Article Assessing Glomerular Filtration Rate in...

Assessing Glomerular Filtration Rate in HospitalizedPatients: A Comparison Between CKD-EPI and FourCystatin C-Based EquationsAlfonso Segarra,* Judith de la Torre,* Natalia Ramos, * Augusto Quiroz,* Maria Garjau,* Irina Torres,*M. Antonia Azancot,* Montserrat Lopez,† and Ana Sobrado†

SummaryBackground and objectives A specific method is required for estimating glomerular filtration rate GFR inhospitalized patients. Our objective was to validate the Chronic Kidney Disease Epidemiology Collabora-tion (CKD-EPI) equation and four cystatin C (CysC)–based equations in this setting.

Design, setting, participants, & measurements This was an epidemiologic, cross-sectional study in a randomsample of hospitalized patients (n � 3114). We studied the accuracy of the CKD-EPI and four CysC-basedequations—based on (1) CysC alone or (2) adjusted by gender; (3) age, gender, and race; and (4) age, gen-der, race, and creatinine, respectively—compared with GFR measured by iohexol clearance (mGFR). Clini-cal, biochemical, and nutritional data were also collected.

Results The CysC equation 3 significantly overestimated the GFR (bias of 7.4 ml/min per 1.73 m2). Most ofthe error in creatinine-based equations was attributable to calculated muscle mass, which depended on pa-tient’s nutritional status. In patients without malnutrition or reduced body surface area, the CKD-EPI equa-tion adequately estimated GFR. Equations based on CysC gave more precise mGFR estimates when malnu-trition, extensive reduction of body surface area, or loss of muscle mass were present (biases of 1 and 1.3ml/min per 1.73 m2 for equations 2 and 4, respectively, versus 5.9 ml/min per 1.73 m2 for CKD-EPI).

Conclusions These results suggest that the use of equations based on CysC and gender, or CysC, age, gen-der, and race, is more appropriate in hospitalized patients to estimate GFR, since these equations are muchless dependent on patient’s nutritional status or muscle mass than the CKD-EPI equation.

Clin J Am Soc Nephrol 6: 2411–2420, 2011. doi: 10.2215/CJN.01150211

IntroductionHospitalized patients undergo intensive medical carethat often includes examinations using radiologiccontrast agents and/or potentially nephrotoxic drugs.A precise and reliable method for measuring the renalfunction is essential in this setting, characterized byan extremely heterogeneous population. The methodsusually employed in hospitalized patients are serumcreatinine and endogenous creatinine clearance.However, they both have important limitations (1–3).The production rate and tubular secretion of creati-nine may be substantially altered in hospitalized pa-tients due to associated comorbidities and/or drugtherapy. The endogenous creatinine clearance tendsto overestimate the glomerular filtration rate GFR asrenal insufficiency progresses and is subject to errordepending on samples collection method (1–3).

Estimation of GFR using equations based on serumcreatinine has been suggested as an alternative. Themost popular examples are Cockroft–Gault (CG) (4) andmodification of diet in renal disease (MDRD) (5) equa-

tions. Use of isotope dilution mass spectrometry (IDMS)traceable creatinine in these equations results in a moreaccurate estimated GFR (eGFR) (6). However, thesewere developed in patients with stable renal dysfunc-tion, and the Kidney Disease Outcomes Quality Initia-tive (KDOQI) guidelines recommend not using them inclinical circumstances particularly prevalent in hospital-ized patients, such as extremes of age and body size,severe malnutrition, or obesity (7). A recent study (8) inhospitalized patients with renal dysfunction concludedthat both CG and MDRD significantly overestimateGFR in this setting.

Recently, the Chronic Kidney Disease Epidemiol-ogy Collaboration has developed a new equation(CKD-EPI) based on serum creatinine, age, gender,and race (9). In comparison to the IDMS-MDRDequation, CKD-EPI has greater precision and reli-ability, especially for glomerular filtration rates�60 ml/min per 1.73 m2. However, to date, thisequation has not been evaluated in hospitalizedpatients.

