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and in kidney transplant patient&#46; The markers chosen to develop these equations have been serum creatinine and cystatin C &#59; the serum concentration of these substances may be modified by factors other than GFR in kidney transplant patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;5</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The CKD-EPI equations using creatinine values were developed to reduce the systematic underestimation that occurred in the GFR with the MDRD study equation when applied to younger population&#44; female sex and lower serum creatinine levels&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> In the development of the CKD-EPI equation in 2009&#44; patients with higher GFR values were included&#44; which allowed improving the estimates with respect to the MDRD equation&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;8</span></a> This improvement is mainly due to the categorization of the population into 4 different groups&#44; which are represented by 4 equations that use the same variables&#44; but with different exponents for a given serum value of creatinine depending on whether they are greater or lower than 0&#46;7&#8239;mg&#47;dL in women and 0&#46;9&#8239;mg&#47;dL in men&#46; In 2012&#44; the same group of researchers tried to improve the predictions of the equations by incorporating cystatin C as a second marker of the GFR&#44; observing a slight improvement in its predictive capacity&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> However&#44; the authors themselves concluded that the equations with cystatin C and creatinine should not be used routinely but rather to confirm the existence of kidney failure&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">The studies that have analyzed the validity of the MDRD and CKD-EPI equations of 2009 in kidney transplant patients have obtained not uniform results&#44; some found a lower bias using CKD-EPI with respect to direct GFR measurement&#44; while others continue to observe a greater overestimation of the GFR with this CKD-EPI equation&#44;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;3&#44;10&#44;11</span></a> and less biases with the use of MDRD formula&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> It has been observed that the CKD-EPI equation from 2012&#44; has less bias that the MDRD&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> In addition it was demonstrated that this estimation of GFR in transplanted patients correlated with different risk factors&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The objective of the present study is to analyze the degree of agreement between the estimated GFR using the MDRD&#44; CKD-EPI equations 2009 and the new equations CKD-EPI 2012 and the GFR measured by the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA in kidney transplant patients in a stable clinical condition&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">This is a retrospective study that included kidney transplant patients from the Kidney Transplant Unit of the Regional University Hospital &#34;Virgen de las Nieves&#34; in Granada&#46; Patients were over 18 years of age&#44; with stable kidney function and had a measurement of the GFR one year after transplantation&#46; Of the 556 available measurements&#44; those who had simultaneous determinations of serum creatinine and cystatin C were selected&#59; only one determination per patient was included in the study&#46; Therefore&#44; for the analysis we finally have 270 GFR measurements with <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">The measurement of GFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA &#40;mGFR&#41; was performed by the Nuclear Medicine Service&#46; After a single injection of a standardized dose of the tracer&#44; blood was drawn at 120 and 240&#8239;min&#44; and the GFR was calculated according to the technique used by Br&#246;chner-Mortensen&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> The values obtained were corrected for the body surface using the DuBois and DuBois formula&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> The supplementary information &#40;appendix 1&#41; shows the equations used to calculate the GFR &#40;eGFR&#41; &#58; MDRD4 2006 traceable to IDMS &#40;eGFR MDRD-IDMS&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> CKD-EPI of creatinine 2009 &#40;eGFR CKD-EPI-Cr&#41;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> and CKD-EPI with creatinine and cystatin C from 2012 &#40;eGFR CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The values of serum creatinine and cystatin C obtained on a date coinciding with the measurement of GFR <span class="elsevierStyleSup">51</span>Cr-EDTA were recorded&#46; Serum Creatinine was measured using the Jaff&#233; technique compensated with traceability to IDMS &#40;Beckman Coulter AU5832 autoanalyzer&#41;&#46; For a concentration of 0&#46;78&#8239;mg&#47;dL the coefficient of variation was 3&#46;1&#37;&#44; for 1&#46;84&#8239;mg&#47;dL of 2&#46;2&#37; and for 6&#46;6&#8239;mg&#47;dL it was 2&#46;3&#37;&#46; cystatin C was measured by nephelometry &#40;Immage 800 by Beckman Coulter&#41; with coefficient of variation intraassay &#60; 1&#46;2&#37; and interassay &#60; 3&#46;25&#37;&#46; This autoanalyzer uses Trimero Diagnostic reagent with traceability to the certified European material ERM-DA471&#47;IFCC &#40;Institute for Reference Materials and Measurements&#44; IRMM&#41;&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">In relation to the statistical analysis&#59; first&#44; it was analyzed&#44; whether there was a normal distributions of the values using the Kolmogorov-Smirnov test for the mGFR variables and the different eGFRs&#46; The mGFR and eGFR-MDRD-IDMS values followed a normal distribution while the eGFR values with all the CKD-EPI equations did not&#46; Therefore&#44; Wilcoxon Test was used to compare of means of paired samples and the Spearman correlation analysis for the degree of relationship between 2 quantitative variables&#46; An analysis using multiple linear regression analysis was conducted where the mGFR was the dependent variable and the independent variables were&#58; sex&#44; age&#44; serum creatinine and cystatin C values to explain the degree of the variance of mGFR with the variables introduced&#46; The values were analyzed with or without logarithmic transformation as was done in the design of the original equations of MDRD and CKD-EPI&#46; A p&#8239;&#60;&#8239;0&#46;05 was considered statistically significant&#46; Statistical analysis was performed using SPSS version 22 program&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">The accuracy of the estimates was expressed by absolute bias&#44; mean square error &#40;root mean square error&#44; RMSE&#41; and with the values P30 and P10&#46; The absolute bias was calculated as the arithmetic mean of the differences between eGFR and mGFR&#46; Since the differences may result to be positive or negative&#44; the deviation between the two measures may not properly reflect the bias&#44; thus we also calculated the RMSE done as follows&#58; first the differences between eGFR and mGFR were calculated&#44; then the resulting value was squared transformed&#44; the values were added&#44; the arithmetic mean was obtained and finally its square root is calculated&#46; The standard deviation of these squared differences were also calculated&#46; The P30 and P10 values were calculated as the percentage of patients whose eGFR value was included in the 30&#37; or 10&#37; above or below the mGFR reference value&#46; The precision was estimated by the standard deviation of the absolute bias&#44; with the square root of the mean square deviation and with the interquartile range of the bias&#46; The interquartile range was obtained by subtracting third quartile and first quartile of the absolute bias values&#46; Finally&#44; we calculated the values of sensitivity&#44; specificity&#44; negative predictive value&#44; positive predictive value and degree of concordance &#40;kappa&#41; for detection of a GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with eGFR equations&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0050" class="elsevierStylePara elsevierViewall">There were 270 patients included in the study and each patient had the measurement GFR using <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows the epidemiological data&#44; the serum creatinine and cystatin C values and the mGFR of the study population&#46; A 99&#46;6&#37; of patients were on prednisone &#40;3&#46;5&#8722;5&#8239;mg&#47;day in 95&#37; of the cases&#41; when the GFR was measured&#46; Most patients had received cadaveric graft &#40;89&#46;6&#37;&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">The relationship between mGFR and serum concentration of creatinine and cystatin C</span><p id="par0055" class="elsevierStylePara elsevierViewall">The mGFR &#40;log&#41; correlated significantly with the serum creatinine values &#40;r &#61; -0&#46;64&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#44; <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>-A&#46; It is observed that the values of mGFR were higher in men&#44; and degree of correlation was also greater in male than females &#40;males r &#61; -0&#46;75&#44; p&#8239;&#60;&#8239;0&#46;001&#59; women r&#8239;&#61;&#8239;0&#46;58&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#46; The mGFR &#40;log&#41; also correlated with cystatin C levels although the r value lower than with Creatinine &#40;r&#8239;&#61;&#8239;&#8722;0&#46;52&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#44; and it there was not a difference between males and females &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>- B&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">Generation of linear regression models for mGFR as dependent variable using as predictive variables&#58; sex&#44; age&#44; creatinine and cystatin C as in the formulas published for CKD-EPI of 2009 and 2012 show multiple correlation coefficients about 0&#46;73 &#40;p&#8239;&#60;&#8239;0&#46;001&#41; with adjusted R<span class="elsevierStyleSup">2</span> values of 0&#46;52&#46; Therefore&#44; in our kidney transplanted population&#44; only half of the GFR variability measured with the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA is explained by regression models that use these variables&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Comparison of mGFR with eGFR MDRD-IDMS and CKD-EPI-Cr</span><p id="par0065" class="elsevierStylePara elsevierViewall">The estimated eGFR using the MDRD-IDMS and CKD-EPI-Cr equations was significantly higher &#40;p&#8239;&#60;&#8239;0&#46;001&#41; than the mGFR &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; The degree of correlation of eGFR obtained by the two methods and mGFR was similar&#44; and the dispersion of values appear to be very similar &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> A&#44;B&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">The absolute bias of eGFR CKD-EPI-Cr was higher than the bias obtained with MDRD-IDMS in the entire group and in each of the subgroups considering the stages of CKD&#46; Bias of eGFR CKD-EPI-Cr was considerable in the group with mGFR &#8805;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> which became almost 9&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> higher than the MDRD-IDMS&#46; Considering RMSE&#44; note that with MDRD-IDMS the values obtained were the lowest for all CKD stages&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">The degree of overestimation of the GFR obtained with the equation eGFR MDRD-IDMS and CKD-EPI-Cr was not related to the mGFR value of &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> A and <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a> A&#41;&#46; However&#44; the overestimation was significantly correlated with the estimated value of eGFR MDRD-IDMS and CKD-EPI-Cr &#40;r&#8239;&#61;&#8239;0&#46;75 and r&#8239;&#61;&#8239;0&#46;83 respectively &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>B and <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>B&#41;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Comparison of mGFR and eGFR CKD-EPI-CystC and eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC</span><p id="par0080" class="elsevierStylePara elsevierViewall">The values of eGFR with CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC were greater &#40;p&#8239;&#60;&#8239;0&#46;001&#41; than the mGFR &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#44; being also greater than eGFR MDRD-IDMS globally&#46; In CKD stage 4 the bias was inferior than that of MDRD-IDMS and higher in the other stages&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">The bias of eGFR CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC was lower than CKD-EPI-Cr for all stages of CKD&#46; The bias of eGFR CKD-EPI-CystC had a greater dispersion of values as compared with CKD-EPI-Cr&#8239;&#43;&#8239;CystC and with the rest of the eGFR equations&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> shows the relationship between eGFR CKD-EPI-CystC and mGFR &#40;A&#41; and CKD-EPI-CystC shown CystC&#8239;&#43;&#8239;Cr and mGFR &#40;B&#41; with correlation coefficients of 0&#46;49 and 0&#46;64&#44; respectively&#46; Some values of eGFR predicted with the CKD-EPI-CystC equation were excessively high with respect to mGFR&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0090" class="elsevierStylePara elsevierViewall">The RMSE values were higher with CKD-EPI-CystC than those obtained with the other 3 equations globally&#44; except in CKD stage 4 that was similar to eGFR CKD-EPI-Cr&#46; The RMSE value CKD-EPI-CystC&#8239;&#43;&#8239;Cr was higher than MDRD-IDMS in all CKD stages except 4&#46; Interquartile range of eGFR MDRD-IDMS bias was the smallest in all stages of CKD&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The bias of eGFR CKD-EPI-CystC did not correlate with the mGFR value &#40; <a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>A&#41; but did show a strong correlation &#40;r&#8239;&#61;&#8239;0&#46;91&#41; with the eGFR CKD-EPI-CystC &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>B&#41;&#46; The bias of the eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC did not correlate with mGFR &#40;<a class="elsevierStyleCrossRef" href="#fig0035">Fig&#46; 7</a>A&#41; but it highly correlated with eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC &#40;r&#8239;&#61;&#8239;0&#46;85&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0035">Fig&#46; 7</a>B&#41;&#46; With both equations there were important overestimations as compared with mGFR that became even more than 80&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with CKD-EPI-CystC and above 70&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with CKD- EPI-Cr&#8239;&#43;&#8239;CystC&#41;&#46;</p><elsevierMultimedia ident="fig0030"></elsevierMultimedia><elsevierMultimedia ident="fig0035"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Comparison of accuracy values of P10 and