Impact of Creatinine Calibration on Performance of GFR Estimating Equations in a Pooled Individual Patient Database

ORIGINAL INVESTIGATIONS Pathogenesis and Treatment of Kidney Disease Impact of Creatinine Calibration on Performance of GFR Estimating Equations in a Pooled Individual Patient Database Lesley A. Stevens, MD, MS,1 Jane Manzi, PhD,2 Andrew S. Levey, MD,1 Jing Chen, MD, MS,3 Amy E. Deysher, BSc,1 Tom Greene, PhD,4 Emilio D. Poggio, MD,5 Christopher H. Schmid, PhD,1 Michael W. Steffes, MD, PhD,6 Yaping (Lucy) Zhang, MS,1 Frederick Van Lente, PhD,5 and Josef Coresh, MD, PhD, MHS2 Background: Variation in performance of glomerular filtration rate (GFR) estimating equations is related to variation in calibration of the creatinine assay across clinical laboratories. Study Design: Cross-sectional analysis. Setting & Participants: 6 research studies and 4 clinical populations including 5,504 participants who had GFR measured using urinary clearance of iothalamate. Measurements: Standardized serum creatinine values obtained by means of calibration to the Cleveland Clinic Research Laboratory using frozen specimens, a calibration panel, and/or survey results from the College of American Pathologists. Predictor: Noncalibrated serum creatinine assayed in research and clinical laboratories compared with standardized serum creatinine. Outcome: Difference between measured GFR versus GFR estimated from the Modification of Diet in Renal Disease (MDRD) Study and Cockcroft-Gault equations. Results: For a noncalibrated serum creatinine value of 1 mg/dL (88.4 ␮mol/L), standardized serum creatinine value was 0.07 mg/dL (6.2 ␮mol/L) less than noncalibrated values. In the pooled data set, for the MDRD Study equation, calibration improved median percentage of difference between measured and estimated GFR from 9.0% (interquartile range [IQR], 28%) to 5.8% (IQR, 28%) and improved the percentage of estimates within 30% of measured GFR (P30) from 80% to 83%. The effect of calibration was greater at higher levels of GFR and varied across studies. For the Cockcroft-Gault equation, calibration worsened the median percentage of difference from ⫺2.0% (IQR, 38%) to ⫺11.4% (IQR, 39%), and the P30, from 74% to 69%. Limitations: College of American Pathologist samples were used for calibration of clinical populations; calibration factors do not account for drift over time in the serum creatinine assay; calibration cannot account for variation in assay performance among individuals. Conclusion: Calibration improves the performance of the MDRD Study equation. After calibration, larger errors remain for GFR estimates greater than 60 mL/min/1.73 m2 (⬎1 mL/s/1.73 m2). Am J Kidney Dis 50:21-35. © 2007 by the National Kidney Foundation, Inc. INDEX WORDS: Chronic kidney disease; glomerular filtration rate; estimated glomerular filtration rate; calibration; creatinine; Modification of Diet in Renal Disease Study equation; Cockcroft-Gault equation; individual patient meta-analysis.

G

lomerular filtration rate (GFR) is the best overall index of kidney function and can be estimated in clinical practice from equations

using serum creatinine level, age, sex, race, or body size. International organizations now recommend that clinical laboratories report estimated

From 1Tufts-New England Medical Center, Boston, MA; Johns Hopkins University, Baltimore, MD; 3Tulane University, New Orleans, LA; 4University of Utah, Salt Lake City, UT; 5Cleveland Clinic, Cleveland, OH; and 6University of Minnesota, Minneapolis, MN. Received December 8, 2006. Accepted in revised form April 2, 2007. Originally published online as doi: 10.1053/j.ajkd.2007.04.004 on June 1, 2007. Because an author of this manuscript is an editor for AJKD, the peer-review and decision-making processes were handled entirely by an outside editor, Marcello Tonelli, MD, SM, University of Alberta, who served as Acting Editor-inChief. Details of the journal’s procedures for potential

editor conflicts are given in the Editorial Policies section of the AJKD Website. Presented in abstract form at the Annual Meeting of the American Society of Nephrology, San Diego, CA, November 16, 2006. Address correspondence to Lesley A. Stevens, MD, MS, Division of Nephrology, Tufts-New England Medical Center, 750 Washington St, Box #391, Boston, MA 02111. E-mail: [email protected]. © 2007 by the National Kidney Foundation, Inc. 0272-6386/07/5001-0005$32.00/0 doi:10.1053/j.ajkd.2007.04.004

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Stevens et al

GFR, and clinicians use estimated GFR rather than serum creatinine alone for clinical assessment of kidney function.1-9 The most commonly used equations to estimate GFR are the Modification of Diet in Renal Disease (MDRD) Study and Cockcroft-Gault equations.10-12 There is now a considerable body of evidence showing variability among studies in the performance of GFR estimating equations. In part, this is thought to be caused by variability among laboratories in serum creatinine assays.13,14 Formal study of the impact of calibration on performance of GFR estimating equations is lacking. The purposes of this report are to describe methods of calibration of serum creatinine assays to creatinine reference standards, and to assess the impact of this calibration on performance of the MDRD Study and Cockcroft-Gault equations in a pooled data set consisting of individual patient data from 6 research studies and 4 clinical populations. METHODS Sources of Data The Chronic Kidney Disease (CKD) Epidemiology Collaboration (CKD-EPI) is a research group formed to develop and validate improved estimating equations for GFR by pooling data from research studies and clinical populations (hereafter referred to as studies). Studies include individuals with diverse clinical characteristics with and without kidney disease and with a wide range of GFRs. We identified studies by searching the MEDLINE database and through personal knowledge of the investigators and other collaborators. Inclusion criteria were as follows: GFR measured as urinary iothalamate clearance; study population greater than 250 adults; availability of serum samples, use of quality control methods, and access to laboratories for calibration of serum creatinine assay; experience of collaborators in GFR measurement, creatinine assay, and clinical investigation; and willingness of collaborators to share individual patient data. Pooling of data from different sources is justified because of the similarity of GFR measurement methods and the ability to calibrate serum creatinine assays (described later). The CKD-EPI pooled creatinine database was divided into 2 distinct data sets, referred to as category 1 and category 2, based on timing of availability of data and with the goal to include similar populations in both data sets. The category 1 data set will be used for model development (random selection of two thirds of individual patient data) and internal validation (remaining third of individual patient data). Clinical populations were subdivided into people with known or suspected CKD and healthy individuals being evaluated for kidney donation. The category 2 data set will be used for external validation. The population described in this study includes people from category 1 studies who are in the development data set.

