State of the Art for Measurement of Urine Albumin: Comparison of Routine. Measurement Procedures to Isotope Dilution Tandem Mass Spectrometry

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1 1 of 27 State of the Art for Measurement of Urine Albumin: Comparison of Routine Measurement Procedures to Isotope Dilution Tandem Mass Spectrometry Supplemental Information, Tables and Data

2 2 of 27 Supplemental Table 1. Routine measurement procedures for urine albumin included in the study. Manufacturer/Instrument Abbott Architect c4000, c8000, c16000 Measurement Procedure Name Multigent Microalbumin IFU Version Number Measurement Principle a Calibrator Name/Part# Calibration Traceability E/4 October 2007 immunoturbidimetric MicroAlbumin Calibrators/ 2K98-02 IRMM-ERM DA470 (CRM 470) Ortho Vitros 5600 Microalbumin J21305_EN Version 4.0 immunoturbidimetric Cal Kit 24 IRMM-ERM DA470 (CRM 470) Beckman AU680 International Parameters Microalbumin BLOSR6x immunoturbidimetric Microalbumin Calibrator/ ODR3024 In-house prepared human serum albumin standard Beckman AU680 Microalbumin BAOSR6x67.02 US Parameters b immunoturbidimetric Microalbumin Calibrator/ ODR3024 In-house prepared human serum albumin standard Beckman Synchron UniCell DxC 800 Microalbumin A18520 AE September 2008 Beckman Immage Microalbumin AE November 2007 Roche cobas c 501 ALBT2 Tina-quant V 3 English Albumin Gen Siemens ADVIA 1650 Microalbumin_ , Rev. B Siemens BN II Albumin OSALG11E0502 (34) FB 1 June 2010 Siemens Immulite Albumin PIL2KHA immunoturbidimetric MA Calibrator/ IRMM-ERM DA470 (CRM 470). immunoturbidimetric Urine Calibrator/ IRMM-ERM DA470 (nephelometry) (CRM 470). immunoturbidimetric Cfas PUC IRMM-ERM DA470 (CRM 470) immunoturbidimetric MALB Ctrl / In-house human albumin standard. immunoturbidimetric N Protein Standard SL IRMM ERM-DA470 (nephelometry) (OQIM15) (CRM470) immunoturbidimetric Albumin Adjustors/LHAL, LHAH In-house prepared human serum albumin standard Siemens DCA Microalbumin/Creatinine Rev.A 2000+/Vantage c immunoturbidimetric In-house prepared calibrator In-house prepared human serum albumin standard Siemens Dimension ExL Max Microalbumin C US immunoturbidimetric MALB Cal / DC114 IRMM ERM-DA470 (CRM470)

3 3 of 27 Manufacturer/Instrument Siemens Dimension RxL Max Measurement Procedure Name Microalbumin IFU Version Number Measurement Principle a Calibrator Name/Part# Calibration Traceability C US immunoturbidimetric MALB Cal / DC114 IRMM ERM-DA470 (CRM470) Siemens Dimension Vista Microalbumin K7062G00E immunoturbidimetric PROT 3 Cal / KC770 IRMM ERM-DA470 (CRM470) Siemens Clinitek Advantus Microalbumin AN73024F 7/07 sulfonephthalein dye binding; semiquantitative In-house prepared calibrator In-house prepared human serum albumin standard a All methods are quantitative unless otherwise indicated. b Two methods for Beckman AU680 were included in the study. The Beckman US parameters (US) method uses a sample volume of 4μL, while the Beckman AU680 International Parameters (Inter) method uses a sample volume of 8μL. The US parameters method includes an additional calibrator (zero calibrator) and utilizes a different calibration curve equation than the International Parameters method. See manufacturer package inserts for more information. c Samples were analyzed on both the DCA2000+ and DCA Vantage. Of the quadruplicate values for each sample, two measurements were performed using each instrument. Both instruments use the same reagents and methods.