P30&#44; sensitivity&#44; specificity and predictive values</span><p id="par0100" class="elsevierStylePara elsevierViewall">A mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> was observed in 91&#46;9&#37; of the transplanted patients&#44; while with MDRD-IDMS and CKD-EPI-Cr it was found in 67&#37; and 56&#46;3&#37; respectively and with CKD-EPI-CystC and CKD-EPI-CystC&#8239;&#43;&#8239;Cr in in 61&#46;1&#37; and 57&#46;8&#37; respectively&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> show the precision values of P10 and P30 for the four equations of eGFR&#46; The P30 values were similar for MDRD-IDMS&#44; CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC and were greater than CKD-EPI-Cr in stages 1&#8239;&#43;&#8239;2 and stage 3&#44; while in stage 4 the P30 values were higher for CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The P10 values were higher with CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC in virtually all stages as compared with MDRD-IDMS and CKD-EPI-Cr&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">As far as ability to detect an GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> it was noted that MDRD-IDMS shows most sensitivity &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41; with a specificity somewhat lower than CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The positive predictive value was similar in all equations&#44; while the negative predictive value was slightly higher with MDRD-IDMS&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Concordance according to CKD stages</span><p id="par0110" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> shows the degree of concordance of the equations of eGFR in the classification of all stages of CKD and in assessment of GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; The coefficients kappa were low for all equations&#44; the highest coefficient corresponded to the MDRD-IDMS equation followed by CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The coefficients were somewhat higher in the assessment of patients with mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46;</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">The low concordance was mainly due to a change in classification of patients from stages 2&#44; 3 and 4 with mGFR to stages 1&#44; 2 and 3 with the eGFR equations &#40;<a class="elsevierStyleCrossRef" href="#fig0040">Fig&#46; 8</a>&#41;&#46; The percent of patients misclassified were low for MDRD-IDMS in stages 2 and 3 and higher in stage 4&#59; misclassification was lower for the stage 4 with CKD-EPI-Cr&#8239;&#43;&#8239;CystC and CKD-EPI-CystC&#46; <a class="elsevierStyleCrossRef" href="#fig0045">Fig&#46; 9</a> shows the percentage of correct classification of CKD stages 2&#44; 3 and 4 using the four eGFR equations&#59; MDRD-IDMS classified more patients correctly in stages 2 and 3 and that CKD-EPI-Cr&#8239;&#43;&#8239;CistC or CKD-EPI-CistC were more efficient for the classification of patients in stage 4&#46;</p><elsevierMultimedia ident="fig0040"></elsevierMultimedia><elsevierMultimedia ident="fig0045"></elsevierMultimedia></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Discussion</span><p id="par0120" class="elsevierStylePara elsevierViewall">Serum creatinine &#40;Cr&#41; is the main marker of kidney function &#40;GFR&#41; being used in daily clinical practice&#46; But&#44; the relationship between serum Cr and GFR is not as close as one would expect&#44; especially in kidney transplants&#46; In our kidney transplant patients after one year of follow up&#44; only about 50&#37; of the change in mGFR could be predicted by serum creatinine&#44; cystatin C&#44; sex and age&#44; indicating that there are other very influential beyond our control variables&#46; The same problem arises with the different equations to estimate GFR published in the literature&#46; One point has to be taken into consideration&#44; in kidney transplants the serum creatinine levels have a different relationship with GFR than in non-transplanted patients&#44; which causes a overestimation of the actual kidney function calculated with these equations&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;10</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">Steroids modify the serum creatinine&#47;muscle mass ratio and may cause a discrepancy between serum Cr levels and kidney function&#44; however other factors may be involved&#46; Some studies indicate that in patients not taking steroids there is less overestimation of GFR however this effect is not that evident with time after transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> Other authors have failed to show an effect of steroids<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;14</span></a> and some authors consider that the low dose of steroids used in long-term patients should not have an effect on serum creatinine&#47;muscle mass ratio&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> In our study&#44; most patients were on a very low doses of prednisone&#44; so we could not analyze a discrepancy between serum creatinine values and the GFR&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">cystatin C is potentially a better marker than creatinine for assessment of GFR because it is less influenced by age&#44; gender&#44; race&#44; body weight or muscle mass&#46; In our case&#44; the correlation of mGFR with cystatin C was lower than with creatinine&#44; but it was observed that with cystatin C the differences related to sex were corrected &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>B&#41;&#46; Also it has been reported that cystatin C levels can be discretely raised by high doses o of steroids&#44; without a true change in GFR&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;14</span></a> The use of trimetroprim may affect the calculation of GFR although it would have less impact on long-term stable transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Thyroid hormones may decrease serum creatinine levels and raise cystatin C levels&#59; most transplant patients should not have thyroid pathology&#44; so the overall impact must be low&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0135" class="elsevierStylePara elsevierViewall">Since the arrival of the MDRD-IDMS equation&#44; several equations have been published by the CKD-EPI group to trying to improve the estimation of the GFR&#46; Publications on the performance of these equation show nonuniform results&#46;&#46; Stevens et al&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> evaluated in kidney transplants of the CKD-EPI equation of 2009&#44; observing that the overall bias was lower with CKD-EPI than with MDRD-IDMS formulas&#44; with small differences between transplanted and non-transplanted&#44; which was considered of low impact&#46; However&#44; the graphics revealed that at for a GFR&#8239;&#8805;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; the overestimation with CKD-EPI is greater than 10&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; which is more bias than with MDRD-IDMS equation&#46; For GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; the bias is marginal and similar for both equations&#46; In patients with a body mass index lower than 20&#8239;kg&#47;m<span class="elsevierStyleSup">2</span>&#44; all the equations performed poorly&#44; so it was suggested using body weight to improve predictions&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Therefore&#44; in this study there were doubts about whether it was convenient the application of the CKD-EPI-Cr equation in kidney transplant patients with high GFR&#46; Subsequent comparative studies in kidney transplant recipients also found greater precision and less bias with CKD-EPI-Cr than with MDRD-IDMS<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20&#8211;23</span></a> with a few studies supporting it&#46;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> In 2012 Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> published the new CKD-EPI equations that included creatinine and cystatin C&#59; the kidney transplanted population was not included because they had many other not well defined factors that may affect the results&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">Our study observed a significant overestimation of GFR in kidney transplants patients using the MDRD-IDMS and the CKD-EPI equations of 2009 and 2012 as compared with the GFR measured as the plasma clearance with <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; The average bias for MDRD-IDMS was 11&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> followed by 14&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD- EPI-Cr&#8239;&#43;&#8239;CystC&#44; and it was greater for the other equations&#46; The bias obtained in our study is greater than the observed in previous studies in kidney transplant patients which varied for CKD-EPI-Cr from &#8722;4&#46;5&#8211;8&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span><a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11&#44;20</span></a> although others reached values similar to ours&#46; With the new 2012 CKD-EPI equations&#44; Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> found in kidney transplants a bias of &#8722;0&#46;54&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-Cr&#8239;&#43;&#8239;CystC and &#8722;2&#46;82&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-CystC&#44; which is less bias than MDRD-IDMS and CKD-EPI-Cr&#46; However&#44; Keddis et al&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> observed a bias of &#8722;5&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> and &#8722;8&#46;7&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; for CKD-EPI-Cr&#8239;&#43;&#8239;CystC and CKD-EPI-CystC respectively and &#8722;1&#46;9 for MDRD-IDMS&#46; Kukla et al&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> found a bias of &#43;9&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#44; &#43;8&#46;6&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-CystC and 6&#46;4&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for MDRD-IDMS&#46; This result of a lower bias for MDRD-IDMS is similar to the results reported in the present article&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">In the present article&#44; the bias observed correlated with the estimated value obtained with all the equations as shown in <a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3&#44;4&#44;6</a> and 7&#44; being particularly striking with the equations CKD-EPI-CystC and CKD-EPI -Cr&#8239;&#43;&#8239;CystC&#46; The bias observed did not correlate with the mGFR obtained with plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; The use of the cystatin C equations in our study overestimated kidney function in patients with higher GFR&#44; so reducing the percentage of patients correctly classified as seen in g&#161;fgures 8 and 9&#46; In these patients the use of the MDRD-IDMS equation could be more reliable since it is more conservative&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;22&#44;24</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">The differences observed in the biases between different series are usually attributed to the demographic variable itself and to possible differences in the techniques of measurement of creatinine and cystatin C if they are not standardized&#44; putting little emphasis on the method used to measure the GFR or the fact that they are kidney transplanted patients&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> In a study that compared the bias in a healthy population &#40;kidney donors&#41; with a population of transplanted patients with renal insufficiency&#44; it was observed that age greatly influenced the degree of bias&#44; with kidney transplants always less than healthy individuals&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">Published studies on kidney transplants have used different techniques for measuring GFR&#58; plasma clearance of inulin&#44; iohexol&#44; iothalamate&#44; or plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA or <span class="elsevierStyleSup">99m</span>Tc-DTPA that although they exhibit a correlation coefficients exceeding 0&#46;9 with respect to the renal clearance of inulin as standard&#44; each technique has its own bias&#46; Thus&#44; it is somewhat difficult to compare results from different studies with different methodologies and conclusions are not easy to obtain&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> For example&#44; GFR values obtained with the renal clearance of inulin are low&#46; Plasma clearance of <span class="elsevierStyleSup">99m</span>Tc-DTPA or <span class="elsevierStyleSup">51</span>Cr-EDTA provides higher values of GFR than the inulin renal clearance&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a> In the work of Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> the equations were developed using urinary or plasma clearance of iothalamate and for validation it was used a population in which a wide variety of methods were used to measure GFR&#44; so the biases found are an average of what one would expect when comparing populations with different techniques&#46; To validate the equation CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#44; Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> used urinary clearance of inulin while Keddis et al&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> and Meeusen et al&#46;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> used iothalamate clearance&#46; The population of Keddis and Meeusen had a similar average GFR&#44; with similar sex ratio and average age &#40;they come in part from the same data source&#44; Mayo Clinic Foundation&#41;&#46; The population of Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> had were relatively young with a higher proportion of men and patients with &#60; 15&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> were excluded&#59; and despite of this in their series the average GFR was 5&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> lower for similar values of creatinine and cystatin C&#46; Our population was younger&#44; proportion of men was similar&#44; somewhat lower serum levels of creatinine and cystatin C despite of which we obtained a GFR 7-12&#8239;mL&#47;min&#47;1&#46;73&#8239;m <span class="elsevierStyleSup">2</span> lower than the other series&#44; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA to measure GFR&#46; This could explain that our biases were greater than in these mentioned series and that were similar to those observed in other studies&#46;<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">19&#44;26</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">The bias as an adjustment parameter of 2 techniques just calculates the differences between the 2 measurements&#44; and arithmetic mean was then calculated&#46; Linear regression generates an equation seeking to minimize residuals&#46; Large positive differences will be canceled by large negative differences&#44; with the appearance that both techniques fit very well if the