Serum Creatinine Assays at the Cleveland Clinic Research Laboratory Calibration was established by comparing creatinine values measured at the collaborators’ laboratories with those measured at the Cleveland Clinic Research Laboratory (CCRL). The CCRL used the modified kinetic rate Jaffé reaction (Beckman Synchron CX3; Beckman Coulter, Inc, Fullerton, CA) method during the MDRD Study. The Beckman CX3 and Roche enzymatic method (Roche-Hitachi P-Module instrument with Roche Creatininase Plus assay; Roche Diagnostics, Indianappolis, IN) are currently used in this laboratory, and both were used for the present study. Since the initiation of this study, the Roche enzymatic assay at CCRL was shown to be equivalent to creatinine reference standard methods traceable to materials with values assigned by isotope dilution mass spectroscopy (IDMS). The Beckman Synchron CX3 assay was also calibrated to these creatinine reference standard methods, with the calibration factor 0.906 (SE ⫽ 0.004) ⴱ Beckman CX3 values. Details of the methods and results were described previously.15 Therefore, all creatinine values were expressed as IDMSstandardized values. The Beckman CX3 showed coefficients of variation of 2.4% and 3.8% at creatinine values of 4.06 and 1.06 mg/dL (359 and 93.7 ␮mol/L) in 2005 (n ⫽ 165), respectively. The Roche assay showed coefficients of variation of 1.1% and 1.6% at creatinine values of 3.84 and 1.00 mg/dL (340 and 88.4 ␮mol/L) in 2005 (n ⫽ 409), respectively.

Calibration of Serum Creatinine Assays The guiding principle of the calibration process was, when possible, to use multiple data sources to attempt to provide redundant and consistent information on the estimate of the calibration factor at each participating laboratory. We surveyed all participating laboratories to determine instruments and reagents and assess availability of qualitycontrol data and stored specimens. We defined a hierarchy of calibration methods, listed next in order of preference. For studies in which several lines of evidence were available, the final calibration equation was selected based on relative rigor of the methods and consistency of calibration methods across all studies. 1. Frozen specimens: A random sample of 200 specimens from each study was requested for assay on the Beckman CX3 at the CCRL. Note that specimens were not required to be from the same people included in the study, but could be from other people who had creatinine measured on the same instrument contemporaneous to the study. A sample size of 200 was requested to allow for an SE for the difference between the original and new measurements of less than 0.02 mg/dL (⬍1.8 ␮mol/L). 2. Calibration panel (reference set of sera): A calibration panel was prepared from excess clear serum obtained from apparently healthy people and patients with CKD as soon as routine testing was completed by the CCRL using methods modified from the Clinical and Laboratory Standards Institute,16 with assigned values based on the Roche enzymatic method. The panel was sent to participating laboratories at which study

Impact of Creatinine Calibration on GFR Estimation

23

instruments were still in use. Panel specimens were measured in triplicate at both the CCRL and the participating laboratories to reduce analytical variance caused by random measurement error. 3. College of American Pathologists (CAP) Survey Data: CAP survey data for the CCRL and participating clinical laboratories from the past 7 years were obtained directly from CAP. Older CAP data, if available, were requested from each participating laboratory, along with any changes in instrumentation or calibration.

estimated GFR (eGFR), with positive values indicating lower estimated GFR than measured GFR (underestimation). Precision is expressed as interquartile range (IQR) for the differences. Accuracy is expressed as the percentage of estimates within 30% of measured values (P30). CIs were calculated using bootstrap methods (2,000 bootstraps) for difference and percentage of difference and the binomial method for P30 in SAS (SAS Institute, Cary, NC). In some analyses, measures of performance were computed separately for estimates greater and less than 60 mL/min/1.73 m2 (1 mL/s/1.73 m2) because this is the threshold value for definition of CKD.2 Receiver operating characteristic curves were computed using measured GFR less than 60 mL/min/ 1.73 m2 (1 mL/s/1.73 m2) as the criterion standard. Areas under the receiver operating characteristic curves were compared using the method of DeLong et al.21 Sensitivity analyses were performed by repeating the analysis in the pooled data set excluding the MDRD Study. Analyses were carried out using R (version 2; Free Software Foundation Inc, Boston, MA), SAS software (version 9.1), and Stata (version 8.0; Stata Corp LP, College Station, TX). Deming regressions and their CIs were calculated in Excel Analyse-it (version 1.73).18,19 Smooth estimates of the mean in the figures were created using the lowess function in R.

GFR Estimation GFR was estimated using the following equations (to convert values to SI units [serum creatinine in ␮mol/L], replace 186 with 32,788 in the original MDRD Study equation, replace 175 with 30,849 in the “reexpressed” MDRD Study equation, and replace 72 with 0.84 in the denominator of the Cockcroft-Gault equation): 1. The original MDRD Study equation10: GFR ⫽ 186 ⫻ Scr⫺1.154 ⫻ age⫺0.203 ⫻ 1.212 (if black) ⫻ 0.742 (if female) 2. The “reexpressed” MDRD Study equation for standardized serum creatinine17: GFR ⫽ 175 ⫻ standardized Scr⫺1.154 ⫻ age⫺0.203 ⫻ 1.212 (if black) ⫻ 0.742 (if female) 3. Cockcroft-Gault equation12: Ccr ⫽ (140 ⫺ age) ⫻ weight ⫻ 0.85 (if female) ⫻ 1.73) ⁄ (72 Scr * BSA) where Scr refers to serum creatinine (mg/dL), age is in years, weight is in kilograms, and BSA is body surface area in kilograms per square meter. The Cockcroft-Gault equation was not reexpressed for use with standardized serum creatinine because the original serum creatinine samples were not available for calibration to standardized serum creatinine assay.