4 4 of 27 Statistical Analysis Section 1. Objectives The objectives of the statistical analysis were to determine the overall agreement between routine measurement procedures for urine albumin and a comparative IDMS measurement procedure and to identify the dominating causes for any disagreements. 2. Absolute or relative differences and variations Urine albumin values over the approximate concentration interval of 10-2,000 mg/l were obtained. An error of 10 mg/l at lower concentrations may be substantial, but an error of this magnitude at higher concentrations may be negligible. Thus, for this study it was preferable to express variations and differences with relative measures, i.e. variation was expressed as a relative standard deviation, coefficient of variation (usually in percent), and bias as a deviation in percent from a reference value. For the statistical analysis, concentrations were transformed to a relative (logarithmic) scale. One advantage of a logarithmic transformation is that the relative deviations become symmetric. For example, if the observed values are 50 and 100 with the IDMS and routine methods respectively, the relative difference is 100 %. If the values are exchanged, the relative difference is -50 %. On a logarithmic scale, it is only the sign that differs (0.69 and -0.69, respectively) and, thus, a bias is avoided. Furthermore, analysis of deviations using a logarithmic transformation simplifies the statistical analysis and often gives more homoscedastic variations. If the natural logarithm is used, the standard deviation (SD) of ln x is approximately equal to the standard deviation of x divided by the mean, such that: 100. SD(ln x) CV(x), (1) where CV is expressed in percent. This approximation is typically acceptable when CV < 20 %. The relative bias (in percent) is obtained from the observed logarithmic bias, bias ln, according to Relative bias (%) 100(Exp(bias ln )-1). (2) 3. Presentation of overall agreement The overall agreement between routine methods and the IDMS method are presented as box and whisker plots of differences in percent. 4. Models A box and whisker plot gives no information about the possible causes for the differences, which needs to be understood for the purpose of method performance improvement. Thus, the differences were decomposed into components, which could be related to possible causes, and for this purpose a reasonable model for the error components was needed. 4.1 Error model For this study the following model was assumed: y = µ + δ(µ) + d + b(µ) + c + e, (3) where y is ln(measured concentration) and µ is the expected value for ln(concentration) with the IDMS method. The other components are defined in Supplemental Table 2, where

5 5 of 27 possible sources of each component are listed. The standard deviations of the components e, c, b and d are denoted σ e, σ c, σ b and σ d. (Estimated standard deviations are denoted by s.) These standard deviations are assumed to be approximately constant over the measuring interval (i.e. independent of the concentration, homoscedastic). Component c is the nonrandom part of the variation within runs. For a sample allocated randomly in a run, as in this study, c can be considered as a random component. Supplemental Table 2. Error components and their possible sources Error component Notation Possible sources (examples) Error component between consecutive measurements within runs (estimated from replicate measurements of each sample) Error component between different positions within a run (estimated from measurements of the same sample non-frozen and after one freeze-thaw) Sample-specific errors (estimated as residual variation between methods not accounted for by other error components) A common error component between runs, assumed to be a continuous function of the concentration μ (estimated from study values) A common systematic error component for the performance conditions of the considered runs, assumed to be a continuous function of the concentration μ (estimated from difference between routine and reference values) e c d b(µ) δ(µ) 1. Variation in performance conditions within runs 2. Trends in performance conditions within runs (may consist of a systematic part, which is the same from run to run, and a random part, which varies from run to run) 3. Differences in influence quantities between samples (matrix effects) 4. Random error in establishing the calibration curve (due to the variation within runs) 5. Interaction between performance conditions and properties of samples and calibrators respectively 6. Error in the assigned value of the reference material used as a calibrator, or for preparation of calibrators, or for calibration traceability of product calibrators 7. Errors in the preparation or value assignment of the product calibrators 8. Unsuitable model for the calibration curve 9. Unsuitable concentrations of the calibrators 10. Non-commutable calibrators (differences in influence quantities between calibrators and samples) 11. A systematic part of the position effects within runs in combination with fixed positions for the calibrators. This model is a basic description of possible error components. The estimated common systematic error, δ(µ), and the standard deviations depend on the performance conditions of an investigation (such as the same laboratory, operator, equipment, reagents, replication or variation of one or more of these factors) and the set of samples studied.

