Luis-Lima, Sergio; Escamilla-Cabrera, Beatriz; Negrín-Mena, Natalia; Estupiñán, Sara; Delgado-Mallén, Patricia; Marrero-Miranda, Domingo; González-Rinne, Ana; Miquel-Rodríguez, Rosa; Cobo-Caso, María Ángeles; Hernández-Guerra, Manuel; Oramas, Juana; Batista, Norberto; Aldea-Perona, Ana; Jorge-Pérez, Pablo; González-Alayón, Carlos; Moreno-Sanfiel, Miguel; González-Rodríguez, Juan Antonio; Henríquez, Laura; Alonso-Pescoso, Raquel; Díaz-Martín, Laura; González-Rinne, Federico; Lavín-Gómez, Bernardo Alio; Galindo-Hernández, Judith; Sánchez-Gallego, Macarena; González-Delgado, Alejandra; Jiménez-Sosa, Alejandro; Torres, Armando; Porrini, Esteban

Nephrology Dialysis Transplantation, Volume Advance Article – May 11, 2018

8 pages

/lp/ou_press/ckd-staging-with-cystatin-c-or-creatinine-based-formulas-flipping-the-O7WlFiSWez

- Publisher
- Oxford University Press
- Copyright
- © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
- ISSN
- 0931-0509
- eISSN
- 1460-2385
- D.O.I.
- 10.1093/ndt/gfy086
- Publisher site
- See Article on Publisher Site

Abstract Background Chronic kidney disease (CKD) affects 10–13% of the population worldwide. CKD classification stratifies patients in five stages of risk for progressive renal disease based on estimated glomerular filtration rate (eGFR) by formulas and albuminuria. However, the reliability of formulas to reflect real renal function is a matter of debate. The effect of the error of formulas in the CKD classification is unclear, particularly for cystatin C–based equations. Methods We evaluated the reliability of a large number of cystatin C and/or creatinine-based formulas in the definition of the stages of CKD in 882 subjects with different clinical situations over a wide range of glomerular filtration rates (GFRs) (4.2–173.7 mL/min). Results Misclassification was a constant for all 61 formulas evaluated and averaged 50% for creatinine-based and 35% for cystatin C–based equations. Most of the cases were misclassified as one stage higher or lower. However, in 10% of the subjects, one stage was skipped and patients were classified two stages above or below their real stage. No clinically relevant improvement was observed with cystatin C–based formulas compared with those based on creatinine. Conclusions The error in the classification of CKD stages by formulas was extremely common. Our study questions the reliability of both cystatin C and creatinine-based formulas to correctly classify CKD stages. Thus the correct classification of CKD stages based on estimated GFR is a matter of chance. This is a strong limitation in evaluating the severity of renal disease, the risk for progression and the evolution of renal dysfunction over time. CKD staging, creatinine, cystatin C, estimated GFR, measured GFR INTRODUCTION The prevalence of chronic kidney disease (CKD) has reached epidemic proportions, affecting ∼10% of the population worldwide [1]. CKD is a risk factor for end-stage renal disease, cardiovascular morbidity, premature death and low quality of life [2]. These adverse outcomes could be prevented by early diagnosis and treatment of renal disease [3]. In 2002, a classification of CKD was proposed by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative [4]. Accordingly, patients with kidney damage are stratified in subgroups of glomerular filtration rate (GFR): CKD-1, >90 mL/min/1.73 m2; CKD-2, 60–89 mL/min/1.73 m2; CKD-3, 30–59 mL/min/1.73 m2; CKD-4, 15–29 mL/min/1.73 m2 and CKD-5, < 15 mL/min/1.73 m2. In 2012, this classification was modified [5], combining albuminuria levels with the stages of CKD to establish a risk score for progressive renal disease. The CKD classification was developed to provide a practical guideline for the diagnosis of renal dysfunction in order to reduce the risks associated with CKD and to prevent disease progression. However, a limitation of the CKD classification is the use of estimated GFR (eGFR) by formulas. Formulas are neither accurate nor precise in reflecting true GFR [6–22], which may lead to over- or underestimation of real GFR and misclassification of patients in CKD stages. Nevertheless, the impact of the error of eGFR in the classification of CKD stages has seldom been evaluated [6–8, 10, 11, 16, 19, 20]. These studies observed that between one-third and one-half of the patients were misclassified in CKD stages. These studies evaluated few formulas, mostly creatinine based, and little evidence is available about cystatin