Statistical Analyses

Creatinine Calibration Linear regression was used to assess significance of the intercept and slope. Slopes and intercepts were adjusted to account for measurement error and regression to the mean using the Deming regression.18,19 Confidence intervals (CIs) around the Deming slopes and intercepts are done automatically in Excel Analyse-it (Analyse-It Software, Ltd, Leeds, England).20 Calibration of the MDRD Study serum creatinine values was performed and reported previously.15 Values for calibrated creatinine measurements were calculated for each study at values of 1.0 and 3.0 mg/dL (88.4 and 265 ␮mol/L). Descriptive statistics summarized this effect across studies.

Performance of GFR Estimating Equations Bias is expressed as the mean value of the difference (mGFR ⫺ eGFR) and percentage of the difference [(mGFR ⫺ eGFR)/mGFR) ⴱ 100] between measured (mGFR) and

Role of the Funding Source CKD-EPI is funded by grants from the National Institute of Diabetes, Digestive, and Kidney Disease (NIDDK) as part of a cooperative agreement in which the NIDDK has substantial involvement in the design of the study and collection, analysis, and interpretation of the data. The NIDDK was not required to approve publication of the finished manuscript. The institutional review boards of all participating institutions approved the study.

RESULTS

Sources of Data

Figure 1 shows the search and selection process for studies that used urinary clearance of iothalamate to measure GFR. For purposes of analyses, the CKD and donor populations at the Cleveland Clinic and Mayo Clinic were considered separately, although methods for GFR and creatinine measurement were the same for CKD and donor populations and are reported together. Ten studies were selected as category 1 studies. The study population for this report includes 5,504 people in the development data set. Table 1 lists participant characteristics in each study. Four studies had mean measured GFR near the normal range (Diabetes Complications and Control Trial [DCCT], Diabetes Renal Disease Study [DRDS], Cleveland Clinic Donors, and Mayo Donors), but none are general population samples.

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Figure 1. Study selection and assignment to category 1 or 2 data sets. Clinical populations were divided into patients with chronic kidney disease and people being evaluated as potential kidney donors. Consideration of the timing of the availability of data and the goal to include similar populations in both data sets determined specific assignments of a study to category 1 or 2.

Table 2 lists methods of GFR and serum creatinine measurements in each study. Creatinine Calibration

Table 3 lists methods and results of calibration for each study. The MDRD Study and African American Study of Kidney Disease and Hypertension (AASK) each used the CCRL as their central laboratory. Samples from the AASK and MDRD Study, conducted 1 and 2 decades earlier, were remeasured on the Beckman Synchron CX3 assay to adjust for the possibility of drift over time, respectively. Calibration of serum creatinine assay in the laboratories for the DCCT, DRDS, and Collaborative Study Group (CSG) research studies were performed using frozen serum samples measured in the individual study laboratories during the study period and at the CCRL using the Beckman Synchron CX3 between 2003 and 2005. For other studies, large sets of frozen specimen were not available and other methods were used. The Chronic Renal

Insufficiency Cohort (CRIC), an ongoing study, used 2 methods for calibration: calibration panel and frozen plasma specimens (N ⫽ 5). For the Cleveland Clinic clinical population, 2 methods were used: comparison of CAP samples between the clinical laboratory and the CCRL (n ⫽ 89) and assay of frozen specimens from a separate study in 1996 (N ⫽ 39). Results from the 2 methods were in general agreement and differ from those previously reported.22 Results from the CAP comparisons were used for final calibration because the number of samples was larger and results covered the range of years represented in this study. For the Mayo Clinic, calibration was performed using comparisons to CAP data from the Mayo Clinic and CCRL. The creatinine assay in each study measured higher than standardized serum creatinine. For a noncalibrated serum creatinine value of 1.00 mg/dL (88.4 ␮mol/L), the median difference between the calibrated and noncalibrated creatinine values across studies was ⫺0.07 mg/dL

Impact of Creatinine Calibration on GFR Estimation

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(IQR, 0.10 mg/dL [⫺6.2 ␮mol/L; IQR, 8.8 ␮mol/ L]). For a noncalibrated serum creatinine value of 3.00 mg/dL (265 ␮mol/L), the median difference between the calibrated and noncalibrated creatinine values across studies was ⫺0.24 mg/dL (IQR, 0.05 mg/dL [⫺21.2 ␮mol/L; IQR, 4.4 ␮mol/L]).

GFR (DCCT, DRDS, and Mayo Donors).24-26 However, even after calibration, median bias remained as high as 10% to 15% in 3 of these 4 studies (DCCT, Cleveland Clinic Donors, and Mayo Donors)23,25,28 and in 1 other study (CSG).27

Performance of the MDRD Study Equation

Table 4 lists the performance of the MDRD Study equation in the pooled database and in each study using noncalibrated and calibrated serum creatinine values. Use of calibrated serum creatinine values improved performance in the pooled data set, with a decrease in median percentage of difference from 9.0% (95% CI, 8.3 to 9.6) to 5.8% (95% CI, 5.1 to 6.4) and an increase in P30 from 80% (95% CI, 80 to 81) to 83% (95% CI, 83 to 84). There was smaller effect on precision; IQR for the median percentage of difference decreased from 27.9% to 27.6%. The effect of calibration across the range of GFRs is shown in Fig 2. The effect of calibration on bias is greater at higher levels of GFR when evaluated on the absolute scale (left panel) and is more constant on the percentage scale (right panel). Table 4 lists the better performance of the MDRD Study equation after calibration in people with estimated GFR less than 60 mL/min/1.73 m2 (⬍1 mL/s/1.73 m2) compared with people with estimated GFR of 60 mL/min/1.73 m2 or greater (ⱖ1 mL/s/1.73 m2). Use of calibrated serum creatinine improved classification of patients with measured GFR less than 60 mL/min/1.73 m2 from area under the curve of 0.971 for noncalibrated serum creatinine to 0.973 for calibrated serum creatinine (P ⬍ 0.0001). In sensitivity analysis, we repeated these analyses excluding the MDRD Study. In the overall data set, with the use of calibrated serum creatinine, there was a decrease in median percentage of difference from 11.7% (95% CI, 10.7 to 12.3) to 7.2% (95% CI, 6.5 to 8.0), a minimal change in IQR from 27.9% to 28.4%, and an increase in P30 from 78% (95% CI, 78 to 79) to 82% (95% CI, 81 to 82). The effect of calibration varied across studies (Table 5). After calibration, median bias improved in 5 studies,23-26 did not change substantially in 3 studies,10,22,27,28 and worsened in 2 studies. The greatest improvement was seen in 3 of the 4 studies with the highest mean measured