bias obtained is close to zero&#46; However&#44; considering the standard deviation or the interquartile range of bias&#44; the impact of these large differences on the overall adjustment are better described&#58; the greater the bias deviation&#44; the greater the mistake made in many individuals&#46; The standard deviation of the bias obtained by the different studies ranges fom 10&#8211;20&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; similar to the data obtained in our study and the analysis of the Bland-Altman graphs of the different studies reveals that there is a large dispersion in the differences of the individual data&#46; <a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3&#44;4&#44;6&#44;7</a> shows that for a specific eGFR value from each equation the error made grows significantly in patients with GFR&#8239;&#62;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; reaching differences of more than 80&#8211;100&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for equations using cystatin C&#46; The study by Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> also mentioned that the equation that only includes cystatin C provides worse results than if used with creatinine&#46;</p><p id="par0165" class="elsevierStylePara elsevierViewall">Some authors prefer to use of the values of mean square deviation or RMSE to compare different equations&#44; because they better reflect individual differences by computing all differences as positive and averaging them&#46; In our study&#44; with MDRD-IDMS there were obtained lower RMSE values that were similar to the other equations in patients with stage 4 CKD&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">The accuracy values P30 and P10 observed in our population were much lower for all the equations than those observed in other series&#44; being less than 40&#46;9&#37; and 59&#46;1&#37; respectively&#46; In other series&#44; values of 77&#8211;84 &#37; are found when moving from MDRD-IDMS to CKD-EPI<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;11</span></a> and they increase from 80&#46;4&#37;&#8211;86&#46;5&#37; when moving from MDRD-IDMS to CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> found P30 values that improved discreetly from 87&#46;2&#37; with CKD-EPI-Cr to 91&#46;5&#37; with CKD-EPI-Cr&#8239;&#43;&#8239;CystC when GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> and rose from 92&#46;2 &#37; at 97&#46;7&#37; when GFR&#8239;&#62;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; This apparent lack of performance in our population could be explained if we assume that the GFR with <span class="elsevierStyleSup">51</span>Cr-EDTA yields measurements between 5 and 10&#8239;mL&#47;min lower than other GFR measurement techniques&#46; In any case&#44; in our data it seems that the degree of improvement achieved in P30 with CKD-EPI-Cr&#8239;&#43;&#8239;CistC is not quantitatively important with respect to the other equations that use only creatinine&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">Our study analyzed the performance of the different equations to detect a GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; It was observed that the MDRD-IDMS equation showed the highest sensitivity&#44; while the highest specificity was obtained with the CKD-EPI-Cr&#8239;&#43;&#8239;CystC equation&#44; which is expected since the latter results in estimates greater than MDRD-IDMS and when creatinine and cystatin C levels are sufficiently elevated&#44; the estimate for GFR are clearly below 60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; Positive predictive values are similar for all equations while negative predictive values are slightly better for CKD-EPI equations&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">A strong positive aspect of the present study is the measurements of GFR in all patients one year after receiving the graft&#44; which adds homogeneity to the study population&#46; In most series&#44; the measurements of GFR were obtained at different times during the follow up after the kidney transplant&#44; the relationship between muscle mass&#44; nutritional status and steroid dose may change with time&#44; therefore relationship between serum creatinine and the degree of function may not be the same throughout the time after the transplant&#46; A limitation of our study is not being able to analyze the influence of the steroid dose&#44; because the most patients were on prednisone at low doses as the maintenance treatment&#44; although this also brings homogeneity to the population analyzed&#46; It would be of interest to compare in patients with the same period after transplant&#44; 2 different measurement of GFR using different techniques and evaluate the differences in the bias between the equations CKD-EPI and MDRD-IDMS and the mGFR measured by different techniques&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">In summary&#44; in the kidney transplant population the GFR estimated by the CKD-EPI equations that use serum creatinine and cystatin C as markers of kidney function&#44; show a greater bias than the MDRD-IDMS equation and the GFR is measure by plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; These equations produce a significant overestimation of the GFR if the eGFR is greater than 60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; however the the error obtained is reduced below the observed with the MDRD-IDMS equation if the GFR is less than 30&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; The degree of precision with all the equations was low in all stages of the CKD&#46; It is important to take into consideration the reference technique used to measure GFR in kidney transplants when analyzing the performance of the different MDRD-IDMS or CKD-EPI equations&#46;</p><p id="par0201" class="elsevierStylePara elsevierViewall">The technique to measure GFR may explain the discrepancies between different reports in the literature comparing the degree of precision of the different equations&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conflict of interests</span><p id="par0195" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest in relation with the contents of this article&#46;</p></span></span>"
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              "titulo" => "The relationship between mGFR and serum concentration of creatinine and cystatin C"
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              "titulo" => "Comparison of mGFR with eGFR MDRD-IDMS and CKD-EPI-Cr"
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              "titulo" => "Comparison of mGFR and eGFR CKD-EPI-CystC and eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC"
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              "titulo" => "Comparison of accuracy values of P10 and P30&#44; sensitivity&#44; specificity and predictive values"
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              "titulo" => "Concordance according to CKD stages"
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            0 => "Glomerular filtration rate"
            1 => "Kidney transplantation"
            2 => "MDRD"
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            4 => "cystatin C"
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            0 => "Filtrado glomerular"
            1 => "Trasplante renal"
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            4 => "Cistatina C"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Background</span><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">When estimating the glomerular filtration rate &#40;GFR&#41; in kidney transplant patients&#44; significant differences have been found between MDRD and the 2009 CKD-EPI equations&#44; and reference techniques&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objective</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">To analyse and compare the performance of MDRD and the 2009 and 2012 CKD-EPI equations against 51Cr-EDTA plasma clearance in measuring GFR in 270 kidney transplant patients after one year&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">The mean measured GFR was 43&#46;0&#8239;&#177;&#8239;11&#46;4 &#40;18&#46;2&#8211;79&#46;4&#41; ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; with creatine levels of 1&#46;42&#8239;&#177;&#8239;0&#46;46 &#40;0&#46;60&#8211;4&#46;33&#41; mg&#47;dl and cystatin C levels of 1&#46;45&#8239;&#177;&#8239;0&#46;53 &#40;0&#46;42&#8211;3&#46;48&#41; mg&#47;l&#46; This correlated moderately with creatinine &#40;r&#61;&#8211;0&#46;61&#44; p&#8239;&#60;&#8239;0&#46;001&#41; and cystatin C &#40;r&#61;&#8211;0&#46;52&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#46; Using linear regression techniques&#44; it was found that creatinine&#44; cystatin C&#44; gender and age only explained 52&#37; of GFR total variance&#46; All equations overestimated GFR&#44; with a mean bias of &#43;11&#46;1&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for MDRD&#44; &#43;16&#46;4&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for 2009-CKD-EPI&#44; &#43;15&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI with cystatin C and &#43;14&#46;1&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for 2012-CKD-EPI with creatinine and cystatin C&#46; eGFR by MDRD and the 2009 CKD-EPI equation correlated better with 51Cr-EDTA than CKD-EPI with creatinine and&#47;or cystatin C&#46; The overestimations were negatively correlated with creatinine and cystatin C levels&#44; most significantly for CKD-EPI with creatinine and&#47;or cystatin C when GFR was greater than 60&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">The 2012 CKD-EPI equations with creatinine and&#47;or cystatin C significantly overestimate GFR in stage 1 and 2 chronic kidney disease&#46; The MDRD equation is therefore recommended in these cases&#46; The reference method used to measure GFR seems to heavily influence the bias of the equations&#46;</p></span>"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducci&#243;n</span><p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Para la estimaci&#243;n del filtrado glomerular renal &#40;FG&#41; en trasplantados renales se emplean las ecuaciones MDRD y CKD-EPI de 2009 que han mostrado diferencias importantes cuando se comparan con el FG medido con t&#233;cnicas de referencia&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Objetivo</span><p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">Analizar el rendimiento de las ecuaciones MDRD&#44; CKD-EPI de 2009 y de 2012 en 270 pacientes trasplantados renales de 1 a&#241;o de evoluci&#243;n&#44; comparando con el FG medido con aclaramiento plasm&#225;tico de 51Cr-EDTA&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0115" class="elsevierStyleSimplePara elsevierViewall">El FG medido fue 43&#44;0&#8239;&#177;&#8239;11&#44;4 &#40;18&#44;2-79&#44;4&#41; mL&#47;min&#47;1&#44;73&#8239;m2&#44; con niveles de creatinina de 1&#44;42&#8239;&#177;&#8239;0&#44;46 &#40;0&#44;60-4&#44;33&#41; mg&#47;dL y de cistatina C de 1&#44;45&#8239;&#177;&#8239;0&#44;53 &#40;0&#44;42-3&#44;48&#41; mg&#47;L&#46; El FG medido se correlacion&#243; moderadamente con creatinina &#40;r&#61; -0&#44;61&#44; p&#8239;&#60;&#8239;0&#44;001&#41; y cistatina C &#40;r&#61; -0&#44;52&#44; p&#8239;&#60;&#8239;0&#44;001&#41;&#46; Empleando t&#233;cnicas de regresi&#243;n lineal observamos que creatinina&#44; cistatina C&#44; sexo y edad s&#243;lo explicaban el 52&#37; de la varianza total del FG&#46; Todas las ecuaciones sobrestimaron el FG&#44; con sesgo medio de &#43;11&#44;1&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para MDRD&#44; &#43;16&#44;4&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI de 2009&#44; &#43;15&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI con cistatina C and &#43;14&#44;1&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI con creatinina y cistatina C de 2012&#46; Las estimaciones con MDRD y CKD-EPI de 2009 se correlacionaron mejor con 51Cr-EDTA que CKD-EPI con creatinina y&#47;o cistatina C&#46; Las sobrestimaciones se correlacionaron negativamente con los niveles de creatinina y cistatina C&#44; siendo m&#225;s importantes para CKD-EPI con creatinina y&#47;o cistatina C cuando el FG fue mayor de 60&#8239;mL&#47;min&#47;1&#44;73&#8239;m2&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0120" class="elsevierStyleSimplePara elsevierViewall">Las ecuaciones CKD-EPI de 2012 con creatinina y&#47;o cistatina C sobrestiman el FG de forma muy marcada en estadios 1 y 2 de la enfermedad renal cr&#243;nica&#44; por lo que en ellos ser&#237;a recomendable emplear la ecuaci&#243;n MDRD&#46; La t&#233;cnica de referencia empleada para medir el FG parece tener una influencia muy importante en el sesgo de las ecuaciones&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Introducci&#243;n"
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          1 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Objetivo"
          ]
          2 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Resultados"
          ]
          3 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Conclusiones"
          ]
        ]
      ]
    ]
    "NotaPie" => array:1 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0025">Please cite this article as&#58; Borrego Utiel FJ et al&#46; Comparaci&#243;n de las ecuaciones MDRD y de las antiguas ecuaciones CKD-EPI frente a las nuevas ecuaciones CKD-EPI en pacientes con trasplante renal cuando se emplea <span class="elsevierStyleSup">51</span>Cr-EDTA para medir el filtrado glomerular&#46; Nefrologia&#46; 2020&#59;40&#58;53&#8211;65&#46;</p>"
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      0 => array:1 [
        "seccion" => array:1 [
          0 => array:3 [
            "apendice" => "<p id="par0200" class="elsevierStylePara elsevierViewall">The equations used to estimate glomerular filtration rate &#40;eGFR&#41; based on serum creatinine levels &#40;Cr&#41; standardized to IDMS and&#47;or serum cystatin C levels &#40;CystC&#41; are detailed below&#46;<elsevierMultimedia ident="fig0050"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "identificador" => "sec0055"
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        ]
      ]
    ]
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        "etiqueta" => "Fig&#46; 1"
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        "mostrarFloat" => true
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Relationship between the measured glomerular filtration rate &#40;mGFR&#41; &#40;plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; with values of creatinine &#40;A&#41; and cystatin C &#40;B&#41;&#46; Both sexes are shown separately&#46; mGFR is expressed as logarithm&#46;</p>"
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        "etiqueta" => "Fig&#46; 2"
        "tipo" => "MULTIMEDIAFIGURA"
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          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the MDRD-IDMS and the measured glomerular filtration rate &#40;mGFR&#41; using plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B &#41; The relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-Cr and the measured glomerular filtration rate &#40;mGFR&#41; using plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
