Albuminuria: Time to Focus on Accuracy

Abstract

Related Articles, p. 405 and p. 415 Related Articles, p. 405 and p. 415 Albuminuria is common in chronic kidney disease (CKD). It is often the earliest marker of kidney damage and, in many circumstances, precedes any decline in glomerular filtration rate (GFR). The recent Kidney Disease: Improving Global Outcomes (KDIGO) guideline for the Evaluation and Management of Chronic Kidney Disease revised the classification system for CKD to include both albuminuria level and GFR for staging the severity of CKD.1Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work GroupKDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.Kidney Int Suppl. 2013; 3: 1-150Crossref Scopus (1615) Google Scholar This revision was based in part on data from large scale meta-analyses of studies of people with and without CKD that demonstrated the prognostic importance of albuminuria for kidney disease outcomes as well as for cardiovascular disease and mortality.2Astor B.C. Matsushita K. Gansevoort R.T. et al.Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts.Kidney Int. 2011; 79: 1331-1340Crossref PubMed Scopus (521) Google Scholar, 3Matsushita K. van der Velde M. Astor B.C. et al.Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.Lancet. 2010; 375: 2073-2081Abstract Full Text Full Text PDF PubMed Scopus (2860) Google Scholar, 4van der Velde M. Matsushita K. Coresh J. et al.Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts.Kidney Int. 2011; 79: 1341-1352Crossref PubMed Scopus (650) Google Scholar, 5Gansevoort R.T. Matsushita K. van der Velde M. et al.Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes in both general and high-risk populations. A collaborative meta-analysis of general and high-risk population cohorts.Kidney Int. 2011; 80: 93-104Crossref PubMed Scopus (554) Google Scholar The level of albuminuria is also relevant for management decisions. Patients with elevated levels of albuminuria may benefit from lower blood pressure targets and medications that block the renin angiotensin system, and in patients with nephrotic syndrome, physicians initiate therapy and monitor its response by the level of albuminuria or proteinuria.6Kidney Disease: Improving Global Outcomes (KDIGO) Glomerulonephritis Work GroupKDIGO clinical practice guideline for glomerulonephritis.Kidney Int Suppl. 2012; 2: 139-274Crossref Scopus (793) Google Scholar Successful implementation of these guidelines into clinical practice requires a simple and accurate way to measure albuminuria in routine care. The gold standard measure of albuminuria is albumin excretion rate (AER) in 24-hour urine specimens, but 24-hour urine collections samples are cumbersome and prone to errors. Thus, spot urine samples are recommended to quantify albuminuria; these adjust the measured urine albumin level for the urine creatinine level to account for differences in urine concentration or dilution.7Ralston S.H. Caine N. Richards I. O'Reilly D. Sturrock R.D. Capell H.A. Screening for proteinuria in a rheumatology clinic: comparison of dipstick testing, 24 hour urine quantitative protein, and protein/creatinine ratio in random urine samples.Ann Rheum Dis. 1988; 47: 759-763Crossref PubMed Scopus (47) Google Scholar, 8Claudi T. Cooper J.G. Comparison of urinary albumin excretion rate in overnight urine and albumin creatinine ratio in spot urine in diabetic patients in general practice.Scand J Prim Health Care. 2001; 19: 247-248Crossref PubMed Scopus (18) Google Scholar, 9Ginsberg J.M. Chang B.S. Matarese R.A. Garella S. Use of single voided urine samples to estimate quantitative proteinuria.N Engl J Med. 1983; 309: 1543-1546Crossref PubMed Scopus (626) Google Scholar The use of this simple equation (urine albumin-creatinine ratio [ACR] = urine albumin in mg/dL divided by urine creatinine in g/dL) is based on the assumption that creatinine excretion rate (CER) is 1 gram per day. Accordingly, an ACR of 1,000 mg/g would translate to an AER of 1,000 mg/d. However, CER varies substantially among individuals, so ACR is only a rough approximation of AER. Strategies to improve the accuracy of assessing albuminuria from measurements in spot samples could enhance its utility in both clinical practice and in