6 6 of 27 It is often assumed that the variation of measurement results consists of two random components: within runs and between runs. However, the variation within runs may not be completely random. In practice there can be trends and other types of non-random patterns within runs. Thus, the variation between consecutive replicates (e) may be less than the total variation within runs. The difference is referred to as position effects. There is generally no reason to believe that the components b(µ) and δ(µ) are linear. The only assumption about these functions in the model is that they are continuous. In this study, a model with two components describes the systematic differences between a routine method and the IDMS method: a common bias function [b(µ)], assumed to be a continuous function of the concentration, and a sample specific difference (d). The latter component can be considered as a random component in a population of samples and is the contribution to the deviations from the bias function that cannot be explained by other random components. The parameters in the error model can be related to specific likely causes and thus can be used to identify measures to reduce the errors. For instance, δ(µ) can be reduced by a correction or improved calibration, while a decrease of σ d requires an improved specificity of the method. For the IDMS measurement procedure, the measured value is denoted by x and the components d and δ(µ) are assumed to be zero. In this study some samples (with high concentrations) were diluted. The dilution was not repeated for each replicate of the diluted sample in a run, thus, there could be a common dilution error for all replicates. In the model, a common dilution error was assumed to be negligible. If there was a common dilution error, it would be confounded with the sample specific error, d, in the comparison of methods. A dilution will also change the matrix of the sample which may contribute to the sample specific errors for diluted samples. Another possible influence of dilution occurs for samples with concentrations close to or just beyond the upper limit of the AMR when some samples were diluted and other samples were undiluted. In this situation, samples in the same concentration range will be evaluated against different parts of the calibration curve. As the calibration error may be different in different parts of the calibration curve, these differences will then be confounded with the sample specific errors. 4.2 Estimation of the effects of a freeze-thaw cycle The measurements with the IDMS procedure were performed on frozen and thawed samples, while the measurements with the routine methods were performed on fresh (non-frozen) samples. For the interpretation of the comparisons with the IDMS method it must be investigated whether there were any freeze-thaw effects. For each method a number of samples were measured in four replicates in the same run both fresh and after freezing and thawing. It is assumed that the freeze-thaw effect can be described by a continuous function of the concentration (main effect) plus a sample-specific effect. These effects may depend on the method if the methods don t measure exactly the same property. With this design samplespecific effects and position effects within runs are confounded. However, sample-specific freeze-thaw effects without main freeze-thaw effects imply that freezing and thawing will both increase and decrease the observed concentrations for the samples in a way that the average effect is zero. If there are no or negligible main effect it is considered as more probable that the sample-specific effects are in fact position effects.

7 7 of 27 5 Estimation of parameters in the model 5.1 General Estimations of the parameters in the model, i.e. the standard deviations of the random components, σ e, σ c, σ b and σ d, and the systematic component, δ(µ), were based on different types of experimental design. There are two main types: those concerning the pattern of variation within a method and those concerning comparison of measurement methods. The first type gave estimates of σ e, σ c and σ b and the second type gave estimates of σ e, σ d and δ(µ). For evaluation of the uncertainty of the estimate of δ(µ), an estimate of σ b from the first type of experiments was used. 5.2 Evaluation of the freeze-thaw study The freeze-thaw results were analyzed according to the same principles as for method comparisons with the x variable the non-frozen results and the y variable the freeze-thaw results, see section 5.5. Pooled estimates of σ e, denoted s e, were obtained from the replicate results for individual samples. Differences between the freeze-thaw results and the non-frozen results were plotted against the mean values. Obvious outliers were identified by inspection of the difference plots. After exclusion of outliers (Supplemental Table 3), trends and mean differences were negligible for all methods. It was then reasonable to conclude there were no freeze-thaw effects and the standard deviation of position effects was estimated by, (4) where s MSSD is the standard deviation of the scatter estimated from the Mean Square Successive Difference in the difference plot. For the IDMS procedure an average 3.8% freeze-thaw effect was found and the differences were plotted against the non-frozen results in Supplemental Fig. 1. There were no trends but significant sample-specific freeze-thaw effects (3.4%) were observed.