*NephrologyDepartment and†Departamento deDocumentacion ClínicaHospital UniversitariVall d’Hebron,Barcelona, Spain

Correspondence: Dr.Alfons Segarra,NephrologyDepartment, HospitalUniversitari Valld’Hebron, Passeig de laVall d’Hebron, 119 to129. 08035 Barcelona,Spain. Phone: �34 93489 30 00; Fax: �3493 274 60 00; E-mail:[email protected]

www.cjasn.org Vol 6 October, 2011 Copyright © 2011 by the American Society of Nephrology 2411

Article

Cystatin C (CysC) is a 13-kD protein freely filtered by theglomerulus and completely reabsorbed by renal tubular ep-ithelial cells (10,11). For these reasons, several CysC-basedequations have been proposed (12,13). Although there aresome factors that influence CysC concentrations (14–16),CysC-based equations are less dependent on muscle mass(17), nutritional status (18), and age (16) than creatinine, sothey may be more precise in hospitalized patients.

The purpose of the study was to evaluate the performance(accuracy, bias, and precision) of the CKD-EPI equation and fourCysC-based equations compared with measured GFR (mGFR)in hospitalized patients with stable renal function.

Materials and MethodsStudy Subjects and Sampling

The study included patients who were admitted to the Valld’Hebron General Hospital between November 2008 andOctober 2009. Exclusion criteria were patients from criticalunits, transplant recipients, history of allergy to iodized con-trast agents, hemodialysis, patients whose anthropometricparameters could not be measured, and unstable renal func-tion (�25% increase or decrease in creatinine since admis-sion).

Patients were selected by simple random sampling. Allpatients were required to grant their informed consent inwriting, and procedures were performed according to theprinciples of the Declaration of Helsinki.

GFR Measurement and EstimationmGFR by iohexol clearance (19–21) was determined dur-

ing the first three days of admission using fasting plasmasampling and HPLC (20,21). The same samples were usedto measure creatinine levels by the Roche Lab “compen-sated” IDMS-traceable method (Hitachi Modular P-800Roche Diagnostics, Germany) and CysC by particle en-hanced immunonephelometry on a BN II system (DadeBehring Marburg GMBH, USA). eGFR was estimated fromcreatinine values by the CKD-EPI equation (9):

eGFR � 141 � min (SCr/k, 1)a � max (Scr/k, 1)�1.209 �0.993age � 1.018 (if female) � 1.159 (if black)

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

The three equations described by Stevens et al. (12) andGrubb’s equation (13) were used to estimate CysC-basedGFR:

(1) eGFR � 76.7 � CysC�1.19

(2) eGFR � 127.7 � CysC�1.17 � age�0.13 � (0.91 if female) �(1.06 if black)

(3) eGFR � 177.6 � SCr�0.65 � CysC�0.57 � age�0.20 �(0.82 if female) � (1.11 if black)

(4) eGFR � 87.62 � CysC�1.693 � (0.94 if female)

Variables and DefinitionsWe examined all medical records and interviewed pa-

tients to record age, gender, ethnic group, limb amputa-tions, diagnoses of muscle diseases with amyotrophy,chronic liver disease or cirrhosis, thyroid conditions, dia-betes, or known chronic kidney disease (CKD). We alsoperformed biochemical determinations and an anthropo-

metric evaluation (weight, height, arm and leg girth, tri-cipital fold, thigh-fold and girth, leg-fold and girth, andbody mass index[BMI]). Obesity was defined as BMI �27kg/m2 (22). Elmore’s equation (23) was used to diagnosemalnutrition. Total-body muscle mass was calculated byLee’s equation (24). The Child–Pugh classification wasused to determine liver disease (25). Patients were consid-ered to suffer from systemic inflammatory response syn-drome (SIRS) if they presented at least one of the follow-ing: temperature �38°C or �36°C; heart rate �90 beats/min; respiratory rate �20 breaths/min; or leukocyte count�12,000 or �4000 cells/mm3 (26). The diagnosis of inflam-matory abdominal disease included acute cholecystitis oracute pancreatitis of any etiology.

The criteria mentioned in the KDOQI guidelines (7) andin the consensus of the Spanish Society of Nephrology (27)were used to define subgroups who did not meet theconditions for using the MDRD equation (see Figure 1).

Statistical AnalysisTo determine whether the study sample represented all

hospitalized patients, we compared the main characteristicsof the cohort with those from the patients who were admittedduring the same period but not included in the study. Cate-gorical variables were compared by the Pearson chi-squaredor Fisher exact test as required. Continuous variables werecompared by t test or the Mann–Whitney U-test.

Univariate and stepwise multivariate linear regressionmodels were developed to determine the independent pre-dictors of calculated muscle mass, creatinine, and CysClevels (adjusting by mGFR in the latter two cases).