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          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41; with the MDRD-IDMS equation &#40;eGFRMDRD-IDMS minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the mGFR using plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41; with the MDRD-IDMS &#40;eGFRMDRD-IDMS minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the estimated values of eGFR MDRD-IDMS&#46;</p>"
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        "tipo" => "MULTIMEDIAFIGURA"
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        "figura" => array:1 [
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          "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41;with the CKD-EPI-Cr equation &#40;eGFR CKD-EPI-Cr minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the measured glomerular filtration rate mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41;with the CKD-EPI-Cr equation &#40;eGFR CKD-EPI-Cr minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the estimated values eGFR CKD-EPI-Cr&#46;</p>"
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        "etiqueta" => "Fig&#46; 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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            "Alto" => 1055
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            "Tamanyo" => 202180
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          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; Relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CystC equation and the measured glomerular filtration rate &#40;mGFR&#41; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; Relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the the CKD-EPI-Cr&#8239;&#43;&#8239;CystC equation and the measured glomerular filtration rate &#40;mGFR&#41; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
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        "etiqueta" => "Fig&#46; 6"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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            "imagen" => "gr6.jpeg"
            "Alto" => 969
            "Ancho" => 2351
            "Tamanyo" => 193682
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            "identificador" => "at0030"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The relationship between bias of estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CystC equation &#40;eGFR KD-EPI-CystC minus the mGFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; &#40;A&#41; versus the mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA clearance and B&#41; versus the estimated eGFR CKD-EPI-CystC values&#46;</p>"
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        "identificador" => "fig0035"
        "etiqueta" => "Fig&#46; 7"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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            "imagen" => "gr7.jpeg"
            "Alto" => 1054
            "Ancho" => 2350
            "Tamanyo" => 208893
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            "identificador" => "at0035"
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          "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The relationship between bias of estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CR&#8239;&#43;&#8239;CystC equation &#40;eGFR KD-EPI-CR&#8239;&#43;&#8239;CystC minus the mGFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; &#40;A&#41; versus the mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA clearance and B&#41; versus the estimated eGFR CKD-EPI- Cr&#8239;&#43;&#8239;CystC values&#46;</p>"
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        "etiqueta" => "Fig&#46; 8"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr8.jpeg"
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            "Ancho" => 2083
            "Tamanyo" => 179538
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        "descripcion" => array:1 [
          "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Percentage of patients reclassified in different CKD stages according to the eGFR equation used&#46;</p>"
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        "identificador" => "fig0045"
        "etiqueta" => "Fig&#46; 9"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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            "imagen" => "gr9.jpeg"
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            "Ancho" => 2083
            "Tamanyo" => 191666
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        "descripcion" => array:1 [
          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Percentage of patients correctly classified in the CKD Stage by the different equations to calculate eGFR&#46;</p>"
        ]
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        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
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          "leyenda" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">BMI&#58; body mass index&#46;</p><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Continuous numerical variables are presented as mean&#8239;&#177;&#8239;standard deviation and the ranges are shown within parentheses&#46; Qualitative variables are presented in absolute count and in brackets the relative frequencies&#46;</p>"
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            0 => array:2 [
              "tabla" => array:1 [
                0 => """
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">N&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">270&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age &#40;years&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">47&#8239;&#177;&#8239;14 &#40;18&#8211;72&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sex &#40;men&#47;women&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">175&#47;95 &#91;64&#46;8&#37;&#47;35&#46;2&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Diabetics&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12 &#91;4&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Weight &#40;kg&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">74&#46;8&#8239;&#177;&#8239;13&#46;4 &#40;42&#46;5&#8211;129&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">BMI &#40;kg&#47;m<span class="elsevierStyleSup">2</span> &#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">25&#46;6&#8239;&#177;&#8239;4&#46;2 &#40;16&#46;4&#8211;37&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Donor type&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Living&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">28 &#91;10&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Cadaveric&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">242 &#91;89&#46;6&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Prednisone&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">269 &#91;99&#46;6&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Tacrolimus&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">201 &#91;74&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Ciclosporin A&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">38 &#91;14&#46;1&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Rapamycin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17 &#91;6&#46;3&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Everolimus&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14 &#91;5&#46;2&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mycophenolate mofetil&#47;M&#46; sodium&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">255 &#91;94&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Creatinine &#40;mg&#47;dL&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42&#8239;&#177;&#8239;0&#46;46 &#40;0&#46;60&#8211;4&#46;33&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">cystatin C &#40;mg&#47;L&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;45&#8239;&#177;&#8239;0&#46;53 &#40;0&#46;42&#8211;3&#46;48&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mGFR &#40;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> &#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;0&#8239;&#177;&#8239;11&#46;4 &#40;18&#46;2&#8211;79&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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        ]
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        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
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          0 => array:3 [
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        "tabla" => array:3 [
          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">mGFR&#58; GFR measured with <span class="elsevierStyleSup">51</span>Cr-EDTA&#59; Bias&#58; eGFR &#8211; mGFR&#46; &#59; eGFR&#58; GFR estimated with equation&#59; Values are expressed in mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; SD&#58; Standard Deviation&#59; IQR&#58; interquartile range&#44; RMSE&#58; root mean square error or mean square error&#46; P10 or P30&#58; percentage of patients included in a range of 10&#37; or 30&#37; of the value of measured mGFR&#46;</p>"
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Method&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mean&#8239;&#177;&#8239;SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Bias&#8239;&#177;&#8239;SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">IQR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">RMSE&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">P10<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">&#40;d&#41;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">P30<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">&#40;d&#41;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Total &#40;n&#8239;&#61;&#8239;270&#41;</span> &#58;</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mGFR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;0&#8239;&#177;&#8239;11&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;MDRD-IDMS&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;1&#8239;&#177;&#8239;17&#46;1 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#46;1&#8239;&#177;&#8239;12&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;6&#8239;&#177;&#8239;28&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">48&#46;5&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">59&#46;3&#8239;&#177;&#8239;20&#46;0 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;4&#8239;&#177;&#8239;14&#46;8 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;0&#8239;&#177;&#8239;28&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#44;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#46;0&#8239;&#177;&#8239;26&#46;9 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;0&#8239;&#177;&#8239;23&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;9&#8239;&#177;&#8239;40&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#44;0&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#46;9&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">57&#46;2&#8239;&#177;&#8239;21&#46;4 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#46;1&#8239;&#177;&#8239;16&#46;7 &#40;c&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;9&#8239;&#177;&#8239;29&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">mGFR&#8239;&#8805;&#8239;60 &#40;n&#8239;&#61;&#8239;22&#41;</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t">mGFR&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">eGFR &#40;CKD-EPI-CystC&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">18&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">57&#46;6&#37;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">33&#46;3&#8239;&#177;&#8239;11&#46;5<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">&#40;b&#41;</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">54&#46;5&#37;&nbsp;\t\t\t\t\t\t\n
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            1 => array:3 [
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          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Performance of the equations for estimation of glomerular filtration rate in kidney transplants patients&#46; Comparison with the measured GFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
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                  \t\t\t\t">72&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">90&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">98&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">22&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">60&#46;9&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">95&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">17&#46;8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">64&#46;9&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">81&#46;8&nbsp;\t\t\t\t\t\t\n
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          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Sensitivity &#40;S&#41;&#44; specificity &#40;SP&#41;&#44; positive predictive value &#40;PPV&#41; and negative predictive value &#40;NPV&#41; of the different eGFR estimation equations to detect GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#44; 73&#8239;m<span class="elsevierStyleSup">2</span> measured with the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
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                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t" scope="col">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">All CKD stages</th><th class="td" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Concordance &#40;95&#37; CI&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;23 &#40;0&#46;13&#59; 0&#46;33&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;002&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;26 &#40;0&#46;16&#59; 0&#46;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60; 0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t"><span class="elsevierStyleBold">eGFR &#40;CKD-EPI-Cr&#41;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;08 &#40;0&#46;01&#59; 0&#46;16&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#44;012&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;19 &#40;0&#46;11&#59; 0&#46;27&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60; 0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
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Original article
Comparison of MDRD equations and the old equations CKD-EPI against new equations CKD-EPI in patients with kidney transplantation when used 51Cr-EDTA to measure glomerular filtration
Comparación de las ecuaciones MDRD y de las antiguas ecuaciones CKD-EPI frente a las nuevas ecuaciones CKD-EPI en pacientes con trasplante renal cuando se emplea 51Cr-EDTA para medir el filtrado glomerular
Francisco José Borrego Utiela,
Corresponding author
fborregou@senefro.org

Corresponding author.