Performance of the Cockcroft-Gault Equation

Use of calibrated serum creatinine worsened the performance of the Cockcroft-Gault equation with an increase in median percentage of difference from ⫺2.0% (95% CI, ⫺2.9 to ⫺1.1) to ⫺11.4% (95% CI, ⫺12.4 to ⫺11.7), a change in IQR from 37.5% to 38.8%, and a decrease in P30 from 74% (95% CI, 74 to 75) to 69% (95% CI, 69 to 70; Table 6). Median percentage of bias was greater than 20 mL/min/1.73 m2 (⬎0.33 mL/s/1.73m2) in 4 studies.10,22,24,25 As shown in Fig 3, the effect is greater at higher levels of GFR on the absolute scale, but relatively constant on the percentage scale. Use of calibrated serum creatinine improved classification of patients with measured GFR less than 60 mL/min/1.73 m2 (1 mL/s/1.73 m2) from area under the curve of 0.957 for noncalibrated serum creatinine to 0.961 for calibrated serum creatinine (P ⬍ 0.0001). DISCUSSION

In this study, we calibrated serum creatinine values from the laboratories of 6 research studies and 4 clinical populations to standardized creatinine materials at the CCRL and showed the impact of this calibration on performance of the MDRD Study and Cockcroft-Gault equations. There are 4 key findings from these analyses. First, in all individual laboratories represented in the pooled data set, noncalibrated serum creatinine values were greater than standardized creatinine values, as observed for most of the clinical laboratories in the United States when surveyed in 2004.29 The overestimate likely is caused by interference from substances other than creatinine (“noncreatinine chromogens”), such as proteins and ketoacids, and high levels of bilirubin or glucose that react in the alkaline picrate (Jaffé) assay. Calibration of creatinine assays adjusts for this interference, as well as other sources of error, but is variable among laboratories. Second, in the overall data set, as well as in some individual studies, calibration of the creati-

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Table 1. Study Characteristics

Study characteristics Type Center No. of participants Dates Clinical characteristics Median age (y) Women Black Diabetes Received transplant Median body mass index (kg/m2) Median GFR (mL/ min/1.73 m2) Median Scr (mg/dL) Median standardized Scr (mg/dL)

Overall

MDRD Study40

— — 5,504 —

RCT MC 1,085 1989-1992

47 (24) 2,397 (44) 1,749 (32) 1,580 (29) 251 (5) 27.1 (7.3)

52 (21) 427 (39) 128 (12) 68 (6) 0 (0) 26.8 (6.3)

DCCT41

DRDS26

CSG21

CRIC28

CC CKD22

CC Donors22

Mayo CKD24

Mayo Donors24

RCT MC 1,205 1995-1998

RCT MC 787 1987-1989

RCT SC 126 1988-1993

RCT MC 266 1987-1992

CS MC 446 2003-2005

CP SC 695 1996-2003

CP SC 301 1996-2003

CP SC 221 1999-2000

CP SC 372 1996-2002

55 (17) 414 (34) 1,205 (100) 0 (0) 0 (0) 29.5 (8.5)

29 (9) 356 (45) 23 (3) 787 (100) 0 (0) 24.2 (3.8)

44 (18) 75 (60) 0 (0) 104 (83) 0 (0) 33.3 (8.1)

34 (11) 129 (48) 21 (8) 266 (100) 0 (0) 24.1 (4.4)

58 (24) 214 (48) 196 (44) 290 (43) 0 (0) 30.3 (9.8)

55 (22) 299 (43) 122 (19) 148 (21) 146 (21) 27.1 (8.2)

42 (16) 191 (64) 50 (17) 0 (0) 0 (0) 26.8 (6.1)

53 (21) 79 (36) 0 (0) 16 (7) 105 (48) 28.4 (6.5)

41 (14) 214 (58) 6 (2) 0 (0) 0 (0) 27.3 (6.3)

73 (46.9)

48 (28)

23 (36)

104 (26)

1.60 (0.8) 1.53 (0.7)

2.80 (3.0) 2.58 (2.67)

0.80 (0.3) 0.80 (0.3)

AASK23

61.1 (65.5)

36.5 (27.3)

56.3 (34.5)

123 (27.7)

114 (37)

1.40 (1.1) 1.29 (1.1)

1.90 (1.3) 1.81 (1.2)

1.60 (0.9) 1.44 (0.8)

0.80 (0.2) 0.72 (0.2)

0.80 (0.2) 0.67 (0.2)

1.25 (0.6) 1.18 (0.5)

44 (36.0) 1.70 (0.9) 1.49 (0.9)

102 (23.0) 1.00 (0.3) 0.80 (0.2)

Note: Values expressed as median (interquartile range) or number (percent). To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply the result by 0.01667; Scr in mg/dL to ␮mol/L, multiply by 88.4. Abbreviations: MDRD, Modification of Diet in Renal Disease; AASK, African American Study of Kidney Diseases and Hypertension; DCCT, Diabetes Control and Complications Trial; DRDS, Diabetic Renal Disease Study; CSG, Collaborative Study Group: Captopril in Diabetic Nephropathy Study; CRIC, Chronic Renal Insufficiency Cohort Study; CC, Cleveland Clinic; Mayo, Mayo Clinic; CKD, chronic kidney disease; RCT, randomized controlled trial; CS, prospective cohort study; MC, multicenter; SC, single center; CP, clinical population, Scr, serum creatinine; GFR, glomerular filtration rate.