C–based equations in the classification of CKD stages. The present study aimed to evaluate the reliability of a large number of cystatin C and/or creatinine-based formulas in the definition of CKD stages in patients with different clinical situations over a wide range of GFRs. MATERIALS AND METHODS Patients In our centre we perform plasma clearance of iohexol in clinical practice and research. For this study we selected consecutive patients >18 years of age who underwent plasma clearance of iohexol from July 2013 to October 2017 with different clinical conditions: renal transplantation, diabetic nephropathy, pre-dialysis, autosomal dominant polycystic kidney disease (ADPKD), glomerulonephritis, interstitial nephritis, nephroangioesclerosis, cancer patients on chemotherapy, heart failure, cirrhosis, liver transplantation and living kidney donors, among others (Table 1). We created a meta-database of 882 individuals and merged the necessary variables for analysis. Table 1 Clinical characteristics of the patients included in the study N 882 Age (years) 57.4 ± 14.2 Gender (male), n (%) 611 (69.3) No renal disease, n (%) Healthy subjects 50 (5.5) Living kidney donors 89 (9.8) Renal disease, n (%) Kidney transplantation 255 (28.1) Diabetic nephropathy 125 (13.8) Glomerulonephritis 45 (5.0) Interstitial nephritis 45 (5.0) Nephroangioesclerosis 43 (4.7) CKD 38 (4.2) ADPKD 26 (2.9) Primary hyperoxaluria 5 (0.6) Other clinical conditions, n (%) Heart failure 54 (5.9) Cirrhosis 44 (4.8) Liver transplantation 36 (4.0) Oncology 14 (1.5) Other 12 (1.3) Unknown 27 (3.0) Measured GFR (mL/min), mean ± SD 61.0 ± 32.2 Measured GFR range (mL/min) 4.2–173.7 CKD stage (mL/min), n (%) 1 (>90) 186 (20.5) 2 (60–90) 258 (28.4) 3 (30–60) 255 (28.1) 4 (15–30) 181 (19.9) 5 (<15) 28 (3.1) Height (m) 1.68 ± 0.09 Weight (kg) 81.4 ± 17.5 Body mass index (kg/m2), mean ± SD 28. 8 ± 5.5 Body surface area (m2), mean ± SD 1.88 ± 0.31 Serum creatinine (mg/dL), mean ± SD 1.68 ± 1.12 Serum cystatin C (g/dL), mean ± SD 1.75 ± 0.72 24-h creatinine clearance (mL/min), median (IQR) 65.7 (33.2–89.2) 24-h proteinuria (mg/24 h), median (IQR) 765.9 (113.3–650.5) N 882 Age (years) 57.4 ± 14.2 Gender (male), n (%) 611 (69.3) No renal disease, n (%) Healthy subjects 50 (5.5) Living kidney donors 89 (9.8) Renal disease, n (%) Kidney transplantation 255 (28.1) Diabetic nephropathy 125 (13.8) Glomerulonephritis 45 (5.0) Interstitial nephritis 45 (5.0) Nephroangioesclerosis 43 (4.7) CKD 38 (4.2) ADPKD 26 (2.9) Primary hyperoxaluria 5 (0.6) Other clinical conditions, n (%) Heart failure 54 (5.9) Cirrhosis 44 (4.8) Liver transplantation 36 (4.0) Oncology 14 (1.5) Other 12 (1.3) Unknown 27 (3.0) Measured GFR (mL/min), mean ± SD 61.0 ± 32.2 Measured GFR range (mL/min) 4.2–173.7 CKD stage (mL/min), n (%) 1 (>90) 186 (20.5) 2 (60–90) 258 (28.4) 3 (30–60) 255 (28.1) 4 (15–30) 181 (19.9) 5 (<15) 28 (3.1) Height (m) 1.68 ± 0.09 Weight (kg) 81.4 ± 17.5 Body mass index (kg/m2), mean ± SD 28. 8 ± 5.5 Body surface area (m2), mean ± SD 1.88 ± 0.31 Serum creatinine (mg/dL), mean ± SD 1.68 ± 1.12 Serum cystatin C (g/dL), mean ± SD 1.75 ± 0.72 24-h creatinine clearance (mL/min), median (IQR) 65.7 (33.2–89.2) 24-h proteinuria (mg/24 h), median (IQR) 765.9 (113.3–650.5) IQR, interquartile range. Table 1 Clinical characteristics of the patients included in the study N 882 Age (years) 57.4 ± 14.2 Gender (male), n (%) 611 (69.3) No renal disease, n (%) Healthy subjects 50 (5.5) Living kidney donors 89 (9.8) Renal disease, n (%) Kidney transplantation 255 (28.1) Diabetic nephropathy 125 (13.8) Glomerulonephritis 45 (5.0) Interstitial nephritis 45 (5.0) Nephroangioesclerosis 43 (4.7) CKD 38 (4.2) ADPKD 26 (2.9) Primary hyperoxaluria 5 (0.6) Other clinical conditions, n (%) Heart failure 54 (5.9) Cirrhosis 44 (4.8) Liver transplantation 36 (4.0) Oncology 14 (1.5) Other 12 (1.3) Unknown 27 (3.0) Measured GFR (mL/min), mean ± SD 61.0 ± 32.2 Measured GFR range (mL/min) 4.2–173.7 CKD stage (mL/min), n (%) 1 (>90) 186 (20.5) 2 (60–90) 258 (28.4) 3 (30–60) 255 (28.1) 4 (15–30) 181 (19.9) 5 (<15) 28 (3.1) Height (m) 1.68 ± 0.09 Weight (kg) 81.4 ± 17.5 Body mass index (kg/m2), mean ± SD 28. 