To validate eGFR equations, bias, precision, accuracy,and Pearson correlation coefficients with respect to mGFRby iohexol (considered as the standard reference and ex-pressed in standardized values per 1.73m2) were calcu-lated. Bias was defined as the mean of individual differ-ences between eGFR and mGFR. Precision was defined asthe SD of bias. Accuracy was evaluated by the percentagesof patients with eGFR within 30% and 50% of mGFR. Ttests for paired samples were used to assess differencesbetween eGFR and mGFR values. Validation of eGFRequations was performed in the overall sample and in eachsubgroup of patients who did not meet the criteria forusing creatinine-based equations.

Bland–Altman plots were made to analyze whether dif-ferences between eGFR and mGFR were related to themagnitude of GFR. In addition, to analyze the variablesstatistically associated to the differences from mGFR, weperformed four multivariate regression models in whichthe standardized residuals for each method were taken asdependent variables, and the following were taken as po-tential predictor variables: age, gender, malnutrition,chronic liver disease, inflammation, and thyroid disease.

A P � 0.05 was considered statistically significant. Med-Calc (MedCalc Software, Broekstraat 52, 9030 Mariakerke,Belgium) and SPSS 15.0 (SPSS, Inc., Headquarters, Chi-cago, IL) software were used for the analyses.

ResultsStudy Population

Figure 1 shows the selection process and the analyzedpopulations. A total of 3114 patients with varied diagno-

2412 Clinical Journal of the American Society of Nephrology

ses, admitted in 20 different services, fulfilled all selectioncriteria. Their mean age (SD) was 62.7 (19.1) years, and 55%were men. Median (Q1, Q3) hospital stay was 8 days (5,13), and mortality rate was 3.85% (120 patients died). Maincomorbidities were hypertension (31.1%), diabetes (14.9%),chronic obstructive pulmonary disease (10.9%), myocardi-opathy (10.0%), and chronic kidney disease (6.7%). Nosignificant differences were found in demographic and

clinical characteristics between included (n � 3114) andnonincluded patients (n � 22,840; data not shown).

Table 1 summarizes anthropometric and biochemicalvariables in both genders and in the total sample. Theprevalence of obesity was 45% (14% women 46% men) andthe prevalence of BMI �35 kg/m2 was 1.8%. Prevalence ofmalnutrition and SIRS were 49.9% and 28.0%, respectively.A poor correlation was observed between nutritional sta-

Figure 1. | Study population and analyzed subgroups.

Table 1. Description of the main biochemical and anthropometric characteristics in the overall study population and in gendersubgroups

Males (n � 1713)Mean � SD or n (%)

Females (n � 1401)Mean � SD or n (%)

Total (n � 3114)Mean � SD or n (%)

Hemoglobin, g/dl 10.8 � 1.9 10.7 � 2.0 10.9 � 2.1Creatinine, mg/dl 1.01 � 0.38 0.91 � 0.42 0.96 � 0.51Cystatin C, mg/dl 0.91 � 0.46 0.89 � 0.38 0.94 � 0.36Albumin, g/dl 3.45 � 0.56 3.54 � 0.71 3.38 � 0.6mGFR by iohexol, ml/min per 1.73 m2 90.3 � 36.3 84.2 � 27.7a 87.5 � 32.5

�30 26 (1.5) 16 (1.1) 42 (1.4)31–60 161 (9.4) 98 (6.9) 259 (8.3)61–90 709 (41.4) 657 (46.8) 1366 (43.9)91–120 674 (39.3) 517 (36.8) 1191 (38.2)�120 142 (8.3) 114 (8.1) 256 (8.2)

Transferrin, mg/dl 192.4 � 87.5 193.8 � 101.3 193.0 � 92.0Lymphocytes, /mm3 1496 � 1180 1478 � 993 1483 � 976Weight, kg 71.5 � 18.2 65.8 � 14.3b 68.9 � 15.8Height, cm 173.3 � 9.7 162.5 � 8.3b 167.1 � 8.6BMI, kg/m2 26.1 � 6.2 27.1 � 7.3a 26.8 � 7.6Calculated muscle mass, kg/1.73 m2 25.4 � 8.1 21.8 � 7.9b 23.7 � 6.9

BMI, body mass index; mGFR, measured GFR.aP � 0.05 versus males.bP � 0.01 versus males.