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Measurements GFR

Study

Iothalamate

Scr

Administration

No. of Periods

Plasma Calculation

Timing of Scr Relative to GFR*

Instrument

MDRD40

125

I

SC

4

Log mean

Same day

Beckman Synchron CX3

AASK23

125

I

SC

4

Log mean

Same day

Beckman Synchron CX3

DCCT41

125

I

SC

4

Log mean

Same day

Beckman Synchron CX3

DRDS26

Nonradioactive

IV

4

NA

Same day

Beckman Analyzer

CSG27

125

I

SC

4

Mean

Same day

Beckman Creatinine Analyzer-2

CRIC28 CC22†

125

I I

SC SC

4 2

Mean Mean

Same day Same day

Vitros 950 BM/H 747, Roche/BMC Modular

Mayo24†

Nonradioactive

SC

1

Mean

Within 2 wk‡

Hitachi 747

125

Assay

Kinetic alkaline picrate reaction by the Jaffé rate method Kinetic alkaline picrate reaction by the Jaffé rate method Kinetic alkaline picrate reaction by the Jaffé rate method Precise assay is unknown, probably Jaffé Kinetic alkaline picrate reaction by the Jaffé rate method 2-Point enzymatic test Kinetic alkaline picrate reaction by the Jaffé rate method on Hitachi 747; laboratory switched to the Roche modular system in 2001 Roche uncompensated calibrator & reagents; Roche compensated with uncompensated set point after 2002

Impact of Creatinine Calibration on GFR Estimation

Table 2. GFR and Creatinine Measurement Methods

Abbreviations: Scr, serum creatinine; GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; AASK, African American Study of Kidney Diseases and Hypertension; DCCT, Diabetes Control and Complications Trial; DRDS, Diabetic Renal Disease Study; CSG, Collaborative Study Group: Captopril in Diabetic Nephropathy Study; CRIC, Chronic Renal Insufficiency Cohort Study; CC, Cleveland Clinic; Mayo, Mayo Clinic; SC, subcutaneous; IV, intravenous; 125I, iodine 125. *Measurements on the same day were not necessarily simultaneous. †The same creatinine assay was used in both the CC and Mayo donor and chronic kidney disease populations. Assay characteristics and calibration results are described together. ‡Eighty percent of measurements were on the same day; the remaining measurements were within 2 weeks preceding GFR measurement or 1 day after.

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Table 3. Methods and Results of Calibration Calibration

Study

Methods Analyzed

Methods Used for Determination of Final Calibration Factor

MDRD Study40 AASK23 DCCT41 DRDS26 CSG27 CRIC28 CC22 Mayo24

Frozen CAP Frozen CAP Frozen Frozen CAP Frozen Frozen Panel CAP Frozen CAP Panel CAP

Frozen Frozen Frozen Frozen Frozen Panel CAP CAP

Calibrated Values* No. of Samples

Intercept (confidence interval)

Slope (confidence interval)

R2

Original Scr Value 1.00 mg/dL

Original Scr Value 3.00 mg/dL

253 242 199 186 283 38 89 45

0.00† ⫺0.026 (⫺0.046-⫺0.005) ⫺0.015 (⫺0.102-0.071) ⫺0.075 (⫺0.101-⫺0.049) 0.055 (0.024-0.086) 0.098 (0.040-0.156) 0.099 (0.037-0.161) ⫺0.216 (⫺0.290-⫺0.141)

0.95 (0.94-0.96) 1.011 (1.000-1.022) 1.015 (0.914-1.117) 1.013 (0.987-1.038) 0.998 (0.979-1.018) 0.893 (0.871-0.915) 0.981 (0.964-0.998) 1.098 (1.079-1.118)

0.9986 0.9931 0.6645 0.9710 0.9724 0.9947 0.9934 0.9966

0.95 0.89 0.91 0.85 0.95 0.99 0.98 0.80

2.85 2.73 2.74 2.68 2.76 2.78 2.76 2.79

Note: To convert Scr from mg/dL to ␮mol/L, multiply by 88.4. Abbreviations: MDRD, Modification of Diet in Renal Disease; AASK, African American Study of Kidney Diseases and Hypertension; DCCT, Diabetes Control and Complications Trial; DRDS, Diabetic Renal Disease Study; CSG, Collaborative Study Group: Captopril in Diabetic Nephropathy Study; CRIC, Chronic Renal Insufficiency Cohort Study; CC, Cleveland Clinic; Mayo, Mayo Clinic; CAP, College of American Pathologists; Scr, serum creatinine. *Values expressed as standardized values. For all studies other than CRIC, creatinine assays were calibrated against the Beckman CX3. For these studies, standardized values are obtained by multiplying the calibration factor by 0.906. CRIC was calibrated directly to standardized values using the Roche enzymatic method with the use of the calibration panel. †Intercept dropped because it was small and not significantly different from 0.11,17

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Table 4. Performance of the Modification of Diet in Renal Disease Study Equation by Level of Estimated Glomerular Filtration Rate Using Standardized Serum Creatinine Difference (mL/min/1.73 m2)

Difference (%)

Estimated GFR (mL/ min/1.73 m2) Group

No.

Median (95% CI)

IQR

Median (95% CI)

IQR

P30

Total ⬍60 ⱖ60

5,504 2,874 2,630

2.7 (2.4-3.1) 0.9 (0.6-1.1) 8.3 (7.4-9.2)

16.4 9.6 26.6

5.8 (5.1-6.4) 3.0 (2.0-3.7) 8.7 (7.5-9.7)

27.6 29.0 25.8

83 (83-84) 82 (81-83) 84 (83-85)

Note: Difference is calculated as measured GFR ⫺ estimated GFR. To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply the result by 0.01667. Percentage of difference is calculated as (measured GFR ⫺ estimated GFR)/measured GFR. Median values measure bias and IQRs measure precision. Abbreviations: GFR, glomerular filtration rate; P30, percentage of estimated GFR within 30% of measured GFR (measures accuracy); IQR, interquartile range; CI, 95% confidence interval.

60 30 0 -30 -90

-60

30 0 -30 -90

-60

Measured GFR - Estimated GFR

60

(Measured GFR - Estimated GFR) / Measured GFR

90

levels of measured GFR. Precision of the estimates did not change substantially. These findings are consistent with properties of the creatinine assay and the calibration procedure used here.30,31 The concentration of noncreatinine

90

nine assay to a reference standard reduced bias in GFR estimates derived from the MDRD Study equation reexpressed for use with standardized creatinine. In studies that showed improvement, the improvement in bias was greater at higher

0

30

60

90

120 2

Estimated GFR, mL/min per 1.73 m

150

0

30

60

90

120

150

2

Estimated GFR, mL/min per 1.73 m

Calibrated Non-calibrated Figure 2. Performance of the Modification of Renal Disease Study equation before and after calibration of serum creatinine assays in the pooled data set by level of estimated glomerular filtration rate (eGFR). Difference calculated as measured GFR ⫺ estimated GFR (mL/min/1.73 m2). Solid curves, nonlinear regression of the mean difference, which measures bias; dashed lines, quantile regressions of the 5th and 95th percentiles of differences, which measure precision; black line, calibrated serum creatinine; grey line, noncalibrated serum creatinine; vertical lines at 60 mL/min/1.73 m2, effect of calibration at level of GFR corresponding to National Kidney Foundation-Kidney Disease Outcome Quality Initiative definition for chronic kidney disease and the recommended threshold for reporting eGFR as a numeric value. To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply by 0.01667.