8 ± 5.5 Body surface area (m2), mean ± SD 1.88 ± 0.31 Serum creatinine (mg/dL), mean ± SD 1.68 ± 1.12 Serum cystatin C (g/dL), mean ± SD 1.75 ± 0.72 24-h creatinine clearance (mL/min), median (IQR) 65.7 (33.2–89.2) 24-h proteinuria (mg/24 h), median (IQR) 765.9 (113.3–650.5) N 882 Age (years) 57.4 ± 14.2 Gender (male), n (%) 611 (69.3) No renal disease, n (%) Healthy subjects 50 (5.5) Living kidney donors 89 (9.8) Renal disease, n (%) Kidney transplantation 255 (28.1) Diabetic nephropathy 125 (13.8) Glomerulonephritis 45 (5.0) Interstitial nephritis 45 (5.0) Nephroangioesclerosis 43 (4.7) CKD 38 (4.2) ADPKD 26 (2.9) Primary hyperoxaluria 5 (0.6) Other clinical conditions, n (%) Heart failure 54 (5.9) Cirrhosis 44 (4.8) Liver transplantation 36 (4.0) Oncology 14 (1.5) Other 12 (1.3) Unknown 27 (3.0) Measured GFR (mL/min), mean ± SD 61.0 ± 32.2 Measured GFR range (mL/min) 4.2–173.7 CKD stage (mL/min), n (%) 1 (>90) 186 (20.5) 2 (60–90) 258 (28.4) 3 (30–60) 255 (28.1) 4 (15–30) 181 (19.9) 5 (<15) 28 (3.1) Height (m) 1.68 ± 0.09 Weight (kg) 81.4 ± 17.5 Body mass index (kg/m2), mean ± SD 28. 8 ± 5.5 Body surface area (m2), mean ± SD 1.88 ± 0.31 Serum creatinine (mg/dL), mean ± SD 1.68 ± 1.12 Serum cystatin C (g/dL), mean ± SD 1.75 ± 0.72 24-h creatinine clearance (mL/min), median (IQR) 65.7 (33.2–89.2) 24-h proteinuria (mg/24 h), median (IQR) 765.9 (113.3–650.5) IQR, interquartile range. Measured GFR Briefly, the morning of the study, 5 mL of iohexol (Omnipaque 300, GE Healthcare, Cardiff, UK) was injected intravenously for 2 min. Afterwards, venous or capillary blood were obtained by finger prick at 120, 180, 240, 300, 360, 420 and 480 min for patients with eGFR ≤40 mL/min/1.73 m2 or at 120, 150, 180, 210 and 240 min for those with eGFR >40 mL/min/1.73 m2. Iohexol was measured in plasma or dried blood spot (DBS) as previously shown [16, 23]. Both methods using plasma or DBS showed excellent agreement and can be considered interchangeable [23]. Iohexol levels were measured by high-performance liquid chromatography (HPLC), as previously decribed by Krutzén et al. [24] and Niculescu-Duvaz et al. [25], with some modifications [23]. For the DBS analysis, a fixed volume of capillary blood (10 µL) was taken by a capillary pipette and deposited on a filter paper. A circle of filter paper containing the whole drop of blood was then punched out for analysis [23]. Plasma clearance of iohexol was calculated according to a one-compartment model and then corrected by the formula proposed by Bröchner-Mortensen [26]. eGFR by formulas Simultaneously with the plasma clearance of iohexol, serum creatinine and cystatin C were determined by calculating 65 equations: 39 creatinine-based, 20 cystatin-C based, and 6 that use both markers (Supplementary data, Table S1). The agreement between formulas and measured GFR (mGFR) was evaluated with the formulas unadjusted for body surface area (BSA). When eGFR was adjusted, we reversed the adjustment of the result by applying the following formula (GFR adjusted = GFR unadjusted/BSA × 1.73). BSA was calculated by the Du-Bois and Du-Bois formula [27]. Biochemistry Creatinine was measured by isotope dilution mass spectrometry–traceable creatinine (cobas c711 module, Roche Diagnostics, Basel, Switzerland) and cystatin C levels by immunonephelometry (BN II System, Siemens Healthcare Diagnostics, Erlangen, Germany), calibrated with ERM-DA471/IFCC. Classification in CKD stages The patients were classified in CKD stages using mGFR and eGFR with 65 formulas (Supplementary data, Table S1). We used the CKD classification of 2002 [4], which does not divide Stage 3 into 3A and 3B, in order to avoid the creation of two additional subgroups with a narrow range of GFRs (15 mL/min). However, we determined the variability of eGFR around the cut-off value of 45 mL/min (Supplementary data, Figure S1A). The agreement between the classification in CKD stages based on eGFR and mGFR was analysed as follows: for each stage we analysed (i) ‘true positive cases’: the subjects correctly classified by a formula in the corresponding stage defined by mGFR; (ii) ‘false positive cases’: subjects who do not belong to the stage of CKD based on mGFR but were incorrectly classified as such by a formula. Both true positive and false positive cases represent 100% of the cases defined by a formula in a given stage of CKD. Finally we analysed the (iii) ‘missing cases’: the subjects that belong to a given stage defined by mGFR but classified in a higher or lower stage by a formula. For example, for CKD-2, mGFR classified 250 patients and 280 by a formula, then 120 were correctly classified by the formula (true positive) and 160 were incorrectly classified in this stage (false positives). Thus true positives + false