Clin J Am Soc Nephrol 6: 2411–2420, October, 2011 CKD-EPI Versus Cystatin C Equations in Hospitalized Patients, Segarra et al. 2413

tus according to BMI and Elmore’s equation (data notshown).

Independent Predictors of Muscle MassThe best model for predicting calculated muscle mass

(R2 � 0.42, P � 0.001) included age (� � �0.08, SE � 0.02,P � 0.002), gender (� � 2.18 for male versus female, SE �0.38, P � 0.001), BMI (� � 0.02, SE � 0.06, P � 0.004), andmalnutrition (� � �0.33, SE � 0.25, P � 0.02). Alone,neither serum albumin (R2 � 0.18) nor BMI (R2 � 0.24) weregood predictors of calculated muscle mass variability.

Independent Predictors of Serum Creatinine and CysCLevels

Table 2 summarizes the independent determinants ofcreatinine and CysC levels. The main determinants of cre-atinine were mGFR, total muscle mass, malnutrition, and

age. In simple linear regression analysis, calculated musclemass predicted 40.8% of the variability in creatinine (R2 �0.48, P � 0.0002).

In the case of CysC, most of the variability could beattributed to mGFR. Other variables such as BMI, gender,and inflammation were statistically significant but addedlittle further value to the model.

Measured GFRTable 1 shows the distribution of GFR categories, as

measured by iohexol clearance. Table 3 shows the preva-lence of mGFR �60 ml/min per 1.73 m2 by age and genderquartiles. The prevalence of mGFR �60 ml/min per 1.73m2 increased in proportion to age (P � 0.0003) and wassignificantly higher in men for each age quartile (Table 3).

Table 2. Independent predictors of serum levels of creatinine and cystatin C

(A) Creatinine

Variable Change (mg/dl) 95% Confidence Interval P-value

mGFR (per 5 ml/min per 1.73 m2) �11.90 �10.63 to �13.17 �0.001Age (per 10 years) �0.08 �0.04 to �0.12 0.001Calculated muscle mass (per 5 kg) 0.90 0.89 to 0.91 0.001Constant 15.33 2.90 to 27.75

Analysis of variance: F, 21.97; P � 0.0001; R2, 0.63.

(B) Cystatin C

Variable Change (mg/dl) 95% Confidence Interval P-value

mGFR (per 5 ml/min per 1.73 m2) �0.20 �1.96 to 1.56 �0.001Age (per 10 years) �0.05 �0.40 to 0.30 0.002BMI (per 1 kg/m2) �0.09 �0.08 to �0.10 0.04Gender (male vs female) 0.01 0.002 to 0.018 0.04SIRS (presence vs absence) 0.05 �0.20 to 0.30 0.02Diabetes (presence vs absence) 0.02 �0.17 to 0.21 0.04Constant 12.50 3.78 to 21.22

Analysis of variance: F, 23.25; P � 0.0001; R 2, 0.52. BMI, body mass index; mGFR, measured GFR; SIRS, systemic inflammatoryresponse syndrome.

Table 3. Prevalence of mGFR by iohexol <60 ml/min per 1.73 m2 by age and gender quartiles in the study population

Age QuartilesTotal

�50 51–65 66–80 �80

Femalesa mGFR �60 ml/min per 1.73 m2, n(%)*

14 (4.0) 24 (6.9) 36 (10.2) 40 (11.4) 114 (8.1)

Malesb mGFR �60 ml/min per 1.73 m2, n(%)*

28 (6.5) 39 (9.15) 55 (12.9) 65 (15.2) 187 (10.9)

Totalc mGFR �60 ml/min per 1.73 m2, n(%)*

42 (5.4) 63 (8.1) 91 (11.7) 105 (13.5) 301 (9.7)

mGFR, measured glomerular filtration rate.*Number and percentage of people in each particular age-gender subgroup who have mGFR values �60 ml/min per 1.73 m2.aDifferences between age categories in females, �2 � 19.05, P � 0.0001.bDifferences between age categories in males, �2 � 17.09, P � 0.0001.cDifferences between males and females, �2 � 6.56, P � 0.01.