8 8 of 27 Supplemental Table 3. Outlier values excluded in the freeze-thaw assessment. Method Mean of frozen (mg/l) Relative difference of Frozen vs. Non-Frozen (%) IDMS Architect c Architect c Architect c Vitros Dimension ExL Dimension Vista Assessment of imprecision components, IDMS method Because the IDMS method was considered a higher-order measurement procedure and employed a sample extraction step, sample specific-effects were assumed to be negligible. For the IDMS method, the measured value was denoted by x and the systematic error components d and δ(µ) were assumed to be zero. Three control samples were analyzed in two consecutive duplicates (at the beginning and end of each run) in 5 runs. For each duplicate, the adjusted mean was reported. There was no indication of a difference between beginning and end, and the SDs within and between runs for each sample were estimated from a one-way analysis of variance. As an estimate of the between run variation, the pooled SD for the three control samples was calculated (the square root of the average of the variances) and denoted s b(x). The standard deviation within runs was also estimated from duplicate determinations of the patient samples. As this estimate, denoted s e(x), was based on more than 300 samples it was considered more reliable than the estimate obtained from the control samples. The total SD of the mean for the duplicate of a patient sample was estimated by. (5) 5.4 Assessment of imprecision components, routine methods Two to three control samples (depending on the analytical measurement range for a given method) were analyzed in two duplicates (at the beginning and end of each run) in 10 runs. Supplemental Table 4 shows that data for some runs were excluded from the analysis based on missing values for one or more of the samples. For each sample, a two-way

9 9 of 27 analysis of variance was performed with runs and positions (beginning and end) as factors. Run was considered as a random factor (the runs are considered as a sample from the population of possible runs) and position was considered as a fixed factor (the two positions are not a random sample from possible positions but we have chosen the beginning and the end). The analysis of variance provided estimates of the following variances: between runs (the standard deviation is denoted s b ), between positions (the systematic part of position effects), interaction between position and run (the random part of position effects) and within positions. The variances of the systematic and random position effects were summed to estimate the total variance of positions (the standard deviation is denoted s c ). The average variances from all control samples were calculated as best estimates of the variances between runs and between positions. The standard deviation within runs, denoted s e, was estimated from four consecutive replicates of the patient samples. As this estimate was based on > 300 samples, it was considered more reliable than the estimate obtained from the control samples and therefore was used in the evaluation of the method comparison data. Also the SD of position effects obtained from the freeze-thaw study was considered as more reliable and was used in the method comparison.

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11 11 of 27 Supplemental Table 4. Number of runs included for between-run imprecision assessment Number of Runs Included Number of Runs Excluded Reasons for excluding the runs Method Level 1 a Level 2 Level 3 Level 1 Level 2 Level 3 IDMS Architect c4000 b : wrong material analyzed for 1 run; 2: 1 replicate not tested for 2 runs; 3: wrong material analyzed for 1 run Architect c8000 b : 1 replicate value not tested for one run Architect c16000 b : 1 replicate value not tested for 1 run Vitros AU680 Inter AU680 US Manufacturer closed for 1 week due to snow; total number of runs = 9 Manufacturer closed for 1 week due to snow; total number of runs = 9 DxC Immage : 1 run excluded due to 1 obvious outlier pair (39.8 mg/l; 55.1 mg/l). Exclusion of this single pair decreased