research studies. The same issues apply to assessing proteinuria from measurements in spot samples. The studies by Fotheringham et al and Abdelmalek et al in this issue of AJKD, both of which propose methods to estimate AER from ACR, are timely and open the field to this important discussion.10Fotheringham J. Campbell M.J. Fogarty D.G. El Nahas M. Ellam T. Estimated albumin excretion rate versus urine albumin-creatinine ratio for the estimation of measured albumin excretion rate: derivation and validation of an estimated albumin excretion rate equation.Am J Kidney Dis. 2014; 63: 405-414Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar, 11Abdelmalek J.A. Gansevoort R.T. Lambers Heerspink H.J. Ix J.H. Rifkin D.E. Estimated albumin excretion rate versus urine albumin-creatinine ratio for the assessment of albuminuria: a diagnostic test study from the Prevention of Renal and Vascular Endstage Disease (PREVEND) Study.Am J Kidney Dis. 2014; 63: 415-421Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar Both studies address the question in similar ways. They both focus on that fact that variation in CER, which is largely a function of creatinine generation by muscle and diet, is the major source of error when using ACR to assess albuminuria. Accordingly, both studies calculate estimated CER (eCER) from prediction equations and then multiply that value by the ACR to compute an estimated AER (eAER). Across the 2 studies, 3 eCER equations (Box 1) are used: one developed in the Modification of Diet in Renal Disease (MDRD) Study population (its development is described by Fotheringham et al; it is referred to by Abdelmalek et al as eCEREllam and referred to here as eCERMDRD), one developed in a pooled dataset of several CKD populations (referred to by Abdelmalek et al as eCERIx and referred to here as eCERCKD-EPI), and one previously developed and used in clinical practice (eCERWalser).10Fotheringham J. Campbell M.J. Fogarty D.G. El Nahas M. Ellam T. Estimated albumin excretion rate versus urine albumin-creatinine ratio for the estimation of measured albumin excretion rate: derivation and validation of an estimated albumin excretion rate equation.Am J Kidney Dis. 2014; 63: 405-414Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar, 11Abdelmalek J.A. Gansevoort R.T. Lambers Heerspink H.J. Ix J.H. Rifkin D.E. Estimated albumin excretion rate versus urine albumin-creatinine ratio for the assessment of albuminuria: a diagnostic test study from the Prevention of Renal and Vascular Endstage Disease (PREVEND) Study.Am J Kidney Dis. 2014; 63: 415-421Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar, 12Ix J.H. Wassel C.L. Stevens L.A. et al.Equations to estimate creatinine excretion rate: the CKD epidemiology collaboration.Clin J Am Soc Nephrol. 2011; 6: 184-191Crossref PubMed Scopus (143) Google Scholar The equations differ slightly but are all based on demographic factors related to creatinine generation, such as sex, race, age, and weight. In particular, eCERMDRD does not use weight, whereas the other 2 equations do. Figure 1A shows the difference in eAER by level of measured ACR for white women and men of varying weights. Regardless of the equation used, for women of average weight, differences between ACR and eAER are relatively small. In contrast, for women at extremes of weight, eCERCKD-EPI and eCERWalser provide a substantially different eAER for any given ACR than eCERMDRD, and these differences are magnified at higher ACR levels. For men, eAER and ACR are similar for eCERWalser and eCERCKD-EPI at low weight and lower levels of ACR, but all equations give a higher eAER than ACR at both average and higher body weight, particularly at higher levels of ACR. Similar patterns were seen for black men and women. Thus using ACR and eCER from different equations will provide different eAERs, particularly at higher levels of ACR and for certain subgroups.Box 1Equations for the Estimation of Creatinine Excretion RateWalser Equation for Estimated Creatinine Excretion RateeCERWalser (mg/d) = Male: (28.2 − 0.172 × age) × weight (kg) Female: (21.9 − 0.115 × age) × weight (kg)MDRD Study (Ellam) Equation for Estimated Creatinine Excretion RateeCERMDRD (mg/d) = Male/black: 1413.9 + (23.2 × age) − (0.3 × age2) Female/black: 1148.6 + (15.6 × age) − (0.3 × age2) Male/nonblack: 1307.3 + (23.1 × age) − (0.3 × age2) Female/nonblack: 1051.3 + (5.3 × age) − (0.1 × age2)CKD-EPI (Ix) Equation for Estimated Creatinine Excretion RateeCERCKD-EPI (mg/d) = 879.89 + 12.51 × [weight (kg) − 6.19] × age+ 34.51 (if black) − 379.42 (if female) Walser Equation for Estimated Creatinine Excretion Rate eCERWalser (mg/d) = Male: (28.2 − 0.172 × age) × weight (kg) Female: (21.9 − 0.115 × age) × weight (kg) MDRD Study (Ellam) Equation for Estimated Creatinine Excretion Rate eCERMDRD (mg/d) = Male/black: 1413.9 + (23.2 × age) − (0.3 × age2) Female/black: 1148.6 + (15.6 × age) − (0.3 × age2) Male/nonblack: 1307.3 + (23.1 × age) − (0.3 × age2) Female/nonblack: 1051.3 + (5.3 × age) − (0.1 × age2) CKD-EPI (Ix) Equation for Estimated Creatinine Excretion Rate eCERCKD-EPI (mg/d) = 879.89 + 12.51 × [weight (kg) − 6.19] × age + 34.51 (if black) − 379.42 (if female) Fotheringham and colleagues evaluated 2 of the equations (eCERMDRD and eCERCKD-EPI) in 2 separate populations: CRIC (Chronic Renal Insufficiency Cohort), which includes individuals with CKD, and DCCT (Diabetes Control and Complications Trial), which includes individuals with diabetes who primarily do not have CKD. Abdelmalek and colleagues applied all 3 equations to a general population cohort, the PREVEND (Prevention of Renal and Vascular End-Stage Disease) Study. All 3 cohorts had fairly low levels of albuminuria (range of median AER, 7-64 mg/d). Studies evaluating prediction equations focus on 3 characteristics: bias, precision, and accuracy. Bias refers to a systematic difference between the estimated and measured values, precision is the spread or magnitude of the differences, and accuracy refers to the combination of bias and precision. Across all 3 populations, we can make 2 general conclusions about the performance of the eAER equation compared to measured AER. First, the eAER provides more accurate estimates than ACR alone. Second, eAER is unbiased compared to measured AER. However, accuracy, defined as the percentage of eAER values that differ by less than 30% from measured AER, varies across the populations (Fig 1). If the equations are unbiased and accuracy is variable, then this implies variable precision. There are potentially important differences in the gold standard and test sample used across these studies that may partially explain this variation. ACR was calculated in both CRIC and DCCT from 24-hour urine samples; accordingly, these studies may have seen better performance of eAER compared to measured AER than would have been found if spot samples had been used. In PREVEND, the 24-hour urine collections and the spot urine sample were not simultaneous, and true biological variation may partially explain some of the difference between eAER and measured AER in this population. My overall interpretation is that all 3 eAER equations are imprecise and therefore will be inaccurate when applied to at least some populations. In addition, there appears to be insufficient data to conclude whether one equation is substantially better than the others, although in the study by Abdelmalek et al, it appears that eCER equations that include weight (eCERCKD-EPI and eCERWalser) may be slightly more accurate than those do not include weight (eCERMDRD) at higher levels of weight. It would have been helpful to see these data in the other 2 populations as well. The cause of imprecision could be error in the gold standard (measured AER), the estimating equation, or the urine albumin or creatinine assay.13Stevens L.A. Zhang Y. Schmid C.H. Evaluating the performance of equations for estimating glomerular filtration rate.J Nephrol. 