30

Table 5. Performance of the Modification of Diet in Renal Disease Study Equation Using Calibrated and Noncalibrated Creatinine Values Noncalibrated Creatinine Difference (mL/min/1.73 m2) Study

Measured GFR (SD) (mL/min/1.73 m2)

No.

Median (CI)

Pooled MDRD Study40 AASK23 DCCT41 DRDS26 CSG27 CRIC28 CC CKD22 CC Donors22 Mayo CKD24 Mayo Donors24

68 (40) 40 (21) 57 (24) 124 (20) 115 (28) 78 (33) 50 (21) 34 (27) 106 (18) 48 (26) 102 (17)

5,504 1,085 1,205 787 126 266 446 695 303 221 372

4.4 (4-4.7) 0.0 (⫺0.4-0.4) 3.9 (3.2-4.5) 17.9 (16.3-20.3) 13.6 (10-20.9) 9.7 (7.7-12.7) 3.3 (2.4-4.7) 0.6 (⫺0.3-1.2) 8.6 (6.7-11.4) 2.9 (1.3-4.7) 28.9 (26.9-30.8)

IQR

Calibrated Creatinine Difference (mL/min/1.73 m2)

Difference (%) Median (CI)

18.7 9.0 (8.3-9.6) 7.8 ⫺0.1 (⫺1.3-1.3) 15.0 7.6 (6.5-9.4) 28.3 14.9 (13.3-16.7) 31.1 13.5 (9.5-18.1) 23.1 15.6 (12.3-17.3) 13.5 8.0 (6.0-10.0) 9.5 2.5 (⫺1.1-5.4) 26.4 8.4 (6.3-11.7) 14.4 8.0 (3.5-11.8) 21.1 28.9 (27.0-30.2)

Difference (%)

IQR

P30 (CI)

Median (CI)

IQR

Median (CI)

IQR

P30 (CI)

27.9 23.9 26.3 21.5 25.9 27.1 26.7 39.8 24.9 29.7 16.5

80 (80-81) 90 (89-91) 85 (84-86) 81 (80-83) 76 (72-80) 75 (73-78) 82 (80-84) 71 (70-73) 86 (84-88) 76 (73-79) 54 (51-57)

2.7 (2.4-3.1) 0.0 (⫺0.4-0.4) 1.1 (0.4-1.7) 12.4 (10.2-13.9) ⫺0.7 (⫺5.7-5) 9.8 (7.3-12.3) 3.7 (2.6-5.2) 0.2 (⫺0.5-0.9) 14.8 (12.7-16.9) 0.1 (⫺1.0-1.2) 13.2 (12-15.4)

16.4 7.8 14.6 29.4 38.5 22.9 14.6 9.7 23.1 11.8 22.8

5.8 (5.1-6.4) 0.1 (⫺1.2-1.5) 2.0 (0.8-3.6) 10.1 (0.8-3.6) ⫺0.7 (⫺5.6-5.0) 14.6 (11.4-17.4) 8.6 (6.2-11.4) 0.6 (⫺2-3.4) 14.4 (12.4-15.9) 0.2 (⫺3.6-3.5) 13.4 (11.9-14.9)

27.6 23.8 28.0 22.8 32.6 26.6 27.8 41.0 21.5 31.0 20.7

83 (83-84) 90 (89-91) 86 (85-87) 85 (83-86) 79 (76-83) 75 (72-78) 81 (79-83) 71 (69-73) 84 (81-86) 77 (74-79) 87 (86-89)

Note: To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply by 0.01667; creatinine in mg/dL to ␮mol/L, multiply by 88.4. Abbreviations: GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; AASK, African American Study of Kidney Diseases and Hypertension; CRIC, Chronic Renal Insufficiency Cohort Study; CC, Cleveland Clinic; CKD, chronic kidney disease; CSG, Collaborative Study Group: Captopril in Diabetic Nephropathy Study; DCCT, Diabetes Control and Complications Trial; DRDS, Diabetic Renal Disease Study; Mayo, Mayo Clinic; P30, percentage of estimates within 30% of measured GFR; IQR, interquartile range; CI, 95% confidence interval; SD, standard deviation.

Stevens et al

Noncalibrated Creatinine (mg/dL) Difference (mL/min/1.73 m2) Study

Pooled MDRD Study40 AASK23 DCCT41 DRDS26 CSG27 CRIC28 CC CKD22 CC Donors22 Mayo CKD24 Mayo Donors24

Measured GFR (SD) (mL/min/1.73 m2)

Median (CI)

Calibrated Creatinine (mg/dL)

Difference (mL/min/1.73 m2)

Difference (%) IQR

Median (CI)

IQR

P30 (CI)

68 (40) 40 (21)

⫺1.1 (⫺1.4-⫺0.6) ⫺5.5 (⫺6.2-⫺5.0)

16.5 ⫺2.0 (⫺2.9-⫺1.1) 37.5 74 (74-75) 7.8 ⫺18.7 (⫺21.0-⫺17.0) 35.7 65 (64-67)

57 (24) 124 (20) 115 (28) 78 (33) 50 (21) 34 (27) 106 (18) 48 (26) 102 (17)

4.2 (3.4-5.1) 5.1 (2.7-7.0) ⫺8.3 (⫺17.1-⫺0.9) 0.2 (⫺2.9-2.9) ⫺1.6 (⫺2.6-⫺0.3) ⫺4.0 (⫺4.7-⫺3.3) ⫺1.6 (⫺3.9-⫺1.3) ⫺3.1 (⫺5.1-⫺1.4) 15.0 (12.7-16.8)