positives = the number of patients classified by the formula (120 +160 = 280). Finally, the formula did not define in this group 130 subjects that belong to CKD-2 and were classified in different stages, who represent the missing cases. Therefore true positives + missing = the number of patients classified by mGFR (120 +130 = 250). Finally, we evaluated the number of cases in which one stage was skipped and patients were not classified in a consecutive stage but in an even higher or lower subgroup (Supplementary data, Table S3). Sensitivity analyses Since some formulas were developed in specific populations, such as obesity (Salazar-Corcoran), renal transplantation (Nankivell-A and B) or elderly subjects (BIS-1 and 2), we compared the agreement of these equations with mGFR in patients with and without these conditions. Also, to evaluate whether formulas perform better in some specific clinical conditions, such as type 2 diabetes, renal transplantation and chronic nephropaties, we compared the agreement between mGFR and a representative number of formulas in these three subgroups of patients. Statistical analysis Agreement between eGFR and mGFR The performance of eGFR in reflecting mGFR was assessed by statistics of agreement for continuous data, including the concordance correlation coefficient (CCC), total deviation index (TDI) and coverage probability (CP) [28–30]. The CCC varies from 0 to 1 and combines meaningful components of accuracy and precision. A CCC >0.90 reflects optimal concordance between measurements. The TDI captures a large proportion of data within a boundary for allowed differences between two measurements. Empirical TDI was calculated for a theoretical TDI of 10% and a CP of 90%. According to this level of TDI, we defined a priori that the acceptable bias between eGFR and mGFR should be at least 10%. This is based on previous reports and the reproducibility of the method in our laboratory, which is <7%. The CP varies from 0 to 1 and estimates whether a given TDI is less than a prespecified fixed percentage. Interrater agreement between eGFR and mGFR Inter-rater agreement was evaluated using the K-index, which can be interpreted as follows: K < 0.20, poor; K = 0.21–0.40, fair; K = 0.41–0.60, moderate; K = 0.61–0.80, good and K = 0.81–1.00, very good agreement [31]. We used the statistical package AGP (Agreement Program) v.1.0 (IGEKO, SP), available at www.ecihucan.es/lfr/apps/? dir=agreement_installer, which is based on the R code developed by Lin et al. [30]. We also analysed data using SPSS Statistics for Windows, version 17.0 (SPSS, Chicago, IL, USA) and MedCalc Statistical Software version 13.0.2 (MedCalc Software, Ostend, Belgium). RESULTS Patients Of 882 patients, 178 (20.4%) were classified as CKD-1, 252 (28.4%) as CKD-2, 251 (28.3%) as CKD-3, 176 (20.0%) as CKD-4 and 25 (2.7%) as CKD-5 (Table 1). The average age was 57.3 ± 14.2 years, 69% of patients were male and mGFR averaged 60.9 ± 32.2 mL/min (range 4.2–173.7) (Table 1). Classification of CKD stages with cystatin C or creatinine-based formulas CKD-1 (n = 178) The number of patients classified by eGFR ranged from 58 (Lund-Malmö) to 366 (Perkins) (Table 2 and Supplementary data, Table S2). True positive cases averaged 70%, missing cases 30% and false positives 42% for creatinine-based formulas and 88%, 12% and 35%, respectively, for cystatin C–based equations. Most missing cases were classified as CKD-2. However, in 10% of the cases with creatinine-based formulas, one stage was skipped and patients were defined as CKD-3 (Supplementary data, Table S3). For example, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on creatinine defined 222 patients as CKD-1; of these, 136 were correctly (true positive) and 86 were incorrectly classified as CKD-1 (false positive) (Table 2). Finally, 42 (24%) of the 178 patients who were classified as CKD-1 by mGFR were not defined as CKD-1 by this formula (missing cases). Table 2 Classification of patients in CKD stages by a representative group of nine creatinine and/or cystatin C–based formulas ‘True positives cases’ represent the subjects that were corretly classified in each CKD stage by eGFR. ‘False positives cases’ represent the patients who were classified in one CKD stage based on eGFR when actually belonging to a different stage. ‘Missing cases’ represent the cases that were not classified in the corresponding CKD stage. a The percentage of false positive cases refers to the