, Angel Miguel Ramírez Navarrob, Rafael Esteban de la Rosac, Juan Antonio Bravo Sotoc
a Unidad de Gestión Clínica de Nefrología, Complejo Hospitalario de Jaén, Jaén, Spain
b Unidad de Gestión Clínica de Medicina Nuclear, Hospital Regional Universitario Virgen de las Nieves, Granada, Spain
c Unidad de Gestión Clínica de Nefrología, Hospital Regional Universitario Virgen de las Nieves, Granada, Spain
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and in kidney transplant patient&#46; The markers chosen to develop these equations have been serum creatinine and cystatin C &#59; the serum concentration of these substances may be modified by factors other than GFR in kidney transplant patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;5</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The CKD-EPI equations using creatinine values were developed to reduce the systematic underestimation that occurred in the GFR with the MDRD study equation when applied to younger population&#44; female sex and lower serum creatinine levels&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> In the development of the CKD-EPI equation in 2009&#44; patients with higher GFR values were included&#44; which allowed improving the estimates with respect to the MDRD equation&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;8</span></a> This improvement is mainly due to the categorization of the population into 4 different groups&#44; which are represented by 4 equations that use the same variables&#44; but with different exponents for a given serum value of creatinine depending on whether they are greater or lower than 0&#46;7&#8239;mg&#47;dL in women and 0&#46;9&#8239;mg&#47;dL in men&#46; In 2012&#44; the same group of researchers tried to improve the predictions of the equations by incorporating cystatin C as a second marker of the GFR&#44; observing a slight improvement in its predictive capacity&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> However&#44; the authors themselves concluded that the equations with cystatin C and creatinine should not be used routinely but rather to confirm the existence of kidney failure&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">The studies that have analyzed the validity of the MDRD and CKD-EPI equations of 2009 in kidney transplant patients have obtained not uniform results&#44; some found a lower bias using CKD-EPI with respect to direct GFR measurement&#44; while others continue to observe a greater overestimation of the GFR with this CKD-EPI equation&#44;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;3&#44;10&#44;11</span></a> and less biases with the use of MDRD formula&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> It has been observed that the CKD-EPI equation from 2012&#44; has less bias that the MDRD&#46;<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> In addition it was demonstrated that this estimation of GFR in transplanted patients correlated with different risk factors&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The objective of the present study is to analyze the degree of agreement between the estimated GFR using the MDRD&#44; CKD-EPI equations 2009 and the new equations CKD-EPI 2012 and the GFR measured by the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA in kidney transplant patients in a stable clinical condition&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">This is a retrospective study that included kidney transplant patients from the Kidney Transplant Unit of the Regional University Hospital &#34;Virgen de las Nieves&#34; in Granada&#46; Patients were over 18 years of age&#44; with stable kidney function and had a measurement of the GFR one year after transplantation&#46; Of the 556 available measurements&#44; those who had simultaneous determinations of serum creatinine and cystatin C were selected&#59; only one determination per patient was included in the study&#46; Therefore&#44; for the analysis we finally have 270 GFR measurements with <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">The measurement of GFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA &#40;mGFR&#41; was performed by the Nuclear Medicine Service&#46; After a single injection of a standardized dose of the tracer&#44; blood was drawn at 120 and 240&#8239;min&#44; and the GFR was calculated according to the technique used by Br&#246;chner-Mortensen&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> The values obtained were corrected for the body surface using the DuBois and DuBois formula&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> The supplementary information &#40;appendix 1&#41; shows the equations used to calculate the GFR &#40;eGFR&#41; &#58; MDRD4 2006 traceable to IDMS &#40;eGFR MDRD-IDMS&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> CKD-EPI of creatinine 2009 &#40;eGFR CKD-EPI-Cr&#41;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> and CKD-EPI with creatinine and cystatin C from 2012 &#40;eGFR CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The values of serum creatinine and cystatin C obtained on a date coinciding with the measurement of GFR <span class="elsevierStyleSup">51</span>Cr-EDTA were recorded&#46; Serum Creatinine was measured using the Jaff&#233; technique compensated with traceability to IDMS &#40;Beckman Coulter AU5832 autoanalyzer&#41;&#46; For a concentration of 0&#46;78&#8239;mg&#47;dL the coefficient of variation was 3&#46;1&#37;&#44; 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An analysis using multiple linear regression analysis was conducted where the mGFR was the dependent variable and the independent variables were&#58; sex&#44; age&#44; serum creatinine and cystatin C values to explain the degree of the variance of mGFR with the variables introduced&#46; The values were analyzed with or without logarithmic transformation as was done in the design of the original equations of MDRD and CKD-EPI&#46; A p&#8239;&#60;&#8239;0&#46;05 was considered statistically significant&#46; Statistical analysis was performed using SPSS version 22 program&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">The accuracy of the estimates was expressed by absolute bias&#44; mean square error &#40;root mean square error&#44; RMSE&#41; and with the values P30 and P10&#46; The absolute bias was calculated as the arithmetic mean of the differences between eGFR and mGFR&#46; Since the differences may result to be positive or negative&#44; the deviation between the two measures may not properly reflect the bias&#44; thus we also calculated the RMSE done as follows&#58; first the differences between eGFR and mGFR were calculated&#44; then the resulting value was squared transformed&#44; the values were added&#44; the arithmetic mean was obtained and finally its square root is calculated&#46; The standard deviation of these squared differences were also calculated&#46; The P30 and P10 values were calculated as the percentage of patients whose eGFR value was included in the 30&#37; or 10&#37; above or below the mGFR reference value&#46; The precision was estimated by the standard deviation of the absolute bias&#44; with the square root of the mean square deviation and with the interquartile range of the bias&#46; The interquartile range was obtained by subtracting third quartile and first quartile of the absolute bias values&#46; Finally&#44; we calculated the values of sensitivity&#44; specificity&#44; negative predictive value&#44; positive predictive value and degree of concordance &#40;kappa&#41; for detection of a GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with eGFR equations&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0050" class="elsevierStylePara elsevierViewall">There were 270 patients included in the study and each patient had the measurement GFR using <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows the epidemiological data&#44; the serum creatinine and cystatin C values and the mGFR of the study population&#46; A 99&#46;6&#37; of patients were on prednisone &#40;3&#46;5&#8722;5&#8239;mg&#47;day in 95&#37; of the cases&#41; when the GFR was measured&#46; Most patients had received cadaveric graft &#40;89&#46;6&#37;&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">The relationship between mGFR and serum concentration of creatinine and cystatin C</span><p id="par0055" class="elsevierStylePara elsevierViewall">The mGFR &#40;log&#41; correlated significantly with the serum creatinine values &#40;r &#61; -0&#46;64&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#44; <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>-A&#46; It is observed that the values of mGFR were higher in men&#44; and degree of correlation was also greater in male than females &#40;males r &#61; -0&#46;75&#44; p&#8239;&#60;&#8239;0&#46;001&#59; women r&#8239;&#61;&#8239;0&#46;58&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#46; The mGFR &#40;log&#41; also correlated with cystatin C levels although the r value lower than with Creatinine &#40;r&#8239;&#61;&#8239;&#8722;0&#46;52&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#44; and it there was not a difference between males and females &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>- B&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">Generation of linear regression models for mGFR as dependent variable using as predictive variables&#58; sex&#44; age&#44; creatinine and cystatin C as in the formulas published for CKD-EPI of 2009 and 2012 show multiple correlation coefficients about 0&#46;73 &#40;p&#8239;&#60;&#8239;0&#46;001&#41; with adjusted R<span class="elsevierStyleSup">2</span> values of 0&#46;52&#46; Therefore&#44; in our kidney transplanted population&#44; only half of the GFR variability measured with the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA is explained by regression models that use these variables&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Comparison of mGFR with eGFR MDRD-IDMS and CKD-EPI-Cr</span><p id="par0065" class="elsevierStylePara elsevierViewall">The estimated eGFR using the MDRD-IDMS and CKD-EPI-Cr equations was significantly higher &#40;p&#8239;&#60;&#8239;0&#46;001&#41; than the mGFR &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; The degree of correlation of eGFR obtained by the two methods and mGFR was similar&#44; and the dispersion of values appear to be very similar &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> A&#44;B&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">The absolute bias of eGFR CKD-EPI-Cr was higher than the bias obtained with MDRD-IDMS in the entire group and in each of the subgroups considering the stages of CKD&#46; Bias of eGFR CKD-EPI-Cr was considerable in the group with mGFR &#8805;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> which became almost 9&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> higher than the MDRD-IDMS&#46; Considering RMSE&#44; note that with MDRD-IDMS the values obtained were the lowest for all CKD stages&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">The degree of overestimation of the GFR obtained with the equation eGFR MDRD-IDMS and CKD-EPI-Cr was not related to the mGFR value of &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> A and <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a> A&#41;&#46; However&#44; the overestimation was significantly correlated with the estimated value of eGFR MDRD-IDMS and CKD-EPI-Cr &#40;r&#8239;&#61;&#8239;0&#46;75 and r&#8239;&#61;&#8239;0&#46;83 respectively &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>B and <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>B&#41;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Comparison of mGFR and eGFR CKD-EPI-CystC and eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC</span><p id="par0080" class="elsevierStylePara elsevierViewall">The values of eGFR with CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC were greater &#40;p&#8239;&#60;&#8239;0&#46;001&#41; than the mGFR &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#44; being also greater than eGFR MDRD-IDMS globally&#46; In CKD stage 4 the bias was inferior than that of MDRD-IDMS and higher in the other stages&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">The bias of eGFR CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC was lower than CKD-EPI-Cr for all stages of CKD&#46; The bias of eGFR CKD-EPI-CystC had a greater dispersion of values as compared with CKD-EPI-Cr&#8239;&#43;&#8239;CystC and with the rest of the eGFR equations&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> shows the relationship between eGFR CKD-EPI-CystC and mGFR &#40;A&#41; and CKD-EPI-CystC shown CystC&#8239;&#43;&#8239;Cr and mGFR &#40;B&#41; with correlation coefficients of 0&#46;49 and 0&#46;64&#44; respectively&#46; Some values of eGFR predicted with the CKD-EPI-CystC equation were excessively high with respect to mGFR&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0090" class="elsevierStylePara elsevierViewall">The RMSE values were higher with CKD-EPI-CystC than those obtained with the other 3 equations globally&#44; except in CKD stage 4 that was similar to eGFR CKD-EPI-Cr&#46; The RMSE value CKD-EPI-CystC&#8239;&#43;&#8239;Cr was higher than MDRD-IDMS in all CKD stages except 4&#46; Interquartile range of eGFR MDRD-IDMS bias was the smallest in all stages of CKD&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The bias of eGFR CKD-EPI-CystC did not correlate with the mGFR value &#40; <a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>A&#41; but did show a strong correlation &#40;r&#8239;&#61;&#8239;0&#46;91&#41; with the eGFR CKD-EPI-CystC &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>B&#41;&#46; The bias of the eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC did not correlate with mGFR &#40;<a class="elsevierStyleCrossRef" href="#fig0035">Fig&#46; 7</a>A&#41; but it highly correlated with eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC &#40;r&#8239;&#61;&#8239;0&#46;85&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0035">Fig&#46; 7</a>B&#41;&#46; With both equations there were important overestimations as compared with mGFR that became even more than 80&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with CKD-EPI-CystC and above 70&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> with CKD- EPI-Cr&#8239;&#43;&#8239;CystC&#41;&#46;</p><elsevierMultimedia ident="fig0030"></elsevierMultimedia><elsevierMultimedia ident="fig0035"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Comparison of accuracy values of P10 and P30&#44; sensitivity&#44; specificity and predictive values</span><p id="par0100" class="elsevierStylePara elsevierViewall">A mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> was observed in 91&#46;9&#37; of the transplanted patients&#44; while with MDRD-IDMS and CKD-EPI-Cr it was found in 67&#37; and 56&#46;3&#37; respectively