12 12 of 27 Number of Runs Included Number of Runs Excluded Reasons for excluding the runs Method Level 1 a Level 2 Level 3 Level 1 Level 2 Level 3 ln SD from to : no results reported by instrument for 1 replicate value, sample analysis was not repeated cobas c ADVIA BN II Immulite N/A c 0 9 N/A 3 weeks included 2 runs; total number of runs = 13. 3: no results reported by instrument for 1 replicate value, sample analysis was not repeated 1: all samples were erroneously analyzed at the beginning of the run for 6 runs, 2 replicate values missing due to analyzer mechanical failure for 1 run 3 weeks included 2 runs; total number of runs = 13; 2: 9 runs included results that were above the upper limit of the AMR; 3: not analyzed target concentration was above the AMR DCA Dimension ExL N/A 0 0 N/A 3: not analyzed target concentration was above the AMR

13 13 of 27 Number of Runs Included Number of Runs Excluded Reasons for excluding the runs Method Level 1 a Level 2 Level 3 Level 1 Level 2 Level 3 Dimension RxL Dimension Vista N/A 0 1 N/A week included 2 runs; total number of runs = 11; 2: 1 run was above the upper limit of the AMR; 3: not analyzed target concentration was above the AMR 1 week included 2 runs; total number of runs = 11; 2: no results reported for 2 replicate values, sample analysis was not repeated; 3: materials not analyzed. a See Supplemental Table 7 for IDMS albumin concentrations for each level of. b Number of runs included for manufacturer s used in Table 2, main report: Architect c4000: 1, 9 runs, 2, 10 runs; Architect c8000: 1 and 2, 10 runs; Architect c16000: 1, 11 runs, 2, 10 runs. c N/A not applicable for reasons indicated.

14 14 of Method comparison analysis assessment of bias and sample-specific effects For each sample i the difference in ln(concentration) between the mean of the routine method replicate results (y i ) and the mean of the IDMS procedure replicate results (x i ) was plotted against the concentration obtained with the IDMS procedure. There are two main characteristics of a difference plot: the width of the scatter and the deviation of the scatter center from zero, the bias, which may depend on the concentration. A procedure for the analysis of a difference plot was suggested in (1) and modified versions of this procedure have been applied in (2) and (3). The variance of the scatter around a continuous bias function was estimated from the Mean Square Successive Difference of the differences in the plot and calculated as, (6) where v i = y i - x i, in ascending order of the concentration obtained with the IDMS procedure. This estimate of the scatter variance is based on the assumption that the bias function is approximately the same for consecutive concentrations. This assumption should be reasonable when the number of samples is large and fairly evenly distributed in the concentration interval investigated. When the bias does not depend on the concentration, s MSSD should be of the same size as s v, the SD of the differences v i. If s MSSD is significantly smaller than s v, there is a dependence on concentration (a trend). With more than 300 samples, a significant trend is obtained when s MSSD /s v is less than 0.95 (p < 0.05). In this study, the samples were measured in 5 different runs with the IDMS procedure and, in most cases, in 10 different runs with the routine methods. Thus, both the variation within runs and the variation between runs of the two compared methods will contribute to the estimated standard deviation of the scatter, s MSSD. For the IDMS procedure, the estimated SD of this contribution was denoted s x, see section 5.4. For means of four consecutive replicates with the routine methods, the contribution to the scatter variance should be. (7) σ e is estimated by the square root of the average of the variances for all samples. Estimates of σ b were obtained from measurements of the control samples, and for σ c were obtained from the freeze-thaw samples. If the sample specific error, component d, was negligible the scatter variance should be estimated by. (8) s FT is the standard deviation of the freeze-thaw effects with the IDMS procedure. Thus, without sample specific errors, we should have s MSSD s E. If s MSSD > s E, the variance of the sample specific errors was estimated by. (9)