14.6 8.3 (6.7-9.7) 29.3 4.1 (2.3-5.5) 38.5 ⫺7.4 (⫺14.9-⫺1.0) 22.9 0.3 (⫺4.4-3.7) 14.6 ⫺3.5 (⫺6.6-0.5) 9.7 ⫺18.0 (⫺21.4-⫺14.4) 23.1 ⫺1.7 (⫺4.0-1.1) 11.9 ⫺8.1 (⫺13.4-⫺4.1) 22.8 14.9 (13.4-17.2)

32.6 24.0 39.6 33.4 40.1 52.7 29.3 41.0 22.1

Median (CI)

⫺5.5 (⫺5.9-⫺5.1) ⫺7.5 (⫺8.0-⫺6.9)

76 (75-77) ⫺0.8 (⫺1.6-⫺0.1) 89 (88-90) ⫺7.3 (⫺9.4-6.0) 69 (65-73) ⫺34.4 (⫺44.2-⫺24.9) 82 (79-84) ⫺3.8 (⫺6.2-⫺1.0) 71 (69-73) ⫺4.2 (⫺4.7-⫺2.3) 59 (57-60) ⫺6.0 (⫺6.8-⫺5.2) 85 (83-87) ⫺1.0 (⫺4.0-0.9) 68 (65-71) ⫺9.0 (⫺10.3-⫺7.3) 85 (83-87) ⫺4.9 (⫺7.3-⫺2.1)

IQR

Difference (%) Median (CI)

IQR

P30 (CI)

18.6 ⫺11.4 (⫺12.4-⫺11.7) 38.8 69 (69-70) 11.2 ⫺24.9 (⫺27.3-⫺23.1) 37.8 57 (55-58) 17.5 ⫺1.5 (⫺3.8-⫺0.3) 31.4 ⫺6.0 (⫺8.1-⫺4.6) 56.2 ⫺29.2 (⫺39.8-⫺22.4) 22.6 ⫺5.6 (⫺10.5-⫺1.2) 17.7 ⫺8.5 (⫺11.0-⫺5.9) 12.6 ⫺25.5 (⫺29.6-⫺21.8) 29.5 ⫺0.9 (⫺4.0-0.9) 16.2 ⫺23.3 (⫺30.6-⫺18.1) 26.9 ⫺4.8 (⫺7.1-⫺2.0)

35.7 26.2 50.4 33.8 44.1 56.4 28.5 46.6 26.9

75 (73-76) 84 (83-86) 49 (45-54) 77 (75-78) 69 (67-71) 52 (50-54) 86 (84-88) 54 (51-58) 84 (82-86)

Impact of Creatinine Calibration on GFR Estimation

Table 6. Performance of the Cockcroft-Gault Equation Using Calibrated and Noncalibrated Creatinine Values

Note: To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply by 0.01667; serum creatinine in mg/dL to ␮mol/L, multiply by 88.4. Abbreviations: CI, 95% confidence interval; SD, standard deviation; IQR, interquartile range; GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; AASK, African American Study of Kidney Diseases and Hypertension; CRIC, Chronic Renal Insufficiency Cohort Study; CC, Cleveland Clinic; CKD, chronic kidney disease; CSG, Collaborative Study Group: Captopril in Diabetic Nephropathy Study; DCCT, Diabetes Control and Complications Trial; DRDS, Diabetic Renal Disease Study; Mayo, Mayo Clinic; P30, percentage of estimates within 30% of measured GFR.

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-90

60 30 0 -30 -90

-60

30 0 -30 -60

Measured GFR - Estimated GFR

60

(Measured GFR - Estimated GFR) / Measured GFR

90

90

32

0

30

60

90

120

150

2

Estimated GFR, mL/min per 1.73 m

0

30

60

90

120

150

2

Estimated GFR, mL/min per 1.73 m

Calibrated Non-calibrated

Figure 3. Performance of the Cockcroft-Gault equation before and after calibration in the pooled data set by level of estimated glomerular filtration rate (eGFR). Difference is calculated as measured GFR ⫺ estimated GFR (mL/min/1.73 m2). Solid curves, nonlinear regression of the mean difference, which measures bias; dashed lines, quantile regressions of the 5th and 95th percentiles of the differences, which measure precision; black line, calibrated serum creatinine; grey line, noncalibrated serum creatinine; vertical lines at 60 mL/min/1.73 m2, the effect of calibration at the level of GFR corresponding to National Kidney Foundation-Kidney Disease Outcome Quality Initiative definition for chronic kidney disease and the recommended threshold for reporting eGFR as a numeric value. To convert GFR in mL/min/1.73 m2 to mL/s/1.73 m2, multiply by 0.01667.

chromogens does not increase as GFR decreases; therefore, the proportion of the assigned value for serum creatinine that reflects noncreatinine chromogens compared with true creatinine is greater at low serum creatinine values. Thus standardization of the creatinine assay would be expected to improve bias in estimating equations more at higher levels of GFR (lower levels of serum creatinine) than lower levels of GFR. Although the calibration procedure that we used adjusted for the average difference between methods, it did not specifically account for interindividual differences in noncreatinine chromogens and therefore is not expected to improve precision substantially. Better performance was shown in singlecenter studies using creatinine assays calibrated to the CCRL22,32,33 compared with noncalibrated assays.24,31,34,35 Calibration performed in this study modified the performance of the

MDRD Study equation in comparison to previously published reports of individual studies. There was improved performance of the Mayo Donors and DCCT and worsening performance of Cleveland Clinic Donors.24,25 In these previous reports, the calibration effect would have been included in the reported differences between measured and estimated GFR. The importance of calibration also was shown previously in simulations of GFR estimates and CKD prevalence estimates.13,36-38 The inconsistent effect of calibration on performance of the equations across studies supports the importance of uniform standardization of clinical laboratories. Third, in contrast to the MDRD Study equation, using standardized creatinine values worsens the performance of Cockcroft-Gault equation in the pooled data set and in most studies. It is expected that the Cockcroft-Gault equation would overestimate measured GFR because cre-