number of cases defined in each CKD stage by mGFR (grey column). The percentage of true positive and missing cases refers to the number of cases defined in each CKD stage by eGFR. Table 2 Classification of patients in CKD stages by a representative group of nine creatinine and/or cystatin C–based formulas ‘True positives cases’ represent the subjects that were corretly classified in each CKD stage by eGFR. ‘False positives cases’ represent the patients who were classified in one CKD stage based on eGFR when actually belonging to a different stage. ‘Missing cases’ represent the cases that were not classified in the corresponding CKD stage. a The percentage of false positive cases refers to the number of cases defined in each CKD stage by mGFR (grey column). The percentage of true positive and missing cases refers to the number of cases defined in each CKD stage by eGFR. CKD-2 (n = 252) Patients classified by formulas ranged from 133 (Grubb) to 321 (Nankivell-SPK) (Table 2 and Supplementary data, Table S2). True positives, missing cases and false positives averaged 50% for creatinine-based formulas and 50%, 50% and 36%, respectively, for cystatin C–based equations. Two-thirds of the missing cases were classified as CKD-1 and one-third as CKD-3 for both creatinine and cystatin C–based formulas. For example, the CKD-EPI (cystatin C) equation defined 182 patients as CKD-2; of these, 128 were true positive cases and 54 were false positive cases (Table 2). Also, 124 (49%) of the 252 patients who were classified as CKD-2 by mGFR were not defined as such by this formula (missing cases). CKD-3 (n = 251) Patients classified by formulas in this stage ranged from 80 (Robinson) to 369 (Baracskay) (Table 2 and Supplementary data, Table S2). True positive cases averaged 54%, missing cases 46% and false positive cases 42% for creatinine-based formulas and 69%, 31% and 33%, respectively, for cystatin C–based equations. Missing cases were classified either as CKD-2 (66% for creatinine, 60% for cystatin C), CKD-4 (15%, 32%) or as CKD-1 (18%, 7%). For example, abbreviated modification of diet in renal disease formula (aMDRD) defined 257 patients as CKD-3: 166 of them were true positive and 91 false positive (Table 2). Also, 85 (34%) of the 251 cases classified as CKD-3 according to mGFR were not defined in this stage (missing) based on aMDRD. CKD-4 (n = 176) Patients classified by eGFR ranged from 28 (Perkins) to 213 (Lund-1) (Table 2 and Supplementary data, Table S2). True positive cases averaged 57%, missing cases 43% and false positive cases 25% with creatinine-based equations and 64%, 36%, 26%, respectively, with cystatin C–based formulas. Missing cases were classified as CKD-3 (71%) for both types of formulas and CKD-5 (19% for creatinine, 25% for cystatin C). Also, 10% of the creatinine-based estimations skipped one stage and were defined as CKD-2. For example, the Mayo Clinic Quadratic (MCQ) (cystatin C) formula defined 210 patients; of these, 133 were true positive and 77 were false positive cases (Table 2). Finally, 43 (24%) of the 176 patients who were classified as CKD-4 by mGFR were not defined in the same stage by this formula (missing cases). CKD-5 (n = 25) Patients classified by formulas in this stage ranged from 1 (Le Bricon, Perkins) to 88 (Grubb-1) (Table 2 and Supplementary data, Table S2). True positive cases averaged 30%, missing cases 70% and false positive cases 59% for creatinine-based equations and 44%, 56% and 39% for cystatin C–based formulas. Most missing cases were classified as CKD-4. Again, 10% of the estimations using creatinine skipped one stage and were defined as CKD-3. For example, the Le Bricon equation (cystatin C) defined only 1 patient as CKD-5 (true positive case), instead of 25 that were classified by mGFR. The remaining 24 cases were not defined as CKD-5 by this equation (missing cases). Classification of CKD stages with cystatin C and creatinine-based formulas These formulas showed a number of true positive, false positive and missing cases for each stage of CKD, comparable with the equations that only include cystatin C (Table 2 and Supplementary data, Table S2). For example, the CKD-EPI (creatinine + cystatin C) formula classified 226 patients as CKD-3 instead of 251; of these, 173 were true positive and 53 were false positive cases (Table 2). Finally, 78 (31%) of the 251 patients who were defined as CKD-3 by mGFR were not classified in the same stage by this formula (missing cases). Similar results were observed for more recent equations like the full age spectrum (FAS) combined formula or other approaches based on the mean of two different equations: revised Lund-Malmö (LMR) and Caucasian, Asian, pediatric, and adult cohorts (CAPA) (Table 2 and Supplementary data, Table S2). Interrater agreement between eGFR and mGFR for the classification of CKD stages For creatinine-based algorithms, K-values ranged from 0.530 to 0.204 for the Mayo Clinic Quadratic (MCQ)-CKD and Robinson formulas, respectively (Supplementary data, Table S5). For cystatin C–based algorithms, K-values ranged from 0.632 to 0.245 for the Stevens-2 and Perkins equations, respectively. For the combined creatinine and cystatin C algorithms, K-values ranged from 0.653 to 0.431 for the Stevens and FAS_crcy formulas, respectively. So no formula showed very good strength of agreement (K = 0.81–1.00) in classifying patients in CKD stages. Few formulas, mainly cystatin C based, had good agreement (K = 0.61–0.80) (Supplementary data, Table S5). Agreement between measured GFR and eGFR Creatinine-based formulas The TDI averaged 86%, ranging from 51.1 to 175.4 for the Lund-Malmö (Rv) and Robinson equations, respectively (Supplementary data, Table S4). As an example, modification of diet in renal disease formula (MDRD) formula had a TDI of 54.2, meaning that 90% of the estimations of GFR showed an error ranging from −54 to +54% when compared with mGFR. The CCC averaged 0.74 for all the formulas, reflecting a low level of precision and accuracy, ranging from 0.55 to 0.92 for the Robinson and Mean LMR+CAPA equations, respectively. Finally, the CP averaged 22 for all the formulas, indicating that >78% of the estimations had an error > ±10%. Cystatin C–based formulas The TDI averaged 60%, ranging from 44.2 to 108.7 for the Stevens-2 and Perkins equations, respectively (Supplementary data, Table S4). As an example, the MCQ formula had a TDI of 57.0, meaning that 90% of the estimations of GFR showed an error ranging from −57 to +57% when compared with mGFR. The CCC averaged 0.89 for all the formulas, reflecting a moderate level of precision and accuracy, ranging from 0.74 to 0.94, for the Stevens-2 and Perkins equations, respectively. Finally, the CP averaged 29 for all the formulas, indicating that >71% of the estimations had an error > ± 10%. The CP ranged from 8 to 33 for the Stevens-2 and Perkins equations, respectively. No formula showed 90% of the estimations within bounds of error of ±10% compared with the gold standard. Creatinine and cystatin C–based formulas The TDI averaged 44%, ranging from 38.3 to 50.0 for the Mean LMR+CAPA and Ma equations, respectively (Supplementary data, Table S4). As an example, the CKD-EPI formula had a TDI of 40.6, meaning that 90% of the estimations of GFR showed an error ranging from −41% to +41% when compared with mGFR. The CCC averaged 0.93 for all the formulas, reflecting a moderate level of precision and accuracy, ranging from 0.92 to 0.95 for the BIS-2 and CKD-EPI, and Mean LMR+CAPA equations, respectively. Finally, the CP averaged 32 for all the formulas, indicating that >68% of the estimations had an error > ±10%. Sensitivity analyses Specific formulas did not outperform in the population in which they were designed. The TDI, CCC and CP of the Salazar-Corcoran, Nankivell-A and B or BIS-1 and 2 were comparable in obese and non-obese subjects, renal transplanted patients and non-transplanted patients or young versus elderly subjects, respectively (Supplementary data, Table S6). Also, no formula was particularly reliable in a specific subgroup of patients, such as diabetes, renal transplantation or chronic nephropathies (Supplementary data, Tables S7–S9). DISCUSSION We observed that the error in the classification of CKD stages using eGFR by formulas was very common. The misclassification for all 65 formulas averaged 50% for creatinine-based and 35% for cystatin C–based equations. This error included incorrect classification in a given stage as well as allocation in a consecutive higher or lower stage. Also, in 10% of the cases the error was such that one stage was skipped and patients were classified two stages above or below their current CKD stage. Finally, no clinically relevant improvement in the classification of CKD stages was observed with the use of cystatin C–based formulas compared with creatinine-based algorithms. The error of formulas in the classification of