15 15 of 27 If s MSSD < s E, the standard deviation of sample specific errors was estimated to be zero. Such a result might be due to, for example, an excessive estimate of σ b. For the sample specific differences it is relevant to consider the following situations: 1. The sample-specific effects were caused by interfering substances having a more or less normal distribution of concentrations in the population of samples. It is then reasonable to assume that the effects are also normally distributed and contribute to the observed standard deviation of an approximately normal distribution of differences from the IDMS procedure. In this situation, it is relevant to characterize the sample-specific effects by a standard deviation, i.e. the standard deviation contributing to the observed standard deviation of the differences. 2. The sample-specific effects were large and were caused by substances occurring only in a fraction of the samples. In this situation, it is more relevant to estimate the size of the fraction of samples that contain the interfering substances. In practice there may often be a mixture of the two situations. The interpretation of experimental data is also complicated by the fact that it is difficult to decide whether a large difference from the IDMS procedure is caused by properties of a sample or a mistake (e.g. mix-up of samples, transcription error, etc.). The following approach was used since there may be a mixture of the two situations above: 1. Identify large deviations (outliers) more or less subjectively from difference plots of ln(concentration). Supplemental Table 5 shows the values excluded as outliers. 2. Exclude the large deviations and estimate the standard deviation of sample-specific differences for the remaining samples. Supplemental Table 5. Outliers values excluded from the method comparisons Method Mean of Routine Method (mg/l) Relative difference of Routine Method vs. IDMS (%) Architect c ADVIA Immulite DCA 26-72

16 16 of 27 Dimension RxL The deviation of the scatter center from zero was used to estimate the bias, δ(µ). The scatter centers at various positions along the x-axis in the difference plots were determined as the moving average of 21 consecutive differences ordered from low to high concentration based on the IDMS concentrations. It was not possible to obtain a correct estimate of the uncertainty of the bias due to the dependence of the bias on the concentration and the unbalanced allocation of the samples to the different runs. Consequently, it was not possible to perform significance testing on the bias vs. the IDMS procedure. References for the statistical analysis section 1. Nilsson G. Comparison of measurement methods based on a model for the error structure. J Chemometrics 1991;5: Miller WG et al. Toward Standardization of Insulin Immunoassays. Clin Chem 2009;55: Miller WG et al. Seven Direct Methods for Measuring HDL and LDL Cholesterol Compared with Ultracentrifugation Reference Measurement Procedures. Clin Chem 2010;56:

17 17 of 27 Supplemental Table 6. Freeze-Thaw Analysis: Percent differences for samples subjected to a single freeze-thaw cycle compared to corresponding non-frozen samples a,b. Measurement Procedure, Full range tested (mg/l), [AMR a (mg/l)] N Difference % Frozen minus Nonfrozen results CV c % Position effects, combined frozen and non-frozen results IDMS n/a Architect c [ ] 67 [56] 0.1 [0.2] 1.4 [1.4] Architect c [ ] 66 [56] -0.2 [-0.2] 1.0 [1.0] Architect c [ ] 66 [55] -0.4 [-0.3] 1.8 [1.9] Vitros [ ] 61 [43] 0.1 [0.6] 1.6 [1.5] AU680 Inter [ ] 60 [42] 0.6 ** [0.6] * 0.9 [1.0] AU680 US [ ] 60 [42] [0.6] * [1.2] Unicel DxC [ ] 67 [49] 0.2 [-0.1] 1.2 [1.2] Immage [ ] 68 [52] 0.4 [0.4] 1.4 [1.6] cobas c [ ] 67 [51] [1.3]

18 18 of 27 Measurement Procedure, Full range tested (mg/l), [AMR a (mg/l)] N Difference % Frozen minus Nonfrozen results CV c % Position effects, combined frozen and non-frozen results ADVIA [ ] 68 [54] 0.4 [0.2] 1.4 [1.4] BN II [ ] 63 [45] -0.5 [-0.4] 0.9 [0.9] Immulite [2.5 60] 68 [23] 0.3 [-0.2] 2.5 [2.8] DCA [ ] 67 [49] 0.4 [0.1] 0.5 [0.3] Dimension ExL [ ] 63 [37] -0.1 [1.0] 3.8 [3.9] Dimension RxL [ ] 64 [38] 0.0 [-0.1] 2.1 [0.9] Dimension Vista mg/l [ ] 64 [48] 0.8 ** [0.7] * 1.3 [1.3] a Mean percent differences for frozen compared to non-frozen samples are shown. Bracketed values indicate calculations performed using data restricted to the analytical measurement range (AMR). CV c represents position effects using combined results from frozen and nonfrozen samples. b Obvious outlier results were identified by visual inspection of difference plots and removed from the analysis. Removed outliers are shown in Supplemental Table 3. * Statistically significant differences for frozen vs. non-frozen results (p <0.05). ** Statistically significant differences for frozen vs. non-frozen results (p <0.01).