Impact of Creatinine Calibration on GFR Estimation

33

atinine clearance exceeds GFR because of creatinine secretion. It is likely that the creatinine assay used in the original Cockcroft-Gault laboratory also overestimated standardized creatinine, which would lead to underestimation of measured GFR. In our database, on average, these 2 errors appeared to cancel when noncalibrated creatinine values were used. However, because the Cockcroft-Gault equation cannot be reexpressed for use with standardized creatinine, standardization of the creatinine assay does not achieve the aim of calibration to Cockcroft-Gault equation. The results forecast the likely greater inaccuracy of estimates based on the CockcroftGault equation after standardization of creatinine assays in clinical laboratories. These findings reinforce the importance of reexpression of all GFR estimating equations for use with standardized creatinine assays. Fourth, even after calibration and use of the reexpressed MDRD Study equation, substantial errors remain in the higher range of GFR estimates. It is possible that we were not able to perfectly calibrate the creatinine assay for each study. Because of multiple steps in calibration, we suspect that differences of 1% to 5% may remain because of unmeasured factors. Other possible explanations for greater errors at higher GFR include errors in measurement of GFR or biological variation in GFR; limitations in generalizing GFR estimating equations developed in populations with CKD to populations without CKD because of differences in creatinine generation and determinants of biological variation in creatinine among populations; or to the phenomenon of regression toward the mean.31 It is important to note that none of these study populations reflect the general population. Greater errors at the higher GFR range support the current recommendations from the National Kidney Disease Education Program and other organizations to report a numeric value only for estimated GFR less than 60 mL/min/1.73 m2 (⬍1 mL/s/1.73 m2).1-9 There are several strengths of this study. The first is the use of the Roche enzymatic assay, which appears unbiased compared with IDMS standards as the calibrator for creatinine reference methods.39 A second strength is the use of redundant sources of information for calibration. In general, analyses using different sources of

information were in agreement, although some differences were detected. A third strength is comparison of the calibration effect across different study populations with varying serum creatinine ranges and assays. There are several limitations to these analyses. The first is that CAP samples, not frozen specimens or calibration panels, were used for final calibration factors for the clinical populations. Thus, calibration factors for these studies may not be as accurate as for the research studies. However, our analyses of secondary sources of calibration data support these conclusions. For the Cleveland Clinic clinical population, a small number of frozen samples were available from a 1-year period, which showed general agreement with analyses from the CAP samples (data not shown). In addition, Mayo Clinic Donors had mean calibrated serum creatinine values similar to the age-, sex-, and race-adjusted general population in the National Health and Nutrition Examination Survey, although Cleveland Clinic Donors had slightly higher levels. A second limitation is that a single calibration factor was applied for each study, although some studies occurred over a several-year period and some studies used more than 1 assay or instrument. Checking calibration using frozen samples at each year of each study separately was not feasible, and we therefore tried to estimate the presence of drift by analysis of CAP samples over time. CAP samples from the Mayo Clinic, but not CRIC or Cleveland Clinic, laboratories showed a drift over time. Nonetheless, given the small number of samples available at each time, the possibility for drift in the creatinine assay cannot be definitely eliminated. Finally, as discussed, the calibration procedure did not account for interindividual differences in noncreatinine chromogens. To reduce this source of variation further, which would be most relevant in the few individuals who had high levels of bilirubin, glucose, or ketoacids, would require remeasuring all individual samples. This was not feasible in this large project. In clinical practice, it is not feasible to calibrate all clinical laboratories to the laboratory in which an estimating equation is developed. The National Kidney Disease Education Program has initiated a creatinine standardization program to enable standardization of serum creatinine as-

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Stevens et al

says for all clinical laboratories, with completion expected by 2008.30 Reexpression of the MDRD Study equation based on a standardized assay enables reporting of estimated GFR in clinical practice using standardized creatinine values. When possible, GFR estimating equations should be reexpressed for use with standardized creatinine. If other filtration markers, such as cystatin C, are incorporated into GFR estimating equations, programs will be needed to ensure assay standardization of these markers. At the current time, GFR estimates less than 60 mL/min/1.73 m2 (⬍1 mL/s/1.73 m2) using standardized serum creatinine appear relatively unbiased, although precision is limited, and can be used to assess kidney function in clinical practice. If greater accuracy is needed, clinicians can request a clearance measurement using an exogenous filtration marker or multiple creatinine clearance measurements. INVESTIGATORS AND RESEARCH STAFF OF THE CKD-EPI

Tufts-New England Medical Center: Andrew S. Levey, MD; Lesley A. Stevens, MD, MS; Christopher H. Schmid, PhD; and Yaping (Lucy) Zhang, MS; Cleveland Clinic: Frederick VanLente, PhD; Liang Li, PhD; University of Utah: Tom Greene, PhD; Johns Hopkins University: Josef Coresh, MD, PhD, MHS; Jane Manzi, PhD; Brad Astor, PhD, MPH; Elizabeth Selvin, PhD, MPH; University of Pennsylvania: Harold I. Feldman, MD, MSCE; J. Richard Landis, PhD; and National Institute of Diabetes and Digestive and Kidney Diseases: John W. Kusek, PhD; Paul W. Eggers, PhD; and Josephine P. Briggs, MD. COLLABORATORS CONTRIBUTING DATA FOR THIS STUDY

MDRD Study: Gerald Beck, PhD; DCCT: Saul Genuth MD; Michael Steffes, MD, PhD; CSG: Captopril in Diabetic Nephropathy Study: Rodger Rodby, MD; Richard Rohde; AASK: Gabriel Contreras, MD; Julie Lewis, MD; DRDS: Robert Nelson, MD; Cleveland Clinic: Phillip Hall, MD; Emilio Poggio, MD; CRIC Study: Lawrence J. Appel, MD, MPH; Jing Chen, MD, MSc; Alan S. Go, MD; Lee Hamm; J Chi-yuan Hsu, MD, MSc; James P. Lash, MD; Akinlolu O. Ojo, MD; Mahboob Rahman, MD; Raymond R. Townsend, MD; Matthew R. Weir, MD; Jackson

T. Wright MD; Mayo Clinic: Andrew Rule, MD, MSc; Timothy Larson, MD; Fernando Cosio, MD. ACKNOWLEDGEMENTS Support: This work was supported by grants UO1 NIDDK 053869, UO1 NIDDK 067651, and UO1 NIDDK 35073. Financial Disclosure: None.

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