CKD stages is a consequence of the error of eGFR in reflecting mGFR. Previous studies have shown that the error of formulas is frequent and wide, about ±20 to ±30% of mGFR. This has been observed in diabetes [9–15], kidney transplantation [16–20], polycystic kidney disease [21, 22], cancer [32], cirrhosis [33], liver transplantation [34] and patients with human immunodeficiency virus [35]. In the present study, we confirmed the low precision and accuracy of a large group of formulas using specific agreement analysis. TDI values averaged ±86% for creatinine-based formulas, ±60% for cystatin C–based equations and ±44% for those that combine both markers. These TDI values indicate that 90% of the estimations of eGFR are included within a limit of 86%, 60% or 44% of mGFR for each type of equation, respectively. Thus the estimation of real GFR by any formula is extremely variable. In example, in a subject with mGFR of 60 mL/min, eGFR may range from 42 (−30%) to 78 mL/min (+30%) or in another with mGFR of 30 mL/min, eGFR may range from 21 to 39 mL/min. This has important consequences in the allocation of patients in subgroups of GFR. This is illustrated in Figure 1, which shows the eGFR values of the patients of the study had mGFRs of 90, 60 and 30 mL/min. For the cases with an mGFR of 90 mL/min (n = 81), about half of them had eGFR values >90 mL/min and half had <90 mL/min, so ∼50% of them were classified as CKD-1 or CKD-2 and few in CKD-3 (Figure 1B). For those with an mGFR of 30 or 60 mL/min (n = 25 and 58, respectively), about two-thirds had eGFR values higher and one-third had eGFR values lower than the cut-off points. Thus ∼60% of the patients were classified as CKD-2 or 3 and ∼30% as CKD-3 or 4. In a few cases the misclassification was even larger (Figure 1B and C). We defined the CKD stages based on the classification of 2002, which does not divide Stage 3 into two subgroups (3A: 45–59 mL/min and 3B: 30–44 mL/min). However, a wide variability of the estimations was observed around the cut-off value of 45 mL/min, which may indicate that the misclassification within Stage 3 is similar to that observed for the other stages (Supplementary data, Figure S1). Finally, the variability observed around the cut-off points that divide the subgroups explains why 30–50% of patients were incorrectly classified by any formula. FIGURE 1 View largeDownload slide eGFR values of the patients with an mGFR of 90 (pannel A), 60 (B) and 30 mL/min (C) with a representative group of nine creatinine and/or cystatin C–based formulas. Based on repeatability of iohexol plasma clearance in our laboratory, which is about ±5%. We consider all cases included within a ±5% margin of mGFR 90, 60 and 30 mL/min as equal to these values. FIGURE 1 View largeDownload slide eGFR values of the patients with an mGFR of 90 (pannel A), 60 (B) and 30 mL/min (C) with a representative group of nine creatinine and/or cystatin C–based formulas. Based on repeatability of iohexol plasma clearance in our laboratory, which is about ±5%. We consider all cases included within a ±5% margin of mGFR 90, 60 and 30 mL/min as equal to these values. No clinically relevant differences were observed with cystatin C–based equations. In general, true positive cases were higher and false positive and missing cases lower with equations using cystatin C (Table 2 and Supplementary data, Table S2). This led to a lower error in the misclassification of CKD stages, which averaged 35% compared with the 50% error of creatinine-based equations. However, this still represents a wide margin to accept cystatin C as a good marker of GFR. This is in line with previous studies that questioned formulas based on cystatin C [9, 15, 16, 36, 37]. Finally, formulas that combine creatinine and cystatin C do not outperform equations that only use cystatin C. There is no clear explanation for this phenomenon. The rationale of combining both markers has not been clearly explained. From our point of view, creatinine, which is a poor marker of GFR, does not improve either the precision or accuracy of the estimation of renal function in these formulas. So the combined formulas do not represent an alternative for the estimation of renal function in clinical practice. It may be argued that calibrated creatinine and cystatin C may influence the performance of the formulas. However, it is interesting to compare two equations, Effersøe and CKD-EPI_cr, both with a similar TDI (Supplementary data, Table