Table 4. Glomerular filtration rate estimations according to the five analyzed equations and validation parameters using mGFR byiohexol as reference

EquationMean � SD

ml/minper 1.73 m2

Bias Precision P-valueaAccuracy

within30% (%)b

Accuracywithin

50% (%)bR

All (n � 3114)mGFR 87.5 � 32.5CKD-EPI 89.8 � 38.9 1.8 23.1 0.124 72 88 0.84CysC 1 90 � 39.1 2.5 14.1 0.167 80 87 0.90CysC 2 89.4 � 40.2 1.9 11.3 0.063 82 89 0.91CysC 3 (�Cr) 94.9 � 37.4 7.4 15.7 0.006 66 78 0.79CysC 4 89.2 � 41.2 1.7 18.4 0.413 84 89 0.93Malnourished patients (n � 1555)mGFR 76.3 � 26.1CKD-EPI 81.2 � 33.4 5.9 12.6 0.035 70 83 0.77CysC 1 76.9 � 28.7 0.6 13.2 0.154 78 86 0.88CysC 2 77.3 � 22.5 1.0 11.7 0.432 85 92 0.91CysC 3 (�Cr) 84.1 � 29.8 7.8 13.1 0.001 58 64 0.73CysC 4 77.6 � 31.2 1.3 14.3 0.165 86 91 0.92All excluding patients with malnutrition or amputations �25% body surface area (n � 1673)mGFR 86.6 � 31.3CKD-EPI 85.7 � 28.2 �0.6 11.4 0.368 84 98 0.84CysC1 87.9 � 27.4 1.3 13.6 0.198 82 98 0.85CysC 2 86.5 � 28.6 �0.1 14.8 0.547 84 98 0.84CysC 3 (�Cr) 85.3 � 30.1 �1.3 10.7 0.483 77 97 0.86CysC 4 87.8 � 28.6 1.2 8.6 0.471 85 98 0.89mGFR �60 ml/min per 1.73 m2 (n � 2813)mGFR 98 � 28.1CKD-EPI 96.5 � 30.3 �1.5 23.9 0.224 82 91 0.88CysC 1 97.8 � 36.3 �0.2 12.8 0.541 81 91 0.87CysC 2 96.9 � 28.7 �1.1 14.1 0.341 84 92 0.84CysC 3 (�Cr) 103.2 � 31.9 5.2 17.6 0.003 69 81 0.71CysC 4 98.1 � 29.6 0.1 11.8 0.598 89 97 0.89GFR �60 ml/min per 1.73 m2 excluding patients with moderate or severe malnutrition or amputation �25% body

surface area (n � 2248)mGFR 104 � 36.1CKD-EPI 104 � 39.2 0.4 22.3 0.312 86 95 0.91CysC 1 101 � 36.3 �3.0 11.8 0.131 82 92 0.91CysC 2 106 � 28.7 2.0 12.3 0.410 83 94 0.93CysC 3 (�Cr) 103.2 � 31.9 �0.8 11.8 0.517 81 90 0.92CysC 4 107 � 29.6 3.0 7.8 0.298 90 97 0.92Age �70 years (n � 1307)mGFR 69.6 � 16.4CKD-EPI 70.3 � 19.8 2.7 19.2 0.356 78 89 0.88CysC 1 65.6 � 29.2 �4.0 25.2 0.004 69 74 0.82CysC 2 68.7 � 30 �0.9 11.1 0.132 79 93 0.87CysC 3 (�Cr) 74.6 � 33.5 5.1 12.3 0.046 61 68 0.84CysC 4 70.2 � 24.6 0.6 8.7 0.554 76 91 0.97Age �70 years excluding patients with moderate or severe malnutrition or amputation �25% body surface area

(n � 850)mGFR 68.1 � 21.5CKD-EPI 68.6 � 27.2 �0.5 24.1 0.221 84 95 0.82CysC 1 61.3 � 19.2 �6.8 34.2 0.0001 82 94 0.83CysC 2 70.2 � 24.3 2.1 21.3 0.214 79 95 0.85CysC 3 (�Cr) 70.6 � 26.8 2.5 18.3 0.056 73 92 0.86CysC 4 68.8 � 19.4 0.7 13.2 0.221 74 91 0.93Child Class C Liver Disease or cirrhosis of the liver (n � 63)mGFR 89.1 � 41.3CKD-EPI 95.3 � 29.5 4.2 28.6 0.037 77 81 0.78CysC 1 89.3 � 21.2 0.1 27.2 0.342 80 91 0.82


Estimated GFRTable 4 shows performance of the five equations for

estimating GFR in each of the analyzed populations. In theoverall sample, the CysC 3 equation overestimated mGFR.The bias in CKD-EPI and equations 1, 2, and 4 was signif-icantly lower and gave more precise results.