and with CKD-EPI-CystC and CKD-EPI-CystC&#8239;&#43;&#8239;Cr in in 61&#46;1&#37; and 57&#46;8&#37; respectively&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> show the precision values of P10 and P30 for the four equations of eGFR&#46; The P30 values were similar for MDRD-IDMS&#44; CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC and were greater than CKD-EPI-Cr in stages 1&#8239;&#43;&#8239;2 and stage 3&#44; while in stage 4 the P30 values were higher for CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The P10 values were higher with CKD-EPI-CystC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC in virtually all stages as compared with MDRD-IDMS and CKD-EPI-Cr&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">As far as ability to detect an GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> it was noted that MDRD-IDMS shows most sensitivity &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41; with a specificity somewhat lower than CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The positive predictive value was similar in all equations&#44; while the negative predictive value was slightly higher with MDRD-IDMS&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Concordance according to CKD stages</span><p id="par0110" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> shows the degree of concordance of the equations of eGFR in the classification of all stages of CKD and in assessment of GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; The coefficients kappa were low for all equations&#44; the highest coefficient corresponded to the MDRD-IDMS equation followed by CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46; The coefficients were somewhat higher in the assessment of patients with mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46;</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">The low concordance was mainly due to a change in classification of patients from stages 2&#44; 3 and 4 with mGFR to stages 1&#44; 2 and 3 with the eGFR equations &#40;<a class="elsevierStyleCrossRef" href="#fig0040">Fig&#46; 8</a>&#41;&#46; The percent of patients misclassified were low for MDRD-IDMS in stages 2 and 3 and higher in stage 4&#59; misclassification was lower for the stage 4 with CKD-EPI-Cr&#8239;&#43;&#8239;CystC and CKD-EPI-CystC&#46; <a class="elsevierStyleCrossRef" href="#fig0045">Fig&#46; 9</a> shows the percentage of correct classification of CKD stages 2&#44; 3 and 4 using the four eGFR equations&#59; MDRD-IDMS classified more patients correctly in stages 2 and 3 and that CKD-EPI-Cr&#8239;&#43;&#8239;CistC or CKD-EPI-CistC were more efficient for the classification of patients in stage 4&#46;</p><elsevierMultimedia ident="fig0040"></elsevierMultimedia><elsevierMultimedia ident="fig0045"></elsevierMultimedia></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Discussion</span><p id="par0120" class="elsevierStylePara elsevierViewall">Serum creatinine &#40;Cr&#41; is the main marker of kidney function &#40;GFR&#41; being used in daily clinical practice&#46; But&#44; the relationship between serum Cr and GFR is not as close as one would expect&#44; especially in kidney transplants&#46; In our kidney transplant patients after one year of follow up&#44; only about 50&#37; of the change in mGFR could be predicted by serum creatinine&#44; cystatin C&#44; sex and age&#44; indicating that there are other very influential beyond our control variables&#46; The same problem arises with the different equations to estimate GFR published in the literature&#46; One point has to be taken into consideration&#44; in kidney transplants the serum creatinine levels have a different relationship with GFR than in non-transplanted patients&#44; which causes a overestimation of the actual kidney function calculated with these equations&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;10</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">Steroids modify the serum creatinine&#47;muscle mass ratio and may cause a discrepancy between serum Cr levels and kidney function&#44; however other factors may be involved&#46; Some studies indicate that in patients not taking steroids there is less overestimation of GFR however this effect is not that evident with time after transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> Other authors have failed to show an effect of steroids<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;14</span></a> and some authors consider that the low dose of steroids used in long-term patients should not have an effect on serum creatinine&#47;muscle mass ratio&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> In our study&#44; most patients were on a very low doses of prednisone&#44; so we could not analyze a discrepancy between serum creatinine values and the GFR&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">cystatin C is potentially a better marker than creatinine for assessment of GFR because it is less influenced by age&#44; gender&#44; race&#44; body weight or muscle mass&#46; In our case&#44; the correlation of mGFR with cystatin C was lower than with creatinine&#44; but it was observed that with cystatin C the differences related to sex were corrected &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>B&#41;&#46; Also it has been reported that cystatin C levels can be discretely raised by high doses o of steroids&#44; without a true change in GFR&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#44;14</span></a> The use of trimetroprim may affect the calculation of GFR although it would have less impact on long-term stable transplantation&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Thyroid hormones may decrease serum creatinine levels and raise cystatin C levels&#59; most transplant patients should not have thyroid pathology&#44; so the overall impact must be low&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0135" class="elsevierStylePara elsevierViewall">Since the arrival of the MDRD-IDMS equation&#44; several equations have been published by the CKD-EPI group to trying to improve the estimation of the GFR&#46; Publications on the performance of these equation show nonuniform results&#46;&#46; Stevens et al&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> evaluated in kidney transplants of the CKD-EPI equation of 2009&#44; observing that the overall bias was lower with CKD-EPI than with MDRD-IDMS formulas&#44; with small differences between transplanted and non-transplanted&#44; which was considered of low impact&#46; However&#44; the graphics revealed that at for a GFR&#8239;&#8805;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; the overestimation with CKD-EPI is greater than 10&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; which is more bias than with MDRD-IDMS equation&#46; For GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; the bias is marginal and similar for both equations&#46; In patients with a body mass index lower than 20&#8239;kg&#47;m<span class="elsevierStyleSup">2</span>&#44; all the equations performed poorly&#44; so it was suggested using body weight to improve predictions&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Therefore&#44; in this study there were doubts about whether it was convenient the application of the CKD-EPI-Cr equation in kidney transplant patients with high GFR&#46; Subsequent comparative studies in kidney transplant recipients also found greater precision and less bias with CKD-EPI-Cr than with MDRD-IDMS<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20&#8211;23</span></a> with a few studies supporting it&#46;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> In 2012 Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> published the new CKD-EPI equations that included creatinine and cystatin C&#59; the kidney transplanted population was not included because they had many other not well defined factors that may affect the results&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">Our study observed a significant overestimation of GFR in kidney transplants patients using the MDRD-IDMS and the CKD-EPI equations of 2009 and 2012 as compared with the GFR measured as the plasma clearance with <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; The average bias for MDRD-IDMS was 11&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> followed by 14&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD- EPI-Cr&#8239;&#43;&#8239;CystC&#44; and it was greater for the other equations&#46; The bias obtained in our study is greater than the observed in previous studies in kidney transplant patients which varied for CKD-EPI-Cr from &#8722;4&#46;5&#8211;8&#46;1&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span><a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11&#44;20</span></a> although others reached values similar to ours&#46; With the new 2012 CKD-EPI equations&#44; Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> found in kidney transplants a bias of &#8722;0&#46;54&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-Cr&#8239;&#43;&#8239;CystC and &#8722;2&#46;82&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-CystC&#44; which is less bias than MDRD-IDMS and CKD-EPI-Cr&#46; However&#44; Keddis et al&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> observed a bias of &#8722;5&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> and &#8722;8&#46;7&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; for CKD-EPI-Cr&#8239;&#43;&#8239;CystC and CKD-EPI-CystC respectively and &#8722;1&#46;9 for MDRD-IDMS&#46; Kukla et al&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> found a bias of &#43;9&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#44; &#43;8&#46;6&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI-CystC and 6&#46;4&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for MDRD-IDMS&#46; This result of a lower bias for MDRD-IDMS is similar to the results reported in the present article&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">In the present article&#44; the bias observed correlated with the estimated value obtained with all the equations as shown in <a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3&#44;4&#44;6</a> and 7&#44; being particularly striking with the equations CKD-EPI-CystC and CKD-EPI -Cr&#8239;&#43;&#8239;CystC&#46; The bias observed did not correlate with the mGFR obtained with plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; The use of the cystatin C equations in our study overestimated kidney function in patients with higher GFR&#44; so reducing the percentage of patients correctly classified as seen in g&#161;fgures 8 and 9&#46; In these patients the use of the MDRD-IDMS equation could be more reliable since it is more conservative&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;22&#44;24</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">The differences observed in the biases between different series are usually attributed to the demographic variable itself and to possible differences in the techniques of measurement of creatinine and cystatin C if they are not standardized&#44; putting little emphasis on the method used to measure the GFR or the fact that they are kidney transplanted patients&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> In a study that compared the bias in a healthy population &#40;kidney donors&#41; with a population of transplanted patients with renal insufficiency&#44; it was observed that age greatly influenced the degree of bias&#44; with kidney transplants always less than healthy individuals&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">Published studies on kidney transplants have used different techniques for measuring GFR&#58; plasma clearance of inulin&#44; iohexol&#44; iothalamate&#44; or plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA or <span class="elsevierStyleSup">99m</span>Tc-DTPA that although they exhibit a correlation coefficients exceeding 0&#46;9 with respect to the renal clearance of inulin as standard&#44; each technique has its own bias&#46; Thus&#44; it is somewhat difficult to compare results from different studies with different methodologies and conclusions are not easy to obtain&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> For example&#44; GFR values obtained with the renal clearance of inulin are low&#46; Plasma clearance of <span class="elsevierStyleSup">99m</span>Tc-DTPA or <span class="elsevierStyleSup">51</span>Cr-EDTA provides higher values of GFR than the inulin renal clearance&#46;<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a> In the work of Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> the equations were developed using urinary or plasma clearance of iothalamate and for validation it was used a population in which a wide variety of methods were used to measure GFR&#44; so the biases found are an average of what one would expect when comparing populations with different techniques&#46; To validate the equation CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#44; Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> used urinary clearance of inulin while Keddis et al&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> and Meeusen et al&#46;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> used iothalamate clearance&#46; The population of Keddis and Meeusen had a similar average GFR&#44; with similar sex ratio and average age &#40;they come in part from the same data source&#44; Mayo Clinic Foundation&#41;&#46; The population of Masson et al&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> had were relatively young with a higher proportion of men and patients with &#60; 15&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> were excluded&#59; and despite of this in their series the average GFR was 5&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> lower for similar values of creatinine and cystatin C&#46; Our population was younger&#44; proportion of men was similar&#44; somewhat lower serum levels of creatinine and cystatin C despite of which we obtained a GFR 7-12&#8239;mL&#47;min&#47;1&#46;73&#8239;m <span class="elsevierStyleSup">2</span> lower than the other series&#44; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA to measure GFR&#46; This could explain that our biases were greater than in these mentioned series and that were similar to those observed in other studies&#46;<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">19&#44;26</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">The bias as an adjustment parameter of 2 techniques just calculates the differences between the 2 measurements&#44; and arithmetic mean was then calculated&#46; Linear regression generates an equation seeking to minimize residuals&#46; Large positive differences will be canceled by large negative differences&#44; with the appearance that both techniques fit very well if the bias