19 19 of 27 Supplemental Table 7. Variance Component Analysis of Quality Control () Materials a. Level 1 [26.0 mg/l] Level 2 [62.4 mg/l] Level 3 [152.5 mg/l] Measurement Procedure CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c IDMS b b b 6.2 Architect c4000 c Architect c8000 c Architect c16000 c Vitros AU680 Inter AU680 US Unicel DxC Immage cobas c ADVIA BN II

20 20 of 27 Level 1 [26.0 mg/l] Level 2 [62.4 mg/l] Level 3 [152.5 mg/l] Measurement Procedure CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c CV e % Withinrun CV b % Between -run CV c % Position effects CV t %, Includes CV e, CV b, CV c Immulite DCA Dimension ExL Dimension RxL Dimension Vista a Coefficients of variation (CV) components: CVe (within-run imprecision); CV b (between-run imprecision); CV c (imprecision due to position effects); CV t (combined imprecision). Empty cells indicate no data, see Supplemental Table 4. b Two-way analysis of variance did not indicate position effects and CV c was estimated to zero. c As noted in Table 2 in the main report, the study results for the Architect c4000, c8000 and c16000 were erratic at the beginning and end of different runs suggesting the samples may have been mishandled or unsuitable for these procedures.

21 21 of 27 Frozen - nonfrozen, Urine albumin, nonfrozen (mg/l) Supplemental Figure 1. Difference of ln(concentration) for the frozen vs. nonfrozen urine samples measured by the IDMS procedure.

22 22 of 27 2A) 2B) Architect c IDMS Architect c IDMS C) 2D) Architect c IDMS Vitros IDMS E) 2F) AU680 Inter - IDMS AU680 US - IDMS

23 23 of 27 2G) 2H) Beck DxC - IDMS Immage - IDMS I) 2J) Cobas c501 - IDMS ADVIA IDMS K) 2L) Siem BNII - IDMS Immulite IDMS

24 24 of 27 2M 2N) Siem DCA - IDMS Dimension ExL - IDMS O) 2P) Dimension RxL - IDMS Dimension Vista - IDMS Supplemental Figure 2. Difference of ln(concentration) for the routine measurement procedures vs. the IDMS procedure. The solid line represents the line of equivalence. The vertical hashed line represents the upper limit of the analytical measurement range for each routine procedure.

25 25 of Clinitek Advantus (mg/l) Supplemental Figure 3. Method Comparison results for the Clinitek Advantus vs. the IDMS procedure. Vertical dashed lines indicate the Clinitek semi-quantitative reportable values: 10, 30, 80 and 150 mg/l. Each sample (N=332) was measured in quadruplicate and results for 1,328 individual replicates are shown.

26 26 of 27 4A) Less than or equal to 30 mg/l % Difference (Routine Method - IDMS) Architect c4000 Architect c8000 Architect c16000 Vitros 5600 AU680 Inter AU680 US Unicel DxC 800 Immage Cobas c501 ADVIA 1650 BN II Immulite 2000 DCA Dimension ExL Dimension RxL Dimension Vista equiv 4B)

27 27 of 27 4C) Supplemental Figure 4. Percent differences of individual samples measured using the routine measurement procedures vs. the IDMS procedure. Mean values for replicate measurements of individual patient samples were used to calculate percent differences. A) concentration interval from the lower limit of quantification to 30 mg/l; B) concentration interval mg/l; C) concentration interval 201-2,000 mg/l.