Figure 2 shows the error in the eGFR depending on themean of eGFR and mGFR. The accuracy of the CKD-EPIequation in the overall sample decreased with increasingGFR. CysC equations 2 and 4 gave more precise results inGFR range of 15 to 100 ml/min per 1.73 m2, but overesti-mated mGFR for values �100 ml/min per 1.73 m2.

The main predictors of error are depicted in Table 5.Neither the presence of SIRS at admission nor diabetesmellitus or thyroid disease showed significance.

Subgroup AnalysesIn patients with malnutrition criteria and reduced

muscle surface or mass, CysC equations provided moreaccurate and precise eGFR estimates than the CKD-EPIequation, although CysC equation 3 was significantlyless precise than the others (Table 4).

The accuracy of the CKD-EPI equation after excludingpatients with malnutrition and/or amputation of �25% ofbody surface area (BSA) was significantly increased, re-maining approximately constant in the range from 15 to100 ml/min per 1.73 m2 (data not shown).

In the subpopulation with mGFR �60 ml/min per 1.73 m2,CysC equation 3 overestimated mGFR, whereas CKD-EPIand CysC equations 1, 2, and 4 had lower bias (Table 4).

CysC equation 1 underestimated mGFR in patients aged�70 yr. The accuracy of the CKD-EPI equation remainedconstant in the subgroup of patients aged �70 years (Table 4).

In patients with cirrhosis of the liver, both the CKD-EPIequation and CysC equation 3 overestimated mGFR. Thebias in equations 1, 2, and 4 was significantly lower. Totalmuscle mass was the main source of eGFR errors in cir-rhotic patients (R2 0.38, P � 0.0015), after adjustment forage and gender.

In patients with a BMI �35 kg/m2, CysC equation 3significantly overestimated, whereas CKD-EPI and CysCequations 1, 2, and 4 underestimated mGFR (Table 4).

DiscussionThe present study analyzes the accuracy of the CKD-EPI

equation and of four equations based on CysC, either as asingle variable or after adjustment for demographic char-acteristics and creatinine level, for estimating GFR in alarge and representative sample of hospitalized patientswith stable creatinine. The study population showed highprevalences of malnutrition, obesity, and SIRS, similar tothose described previously (28–34), confirming the needfor validating eGFR equations in this setting.

When we analyzed the main sources of error for CKD-EPIequation, we observed that total muscle mass and serum creat-inine were the most powerful predictors. Total muscle mass wassignificantly conditioned by patients’ nutritional status, afteradjusting for age, gender, and BMI. These data suggest that theoverestimation was a consequence of the high prevalence of

Table 4. (Continued)

EquationMean � SD

ml/minper 1.73 m2

Bias Precision P-valueaAccuracy

within30% (%)b

Accuracywithin

50% (%)bR

CysC 2 88.4 � 25.6 �0.7 31.3 0.214 79 92 0.83CysC 3 (�Cr) 97.8 � 27.3 8.7 36.5 0.012 76 84 0.74CysC 4 88.8 � 29.5 �0.3 23.6 0.363 79 91 0.88BMI �35 kg/m2 (n � 56)mGFR 108.9 � 46.8CKD-EPI 86.5 � 24.3 �22.4 31.8 0.000 60 71 0.66CysC 1 88.9 � 31.6 �20.0 38.1 0.000 58 66 0.63CysC 2 87.9 � 32.4 �21.0 29.7 0.000 54 68 0.65CysC 3 (�Cr) 85.8 � 28.9 23.1 33.2 0.000 56 70 0.68CysC 4 87.3 � 36.2 �21.6 40.2 0.000 55 67 0.62

Results are presented for the general hospitalized patients and for each subgroup of patients in which, according to KidneyDisease Outcomes Quality Initiative guidelines, creatinine-based equations cannot be used to estimate GFR. BMI, body massindex; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration equation; CysC, cystatin C; mGFR, measured GFR; R,Pearson correlation coefficient; eGFR, estimated GFR.aP-value for t tests between eGFR and mGFR values.b% patients with eGFR within 30% or 50% of mGFR.Equations tested:CKD-EPI: eGFR � 141 � min (SCr/k, 1)a � max (Scr/k, 1)�1.209 � 0.993age � 1.018 �if female � 1.159 �if black�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 ofScr/k or 1, and max indicates the maximum of Scr/k or 1.CysC 1: eGFR � 76.7 � CysC�1.19

