-CysC of all subjects were measured at PLA General Hospital and 44 randomly selected fresh-frozen plasma samples were sent to the Cleveland Clinic Laboratory to ensure equal calibration to the CKD-EPI study. Second, the study population was large, comprising 310 CKD patients at different stages and 307 healthy people, covered all age groups, and was gender-balanced. In particular, those 60 years of age or older accounted for more than a quarter of the population in this study, allowing for analysis of the elderly subgroup. The demographic data show that the proportion of elderly patients is high, as are the percentages of diabetic nephropathy and hypertensive nephropathy patients, thus the study subjects showed a wide range of conditions. Analyses could lead to differing applicabilities in terms of estimating GFR values for several reasons. First, differences in the reference method; in our study, we used dual blood sampling of 99mTc-DTPA as the reference standard, which was accepted as a standard GFR by the 21st International Annual Meeting of the Society of Nuclear Medicine Europe [14]. CKD-EPI collaborators for the development and internal validation of equations were restricted primarily to the urinary clearance of 125I-iothalamate [4, 13]. Thus, the two methods of calculating ‘standard’ GFR is one possible cause of systemic errors and discrepancies. Second, differences in ethnicity and muscle mass may be relevant. White populations generally have higher body weights, different fat distributions, more extracellular fluid and a greater BSA than Chinese populations [27, 28]; all of these factors may lead to differences. Third, although s-CysC levels are less affected by muscle mass and renal tubular secretion than Scr levels, they are apparently influenced by several factors, including age, gender, race and BMI [29, 30]. Indeed, one study demonstrated that s-CysC levels were 9% lower in females than in males, 6% higher in blacks than in whites and 9% lower in 40-year olds than in 20-year olds with the same eGFR [31]; these issues could all cause differences. This study also has several limitations. First, we used a single data set of 617 subjects with high GFRs (mean mGFR = 73.8 ± 37.6 mL/min/1.73 m2), which may not be representative of the general population. Second, further studies of larger populations with hypertensive nephropathy, diabetic nephropathy and malnutrition are needed to fully validate the performance of the CKD-EPI equation in multiple centers. In conclusion, in this study, we compared the applicability of the CKD-EPI equation with combined creatinine and cystatin C versus equations based on either of these markers alone in a Chinese population. We found that the eGFRcr equation and eGFRcr-cys equation showed a similar performance and were superior to the eGFRcys equation at higher GFR. The present study demonstrate that the selected three equations may be more suitable to evaluate GFR in normal and moderate kidney disease and further improvements are needed for these equations in severe kidney disease. This work was supported by the grants from the Major State Basic Research Development Program of China R E F E R E N C E S Appendix 1. The evaluated equations Scr-based CKD-EPI equation CysC-based CKD-EPI equation CysC-Scr-based CKD-EPI equation Appendix 2. The causes of CKD The causes of CKD Female SCr ≤0.7 mg/dL: GFR = 144 × (Scr/0.7)−0.329 × (0.993)Age × (1.159 if black) SCr >0.7 mg/dL: GFR = 144 × (Scr/0.7)−1.209 × (0.993)Age × (1.159 if black) Male SCr ≤0.9 mg/dL: GFR = 141 × (Scr/0.9)−0.411 × (0.993)Age × (1.159 if black) SCr >0.9 mg/dL: GFR = 141 × (Scr/0.9)−1.209 × (0.993)Age × (1.159 if black) Female or male CysC ≤0.8 133 × (Scys/0.8)−0 .499 × 0.996Age[ × 0.932 if female] Female or male CysC >0.8 133 × (Scys/0.8)−1 .328 × 0.996Age[ × 0.932 if female] Female SCr ≤0.7 CysC ≤0.8 130 × (Scr/0.7)−0.248 × (Scys/0.8)−0 .375 × 0.995Age [ × 1.08 if black] CysC >0.8 130 × (Scr/0.7)−0.248 × (Scys/0.8)−0 .711 × 0.995Age[ × 1.08 if black] Female SCr >0.7 CysC ≤0.8 130 × (Scr/0.7)−0.601 × (Scys/0.8)−0 .375 × 0.995Age[ × 1.08 if black] CysC >0.8 130 × (Scr/0.7)−0.601 × (Scys/0.8)−0 .711 × 0.995Age[ × 1.08 if black] Male SCr ≤0.9 CysC ≤0.8 135 × (Scr/0.9)−0.207 × (Scys/0.8)−0 .375 × 0.995Age[ × 1.08 if black] CysC >0.8 135 × (Scr/0.9)−0.207 × (Scys/0.8)−0 .711 × 0.995Age[ × 1.08 if black] Male SCr >0.9 CysC ≤0.8 135 × (Scr/0.9)−0.601 × (Scys/0.8)−0 .375 × 0.995Age [ × 1.08 if black] CysC >0.8 135 × (Scr/0.9)−0.601 × (Scys/0.8)−0 .711 × 0.995Age[ × 1.08 if black]


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Min Zhang, Yunshuang Chen, Li Tang, Jie Zhang, Shuwen Liu, Sihe Wang, Ribao Wei, Jianhui Zhou, Xueying Cao, Weiguang Zhang, Jinping Zhang, Yang Yang, Guangyan Cai, Xuefeng Sun, Xiangmei Chen. Applicability of Chronic Kidney Disease Epidemiology Collaboration equations in a Chinese population, Nephrology Dialysis Transplantation, 2014, 580-586, DOI: 10.1093/ndt/gft374