obtained is close to zero&#46; However&#44; considering the standard deviation or the interquartile range of bias&#44; the impact of these large differences on the overall adjustment are better described&#58; the greater the bias deviation&#44; the greater the mistake made in many individuals&#46; The standard deviation of the bias obtained by the different studies ranges fom 10&#8211;20&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; similar to the data obtained in our study and the analysis of the Bland-Altman graphs of the different studies reveals that there is a large dispersion in the differences of the individual data&#46; <a class="elsevierStyleCrossRefs" href="#fig0015">Figs&#46; 3&#44;4&#44;6&#44;7</a> shows that for a specific eGFR value from each equation the error made grows significantly in patients with GFR&#8239;&#62;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; reaching differences of more than 80&#8211;100&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for equations using cystatin C&#46; The study by Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> also mentioned that the equation that only includes cystatin C provides worse results than if used with creatinine&#46;</p><p id="par0165" class="elsevierStylePara elsevierViewall">Some authors prefer to use of the values of mean square deviation or RMSE to compare different equations&#44; because they better reflect individual differences by computing all differences as positive and averaging them&#46; In our study&#44; with MDRD-IDMS there were obtained lower RMSE values that were similar to the other equations in patients with stage 4 CKD&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">The accuracy values P30 and P10 observed in our population were much lower for all the equations than those observed in other series&#44; being less than 40&#46;9&#37; and 59&#46;1&#37; respectively&#46; In other series&#44; values of 77&#8211;84 &#37; are found when moving from MDRD-IDMS to CKD-EPI<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;11</span></a> and they increase from 80&#46;4&#37;&#8211;86&#46;5&#37; when moving from MDRD-IDMS to CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> Inker et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> found P30 values that improved discreetly from 87&#46;2&#37; with CKD-EPI-Cr to 91&#46;5&#37; with CKD-EPI-Cr&#8239;&#43;&#8239;CystC when GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> and rose from 92&#46;2 &#37; at 97&#46;7&#37; when GFR&#8239;&#62;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; This apparent lack of performance in our population could be explained if we assume that the GFR with <span class="elsevierStyleSup">51</span>Cr-EDTA yields measurements between 5 and 10&#8239;mL&#47;min lower than other GFR measurement techniques&#46; In any case&#44; in our data it seems that the degree of improvement achieved in P30 with CKD-EPI-Cr&#8239;&#43;&#8239;CistC is not quantitatively important with respect to the other equations that use only creatinine&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">Our study analyzed the performance of the different equations to detect a GFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; It was observed that the MDRD-IDMS equation showed the highest sensitivity&#44; while the highest specificity was obtained with the CKD-EPI-Cr&#8239;&#43;&#8239;CystC equation&#44; which is expected since the latter results in estimates greater than MDRD-IDMS and when creatinine and cystatin C levels are sufficiently elevated&#44; the estimate for GFR are clearly below 60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; Positive predictive values are similar for all equations while negative predictive values are slightly better for CKD-EPI equations&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">A strong positive aspect of the present study is the measurements of GFR in all patients one year after receiving the graft&#44; which adds homogeneity to the study population&#46; In most series&#44; the measurements of GFR were obtained at different times during the follow up after the kidney transplant&#44; the relationship between muscle mass&#44; nutritional status and steroid dose may change with time&#44; therefore relationship between serum creatinine and the degree of function may not be the same throughout the time after the transplant&#46; A limitation of our study is not being able to analyze the influence of the steroid dose&#44; because the most patients were on prednisone at low doses as the maintenance treatment&#44; although this also brings homogeneity to the population analyzed&#46; It would be of interest to compare in patients with the same period after transplant&#44; 2 different measurement of GFR using different techniques and evaluate the differences in the bias between the equations CKD-EPI and MDRD-IDMS and the mGFR measured by different techniques&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">In summary&#44; in the kidney transplant population the GFR estimated by the CKD-EPI equations that use serum creatinine and cystatin C as markers of kidney function&#44; show a greater bias than the MDRD-IDMS equation and the GFR is measure by plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; These equations produce a significant overestimation of the GFR if the eGFR is greater than 60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; however the the error obtained is reduced below the observed with the MDRD-IDMS equation if the GFR is less than 30&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46; The degree of precision with all the equations was low in all stages of the CKD&#46; It is important to take into consideration the reference technique used to measure GFR in kidney transplants when analyzing the performance of the different MDRD-IDMS or CKD-EPI equations&#46;</p><p id="par0201" class="elsevierStylePara elsevierViewall">The technique to measure GFR may explain the discrepancies between different reports in the literature comparing the degree of precision of the different equations&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conflict of interests</span><p id="par0195" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest in relation with the contents of this article&#46;</p></span></span>"
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              "identificador" => "sec0020"
              "titulo" => "The relationship between mGFR and serum concentration of creatinine and cystatin C"
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              "identificador" => "sec0025"
              "titulo" => "Comparison of mGFR with eGFR MDRD-IDMS and CKD-EPI-Cr"
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              "titulo" => "Comparison of mGFR and eGFR CKD-EPI-CystC and eGFR CKD-EPI-Cr&#8239;&#43;&#8239;CystC"
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              "identificador" => "sec0035"
              "titulo" => "Comparison of accuracy values of P10 and P30&#44; sensitivity&#44; specificity and predictive values"
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              "titulo" => "Concordance according to CKD stages"
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    "fechaRecibido" => "2017-11-02"
    "fechaAceptado" => "2019-07-15"
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          "palabras" => array:6 [
            0 => "Glomerular filtration rate"
            1 => "Kidney transplantation"
            2 => "MDRD"
            3 => "CKD-EPI"
            4 => "cystatin C"
            5 => "Creatinine&#46;"
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            0 => "Filtrado glomerular"
            1 => "Trasplante renal"
            2 => "MDRD"
            3 => "CKD-EPI"
            4 => "Cistatina C"
            5 => "Creatinina&#46;"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Background</span><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">When estimating the glomerular filtration rate &#40;GFR&#41; in kidney transplant patients&#44; significant differences have been found between MDRD and the 2009 CKD-EPI equations&#44; and reference techniques&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objective</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">To analyse and compare the performance of MDRD and the 2009 and 2012 CKD-EPI equations against 51Cr-EDTA plasma clearance in measuring GFR in 270 kidney transplant patients after one year&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">The mean measured GFR was 43&#46;0&#8239;&#177;&#8239;11&#46;4 &#40;18&#46;2&#8211;79&#46;4&#41; ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; with creatine levels of 1&#46;42&#8239;&#177;&#8239;0&#46;46 &#40;0&#46;60&#8211;4&#46;33&#41; mg&#47;dl and cystatin C levels of 1&#46;45&#8239;&#177;&#8239;0&#46;53 &#40;0&#46;42&#8211;3&#46;48&#41; mg&#47;l&#46; This correlated moderately with creatinine &#40;r&#61;&#8211;0&#46;61&#44; p&#8239;&#60;&#8239;0&#46;001&#41; and cystatin C &#40;r&#61;&#8211;0&#46;52&#44; p&#8239;&#60;&#8239;0&#46;001&#41;&#46; Using linear regression techniques&#44; it was found that creatinine&#44; cystatin C&#44; gender and age only explained 52&#37; of GFR total variance&#46; All equations overestimated GFR&#44; with a mean bias of &#43;11&#46;1&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for MDRD&#44; &#43;16&#46;4&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for 2009-CKD-EPI&#44; &#43;15&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for CKD-EPI with cystatin C and &#43;14&#46;1&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> for 2012-CKD-EPI with creatinine and cystatin C&#46; eGFR by MDRD and the 2009 CKD-EPI equation correlated better with 51Cr-EDTA than CKD-EPI with creatinine and&#47;or cystatin C&#46; The overestimations were negatively correlated with creatinine and cystatin C levels&#44; most significantly for CKD-EPI with creatinine and&#47;or cystatin C when GFR was greater than 60&#8239;ml&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">The 2012 CKD-EPI equations with creatinine and&#47;or cystatin C significantly overestimate GFR in stage 1 and 2 chronic kidney disease&#46; The MDRD equation is therefore recommended in these cases&#46; The reference method used to measure GFR seems to heavily influence the bias of the equations&#46;</p></span>"
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            "titulo" => "Background"
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          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Objective"
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          2 => array:2 [
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            "titulo" => "Results"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducci&#243;n</span><p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Para la estimaci&#243;n del filtrado glomerular renal &#40;FG&#41; en trasplantados renales se emplean las ecuaciones MDRD y CKD-EPI de 2009 que han mostrado diferencias importantes cuando se comparan con el FG medido con t&#233;cnicas de referencia&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Objetivo</span><p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">Analizar el rendimiento de las ecuaciones MDRD&#44; CKD-EPI de 2009 y de 2012 en 270 pacientes trasplantados renales de 1 a&#241;o de evoluci&#243;n&#44; comparando con el FG medido con aclaramiento plasm&#225;tico de 51Cr-EDTA&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0115" class="elsevierStyleSimplePara elsevierViewall">El FG medido fue 43&#44;0&#8239;&#177;&#8239;11&#44;4 &#40;18&#44;2-79&#44;4&#41; mL&#47;min&#47;1&#44;73&#8239;m2&#44; con niveles de creatinina de 1&#44;42&#8239;&#177;&#8239;0&#44;46 &#40;0&#44;60-4&#44;33&#41; mg&#47;dL y de cistatina C de 1&#44;45&#8239;&#177;&#8239;0&#44;53 &#40;0&#44;42-3&#44;48&#41; mg&#47;L&#46; El FG medido se correlacion&#243; moderadamente con creatinina &#40;r&#61; -0&#44;61&#44; p&#8239;&#60;&#8239;0&#44;001&#41; y cistatina C &#40;r&#61; -0&#44;52&#44; p&#8239;&#60;&#8239;0&#44;001&#41;&#46; Empleando t&#233;cnicas de regresi&#243;n lineal observamos que creatinina&#44; cistatina C&#44; sexo y edad s&#243;lo explicaban el 52&#37; de la varianza total del FG&#46; Todas las ecuaciones sobrestimaron el FG&#44; con sesgo medio de &#43;11&#44;1&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para MDRD&#44; &#43;16&#44;4&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI de 2009&#44; &#43;15&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI con cistatina C and &#43;14&#44;1&#8239;mL&#47;min&#47;1&#44;73&#8239;m2 para CKD-EPI con creatinina y cistatina C de 2012&#46; Las estimaciones con MDRD y CKD-EPI de 2009 se correlacionaron mejor con 51Cr-EDTA que CKD-EPI con creatinina y&#47;o cistatina C&#46; Las sobrestimaciones se correlacionaron negativamente con los niveles de creatinina y cistatina C&#44; siendo m&#225;s importantes para CKD-EPI con creatinina y&#47;o cistatina C cuando el FG fue mayor de 60&#8239;mL&#47;min&#47;1&#44;73&#8239;m2&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0120" class="elsevierStyleSimplePara elsevierViewall">Las ecuaciones CKD-EPI de 2012 con creatinina y&#47;o cistatina C sobrestiman el FG de forma muy marcada en estadios 1 y 2 de la enfermedad renal cr&#243;nica&#44; por lo que en ellos ser&#237;a recomendable emplear la ecuaci&#243;n MDRD&#46; La t&#233;cnica de referencia empleada para medir el FG parece tener una influencia muy importante en el sesgo de las ecuaciones&#46;</p></span>"
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        "nota" => "<p class="elsevierStyleNotepara" id="npar0025">Please cite this article as&#58; Borrego Utiel FJ et al&#46; Comparaci&#243;n de las ecuaciones MDRD y de las antiguas ecuaciones CKD-EPI frente a las nuevas ecuaciones CKD-EPI en pacientes con trasplante renal cuando se emplea <span class="elsevierStyleSup">51</span>Cr-EDTA para medir el filtrado glomerular&#46; Nefrologia&#46; 2020&#59;40&#58;53&#8211;65&#46;</p>"
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            "apendice" => "<p id="par0200" class="elsevierStylePara elsevierViewall">The equations used to estimate glomerular filtration rate &#40;eGFR&#41; based on serum creatinine levels &#40;Cr&#41; standardized to IDMS and&#47;or serum cystatin C levels &#40;CystC&#41; are detailed below&#46;<elsevierMultimedia ident="fig0050"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Relationship between the measured glomerular filtration rate &#40;mGFR&#41; &#40;plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; with values of creatinine &#40;A&#41; and cystatin C &#40;B&#41;&#46; Both sexes are shown separately&#46; mGFR is expressed as logarithm&#46;</p>"