S4). The former was developed in 1957 with uncalibrated creatinine, whereas the latter was designed in 2011 with the calibrated method. Also, more recent formulas have shown a worse TDI than the old equations (MCQ versus Effersøe: Supplementary data, Table S4). In the same line, cystatin C–based formulas like Le Bricon (from 2000) and FAS_cy (from 2017) have a TDI of ∼70%. Although this is indirect evidence, since we do not measure creatinine or cystatin C with the non-calibrated methods, it seems plausible that the calibration does not have a relevant weight in the performance of formulas. The causes of the error of formulas are unclear. Formulas are algorithms mainly based on endogenous biomarkers such as serum creatinine and/or cystatin C. These molecules do not reflect real GFR properly. The synthesis of creatinine is not constant since it is affected by the daily intake of proteins and muscle turnover [38]. Changes in muscle mass also influence creatinine production [39–42]. Secretion and reabsorption by renal tubular cells and extrarenal clearance influence the serum levels of creatinine [43–49]. Serum cystatin C is influenced by obesity, diabetes, hypertension and metabolic syndrome. Thus high levels of this marker may reflect these conditions, and not necessarily renal dysfunction. Our study is in line with previous reports. Froissart et al. [7] evaluated the performance of the Cockcroft–Gault and MDRD formulas in 2095 subjects, showing that ∼30% of the patients were incorrectly classified in CKD stages. Other studies evaluating creatinine-based formulas observed similar results [6, 8, 10, 11, 19, 20, 36]. However, these publications evaluated few formulas and reported the classification in CKD stages only in terms of patients correctly classified or not. On the other hand, the error of cystatin C formulas has been seldom studied [8, 16]. Feng et al. [8] evaluated creatinine and cystatin C–based formulas, showing that ∼30–50% of the cases were incorrectly classified in CKD stages. Our group observed similar results evaluating 51 formulas in 193 renal transplanted patients. Thus, to the best of our knowledge, this is the first study that evaluated the performance of a large number of cystatin C and/or creatinine-based formulas in the classification of the stages of CKD in patients with different clinical situations over a wide range of GFRs. Our study has limitations. First is small number of patients with CKD Stage 5, which may limit the interpretation of our results in this subgroup of patients. However, other studies have shown that the error of eGFR is observed in advanced kidney failure (GFR < 15 mL/min) [19, 50]. Second, we studied mainly a Caucasian cohort and so we can not extrapolate our results to other populations. CONCLUSION We found that one or two patients out of three are incorrectly classified in higher or lower stages of CKD when renal function is estimated by formulas. This was observed for every stage of CKD and for all 65 evaluated formulas. Thus the correct classification of patients in CKD stages based on eGFR is a matter of chance. This is a strong limitation in evaluating the severity of renal disease, the risk for progression and the evolution of renal dysfunction over time. SUPPLEMENTARY DATA Supplementary data are available at ndt online. ACKNOWLEDGEMENTS We thank the Instituto de Tecnologías Biomédicas (ITB), the Instituto de Salud Carlos III (grants PI13/00342 and PI16/01814), REDINREN (RD16/0009/0031), the DISA Foundation, the Spanish Society of Nephrology (SENEFRO) and the IMBRAIN project for support (FP7-RE6-POT-2012-CT2012-31637-IMBRAIN) funded under the 7th Frameworks Programme capacities. FUNDING This study was supported by the Instituto de Salud Carlos III (PI13/00342, PI16/01814), REDINREN (RD16/0009/0031), DISA Foundation, the Spanish Society of Nephrology (SENEFRO) and the IMBRAIN project (FP7-RE6-POT-2012-CT2012-31637-IMBRAIN). S.L.L. is a research fellow supported by the Instituto de Salud Carlos III (grants for Río Hortega specialized health care post-training contracts), ISCIII CM15/00214. E.P. is a researcher in the Program Ramón y Cajal (RYC-2014-16573). CONFLICT OF INTEREST STATEMENT None declared. REFERENCES 1 Trillini M , Perico N , Remuzzi G. 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Nephrology Dialysis Transplantation – Oxford University Press

**Published: ** May 11, 2018

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