CysC 2: eGFR � 127.7 � CysC�1.17 � age�0.13 � (0.91 if female) � (1.06 if black)CysC 3: eGFR � 177.6 � SCr�0.65 � CysC�0.57 � age�0.20 � (0.82 if female) � (1.11 if black)CysC 4: eGFR � 87.62 � CysC�1.693 � (0.94 if female)


malnutrition among hospitalized patients. Proof of this is theincreased accuracy of the CKD-EPI equation when patients withmalnutrition or amputations �25% of BSA were excluded fromthe analysis. In these conditions, the CKD-EPI equation gave aprecise estimate in the 15 to 100 ml/min per 1.73 m2 range (forvalues above 100 ml/min per 1.73 m2, it significantly underes-timated mGFR).

Regarding applicability of CKD-EPI in patients over 70years of age (42% in our study), our study identified reducedmuscle mass and BSA as the main sources of error, andshowed little effect of age. The accuracy in patients aged �70years with no malnutrition or reduced BSA was good, whichsuggests that age alone is not a factor limiting its use.

With regard to CysC-based equations, our data showthat CysC equations 1, 2, and 4 give more precise resultsthan the CysC equation 3 in general hospitalized patients,and that they do not systematically overestimate mGFR for

values between 15 to 100 ml/min per 1.73 m2. As in otherstudies (15–16), we found that CysC levels were signifi-cantly associated with age, inflammation, and BMI, butthese variables had little impact on eGFR differences.Equation 1 performed differently in elderly patients, lead-ing to an underestimation of mGFR.

Cys C equations 2 and 4 gave comparable GFR valuesfor mGFR values of 100 ml/min per 1.73 m2 or lower. Theaccuracy and precision of CysC equation 3 in our studydiffered significantly from those reported in nonhospital-ized patients with CKD (12) This is probably due to the factthat it takes into account serum creatinine, and musclemass was the main source of error in our sample.

In patients with malnutrition criteria and reducedmuscle mass or BSA, all of the CysC equations providedmore accurate and precise eGFR than the CKD-EPI equa-tion. The impact of BMI on differences for the CysC

Figure 2. | Distribution of differences for each analyzed equation with respect to mean of measured GFR (mGFR) and estimated GFR(eGFR) values. The Y-axes display differences between “estimated” minus “measured” GFR values, to obtain positive values in case ofoverestimation.


equations was opposite than that found for the CKD-EPIequation. This difference may be explained by the cor-relation described between CysC levels and body fatmass (16).

The activation of inflammatory response may alter muscu-lar creatinine production and increase CysC levels (16,35),

which could, theoretically, influence both CKD-EPI and CysCequations. However, in our study, the presence of SIRS atadmission did not predict error in eGFR. This could be due tothe exclusion of patients admitted to critical units and pa-tients with unstable renal function, who tend to have moresevere inflammatory responses (34,36).

Table 5. Independent predictors of difference with respect to measured GFR for each equation estimating GFR in the studypopulation

(A) CKD-EPI Equation

Variable Difference (ml/min per 1.73 m2) 95% Confidence Interval P-value

BMI �35 kg/m2 (versus �35 kg/m2) 0.26 0.24 to 0.28 0.002Calculated muscle mass (per 5 kg) �0.30 �0.40 to �0.20 �0.0001Measured GFR �90 ml/min per 1.73 m2

(versus �90 ml/min per 1.73 m2)�0.43 �0.53 to �0.33 0.004

Constant 29.2 18.75 to 39.65


(B) Cystatin C Equation 1


Age (per 10 years) �3.5 �4.87 to �2.13 0.001BMI (per 1 kg/m2) �0.02 �0.08 to 0.04 0.001Measured GFR �100 ml/min per 1.73

m2 (versus �100 ml/min per 1.73 m2)�0.51 �1.35 to 0.33 0.001

Constant 34.69 21.54 to 47.84


(C) Cystatin C Equation 2


BMI (per 1 kg/m2) �0.06 �0.24 to 0.12 0.001Measured GFR �100 ml/min per 1.73

m2 (versus �100 ml/min per 1.73 m2)�0.43 �0.86 to 0.001 0.001

Constant 16.15 0.55 to 31.75

Analysis of the variance: F, 16.77; P � 0.001; R 2, 0.16.