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          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41; with the MDRD-IDMS equation &#40;eGFRMDRD-IDMS minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the mGFR using plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41; with the MDRD-IDMS &#40;eGFRMDRD-IDMS minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the estimated values of eGFR MDRD-IDMS&#46;</p>"
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      ]
      3 => array:8 [
        "identificador" => "fig0020"
        "etiqueta" => "Fig&#46; 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 983
            "Ancho" => 2333
            "Tamanyo" => 193562
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          "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41;with the CKD-EPI-Cr equation &#40;eGFR CKD-EPI-Cr minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the measured glomerular filtration rate mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; The relationship between the bias of estimated glomerular filtration rate &#40;eGFR&#41;with the CKD-EPI-Cr equation &#40;eGFR CKD-EPI-Cr minus mGFR <span class="elsevierStyleSup">51</span>Cr-EDTA&#41; and the estimated values eGFR CKD-EPI-Cr&#46;</p>"
        ]
      ]
      4 => array:8 [
        "identificador" => "fig0025"
        "etiqueta" => "Fig&#46; 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr5.jpeg"
            "Alto" => 1055
            "Ancho" => 2352
            "Tamanyo" => 202180
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0025"
            "detalle" => "Fig&#46; "
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          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; Relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CystC equation and the measured glomerular filtration rate &#40;mGFR&#41; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46; &#40;B&#41; Relationship between the estimated glomerular filtration rate &#40;eGFR&#41; with the the CKD-EPI-Cr&#8239;&#43;&#8239;CystC equation and the measured glomerular filtration rate &#40;mGFR&#41; using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
        ]
      ]
      5 => array:8 [
        "identificador" => "fig0030"
        "etiqueta" => "Fig&#46; 6"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr6.jpeg"
            "Alto" => 969
            "Ancho" => 2351
            "Tamanyo" => 193682
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          0 => array:3 [
            "identificador" => "at0030"
            "detalle" => "Fig&#46; "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The relationship between bias of estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CystC equation &#40;eGFR KD-EPI-CystC minus the mGFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; &#40;A&#41; versus the mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA clearance and B&#41; versus the estimated eGFR CKD-EPI-CystC values&#46;</p>"
        ]
      ]
      6 => array:8 [
        "identificador" => "fig0035"
        "etiqueta" => "Fig&#46; 7"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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            "imagen" => "gr7.jpeg"
            "Alto" => 1054
            "Ancho" => 2350
            "Tamanyo" => 208893
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        "detalles" => array:1 [
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            "identificador" => "at0035"
            "detalle" => "Fig&#46; "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">The relationship between bias of estimated glomerular filtration rate &#40;eGFR&#41; with the CKD-EPI-CR&#8239;&#43;&#8239;CystC equation &#40;eGFR KD-EPI-CR&#8239;&#43;&#8239;CystC minus the mGFR using the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#41;&#44; &#40;A&#41; versus the mGFR by <span class="elsevierStyleSup">51</span>Cr-EDTA clearance and B&#41; versus the estimated eGFR CKD-EPI- Cr&#8239;&#43;&#8239;CystC values&#46;</p>"
        ]
      ]
      7 => array:8 [
        "identificador" => "fig0040"
        "etiqueta" => "Fig&#46; 8"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr8.jpeg"
            "Alto" => 1417
            "Ancho" => 2083
            "Tamanyo" => 179538
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0040"
            "detalle" => "Fig&#46; "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Percentage of patients reclassified in different CKD stages according to the eGFR equation used&#46;</p>"
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      ]
      8 => array:8 [
        "identificador" => "fig0045"
        "etiqueta" => "Fig&#46; 9"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr9.jpeg"
            "Alto" => 1303
            "Ancho" => 2083
            "Tamanyo" => 191666
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            "identificador" => "at0045"
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          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Percentage of patients correctly classified in the CKD Stage by the different equations to calculate eGFR&#46;</p>"
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      ]
      9 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
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        "detalles" => array:1 [
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          "leyenda" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">BMI&#58; body mass index&#46;</p><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Continuous numerical variables are presented as mean&#8239;&#177;&#8239;standard deviation and the ranges are shown within parentheses&#46; Qualitative variables are presented in absolute count and in brackets the relative frequencies&#46;</p>"
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              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">N&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">270&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age &#40;years&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">47&#8239;&#177;&#8239;14 &#40;18&#8211;72&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sex &#40;men&#47;women&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">175&#47;95 &#91;64&#46;8&#37;&#47;35&#46;2&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Diabetics&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12 &#91;4&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Weight &#40;kg&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">74&#46;8&#8239;&#177;&#8239;13&#46;4 &#40;42&#46;5&#8211;129&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">BMI &#40;kg&#47;m<span class="elsevierStyleSup">2</span> &#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">25&#46;6&#8239;&#177;&#8239;4&#46;2 &#40;16&#46;4&#8211;37&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Donor type&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Living&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">28 &#91;10&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Cadaveric&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">242 &#91;89&#46;6&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Prednisone&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">269 &#91;99&#46;6&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Tacrolimus&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">201 &#91;74&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Ciclosporin A&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">38 &#91;14&#46;1&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Rapamycin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17 &#91;6&#46;3&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Everolimus&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14 &#91;5&#46;2&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mycophenolate mofetil&#47;M&#46; sodium&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">255 &#91;94&#46;4&#37;&#93;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Creatinine &#40;mg&#47;dL&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42&#8239;&#177;&#8239;0&#46;46 &#40;0&#46;60&#8211;4&#46;33&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">cystatin C &#40;mg&#47;L&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;45&#8239;&#177;&#8239;0&#46;53 &#40;0&#46;42&#8211;3&#46;48&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mGFR &#40;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span> &#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;0&#8239;&#177;&#8239;11&#46;4 &#40;18&#46;2&#8211;79&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
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          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Characteristics of the population studied&#46;</p>"
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          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">mGFR&#58; GFR measured with <span class="elsevierStyleSup">51</span>Cr-EDTA&#59; Bias&#58; eGFR &#8211; mGFR&#46; &#59; eGFR&#58; GFR estimated with equation&#59; Values are expressed in mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span>&#44; SD&#58; Standard Deviation&#59; IQR&#58; interquartile range&#44; RMSE&#58; root mean square error or mean square error&#46; P10 or P30&#58; percentage of patients included in a range of 10&#37; or 30&#37; of the value of measured mGFR&#46;</p>"
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                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Method&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mean&#8239;&#177;&#8239;SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Bias&#8239;&#177;&#8239;SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">IQR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">RMSE&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">P10<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">&#40;d&#41;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">P30<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">&#40;d&#41;</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Total &#40;n&#8239;&#61;&#8239;270&#41;</span> &#58;</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mGFR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;0&#8239;&#177;&#8239;11&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;MDRD-IDMS&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;1&#8239;&#177;&#8239;17&#46;1 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#46;1&#8239;&#177;&#8239;12&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;6&#8239;&#177;&#8239;28&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;7&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">48&#46;5&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">59&#46;3&#8239;&#177;&#8239;20&#46;0 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;4&#8239;&#177;&#8239;14&#46;8 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;0&#8239;&#177;&#8239;28&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#44;8&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#46;0&#8239;&#177;&#8239;26&#46;9 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;0&#8239;&#177;&#8239;23&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;9&#8239;&#177;&#8239;40&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#44;0&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#46;9&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#8239;&#43;&#8239;CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">57&#46;2&#8239;&#177;&#8239;21&#46;4 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#46;1&#8239;&#177;&#8239;16&#46;7 &#40;c&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;9&#8239;&#177;&#8239;29&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&#46;4&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " colspan="7" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">mGFR&#8239;&#8805;&#8239;60 &#40;n&#8239;&#61;&#8239;22&#41;</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mGFR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;0&#8239;&#177;&#8239;5&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;MDRD-IDMS&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">79&#46;8&#8239;&#177;&#8239;15&#46;7 &#40;b&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;8&#8239;&#177;&#8239;15&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">22&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">19&#46;4&#8239;&#177;&#8239;23&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#44;3&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">59&#46;1&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-Cr&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">88&#46;7&#8239;&#177;&#8239;16&#46;2 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;7&#8239;&#177;&#8239;15&#46;2 &#40;a&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">26&#46;3&#8239;&#177;&#8239;27&#46;2&#40;c &#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#46;2&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45&#44;5&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">eGFR &#40;CKD-EPI-CystC&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">83&#46;7&#8239;&#177;&#8239;26&#46;0 &#40;b&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">90&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">98&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">22&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">60&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">95&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">All CKD stages</th><th class="td" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">mGFR&#8239;&#60;&#8239;60&#8239;mL&#47;min&#47;1&#46;73&#8239;m<span class="elsevierStyleSup">2</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t">0&#46;26 &#40;0&#46;16&#59; 0&#46;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#60; 0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#44;012&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;17 &#40;0&#46;08&#59; 0&#46;26&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;17 &#40;0&#46;09&#59; 0&#46;25&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60; 0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;22 &#40;0&#46;14&#59; 0&#46;30&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60; 0&#46;001&nbsp;\t\t\t\t\t\t\n
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          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Coefficients kappa of concordance in the staging of chronic kidney disease &#40;CKD&#41; using different eGFR equations&#58; MDRD-IDMS&#44; CKD-EPI-Cr&#44; CKD-EPI-CistC and CKD-EPI-Cr&#8239;&#43;&#8239;CystC in relation to the glomerular filtration measured with the plasma clearance of <span class="elsevierStyleSup">51</span>Cr-EDTA&#46;</p>"
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ISSN: 20132514
Original language: English
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