(D) Cystatin C Equation 3


Calculated muscle mass (per 5 kg) �0.95 �1.06 to �0.84 0.001BMI (per 1 kg/m2) �0.07 �0.10 to �0.03 0.01Measured GFR �100 ml/min per 1.73

m2 (versus �100 ml/min per 1.73 m2)�0.18 �0.24 to �0.12 0.001

Constant 17.33 �0.21 to 34.87

Analysis of variance: F, 13.27; P � 0.0001; R2, 0.27

(E) Cystatin C Equation 4


BMI (per 1 kg/m2) �0.09 �0.13 to �0.05 0.001Measured GFR �100 ml/min per 1.73

m2 (versus �100 ml/min per 1.73 m2)�0.26 �0.51 to �0.005 0.001

Constant 7.33 �2.37 to 17.03

Analysis of variance: F, 11.32; P � 0.0001; R2, 0.14. BMI, body mass index.


In patients with liver cirrhosis or Child class C liverdisease, low creatinine production secondary to reducedmuscle mass implies systematic overestimation of GFRwith both creatinine-based equations and creatinine clear-ance (37–40). Some studies have highlighted that CysC inthese patients could provide a more precise estimate (41–44). However, results are inconsistent. In our study, theCKD-EPI equation systematically overestimated mGFR inpatients with liver cirrhosis, and the error was associatedwith muscle mass. This suggests that cirrhotic patients arecomparable to patients with malnutrition due to otherfactors.

On the whole, our data suggest that CysC equations 2and 4 might be more appropriate than the CKD-EPI forassessing renal function in hospitalized patients. However,cystatin-based methods are much more expensive thancreatinine-based methods. Therefore, the clinical context inwhich they should be used needs to be defined in detail.Our data show that, in patients with stable kidney func-tion, mGFR �100 ml/min per 1.73 m2, and no malnutritionor reduced BSA, the CKD-EPI equation adequately esti-mates GFR in all age groups. Detection of malnutrition andmoderate loss of muscle mass is not easy without trainedpersonnel and a huge investment in time. Therefore, sys-tematic determination in all hospitalized patients is notpossible. According to our data and to previous studies(8,45), the use of creatinine-based equations requires cor-rection by muscle mass variations, particularly in hospital-ized patients whose muscle mass depends heavily on theirnutritional status. Rule et al. (45) showed that equationsthat include age and gender show a higher prevalence ofrenal disease than equations that only include musclemass. It has been suggested that the six-variable MDRDequation (8) could be adapted to the hospitalized popula-tion, giving more weight to albumin. In our patients, how-ever, although albumin was significantly related to thepresence of malnutrition, its relation to muscle mass wasquantitatively weak. These findings also apply to BMI.

The present study has some limitations. The exclusion ofpatients whose clinical status prevented the measurementof anthropometric variables could have led to an underes-timation of malnutrition. No interviews on diet were con-ducted to determine daily protein intake, and this variablecould not be included in the multivariate analysis for pre-dicting creatinine. Finally, only one method was used todetermine CysC, and important variations between meth-ods have been reported (46).

In view of our findings, we conclude that, since the perfor-mance of the CKD-EPI equation is highly dependable on thepresence of malnutrition or muscle mass loss, and measuringthese variables is difficult in the clinical practice, the use of anequation based on CysC and gender, or on CysC, age, gender,and race, could be more appropriate to estimate GFR inhospitalized patients with reduced body surface area or inwhom malnutrition is suspected.

AcknowledgmentsOur acknowledgments to Montserrat Hervas, Helena Angulo,

Maria Teresa Arraiz, and Mar Aguilar, the nurses who conductedthe anthropometric studies and interviewed the patients.

We thank Dr. Ximena Alvira from Health Co SL (Madrid, Spain)

and Dr. Neus Valveny from Trial Form Support SL (Barcelona,Spain) for assistance in the preparation of the manuscript. Thisstudy was supported by a grant from Amgen, S.A.

DisclosuresWriting assistance was supported by Amgen S.A., but this com-

pany did not participate in the study design, data collection, oranalyses. The authors declare no other conflict of interest.

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Received: February 7, 2011 Accepted: June 20, 2011

Published online ahead of print. Publication date available atwww.cjasn.org.

Access to UpToDate online is available for additional clinicalinformation at www.cjasn.org.


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