2008; 21: 797-807PubMed Google Scholar Errors both in the gold standard used to develop the CER equation as well as errors in the gold standard used to evaluate the eAER are relevant. Both gold standards are derived from values determined from 24-hour urine collections, which, as mentioned above, are prone to error despite investigators' best efforts. One important cause of imprecision in the estimating equations is individual variation in determinants of urine creatinine excretion that are not fully accounted for by the variables included in the equations; this could lead to bias in subgroups of the population and imprecision overall. Indeed, the higher accuracy in the DCCT population may suggest that these equations have reasonable accuracy in people with average weight and low levels of albuminuria. Finally, differences in creatinine assays between those used to develop the equation and those used to measure the ACR could potentially lead to error. However, this is more likely to lead to bias or systematic error than imprecision and is probably not a major factor in these studies. Since albumin was measured for the AER and ACR in the same lab and at approximately the same time, differences in albumin assays are not likely to be factors. What should we do now? First, the concept of estimating AER is sound and it is reasonable for clinicians to incorporate this process into clinical practice. In so doing, clinicians should understand that there remains much inaccuracy in the eAER, especially in particular populations. If we use the example of estimated GFR to guide us, then the next question that should be asked is whether one of these equations should be reported by clinical laboratories whenever a spot urine albumin is requested. In my opinion, this seems premature given the variable accuracy and particularly that there are no data at the higher albuminuria range; however, I do advocate a well-designed study to develop an equation suitable for general use and laboratory reporting. The ideal study design for the development of an estimating equation for AER should follow similar recommendations for the development of GFR estimating equations.13Stevens L.A. Zhang Y. Schmid C.H. Evaluating the performance of equations for estimating glomerular filtration rate.J Nephrol. 2008; 21: 797-807PubMed Google Scholar, 14Earley A. Miskulin D. Lamb E.J. Levey A.S. Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review.Ann Intern Med. 2012; 156: 785-795Crossref PubMed Scopus (368) Google Scholar First, it is critical to start with an accurate gold standard measurement. Given the errors in 24-hour urine measurements, the best approach would be to collect the urine under direct observation for 24 hours, repeat the collection, evaluate any large discrepancies between the collections, and consider the average of these 2 measurements to be the gold standard value. Second, given that the goal is to use these equations in populations that either have CKD or are at risk for CKD, representatives from diverse populations should be included in the development population. Third, careful consideration should be given to the variables used and their forms in developing the equation. Neither the paper by Fotheringham et al nor the study by Abdelmalek et al shows results by ACR level or creatinine excretion, but, reflecting that there may be different relationships by sex and weight between eAER and ACR (Fig 1), it is possible that equations that incorporate more complex variable forms and interactions among variables will better capture the true relationship. Fourth, evaluation of equation performance should be performed using spot samples and 24-hour collections conducted on the same day and should be performed in separate validation populations than those from which the equations were derived. Finally, use of an assay in both the gold standard and test samples should be traceable to higher level reference materials. In conclusion, Fotheringham and colleagues and Abdelmalek and colleagues should be congratulated for bringing this important topic to the forefront. Improved tools to estimate AER from spot urine samples may help us to prognosticate more efficiently, develop better treatments, and make better treatment decisions. Support: None. Financial Disclosure: Dr. Inker's institution receives funding from Pharmalink AB. Estimated Albumin Excretion Rate Versus Urine Albumin-Creatinine Ratio for the Estimation of Measured Albumin Excretion Rate: Derivation and Validation of an Estimated Albumin Excretion Rate EquationAmerican Journal of Kidney DiseasesVol. 63Issue 3PreviewGlomerular filtration rate estimation equations use demographic variables to account for predicted differences in creatinine generation rate. In contrast, assessment of albuminuria from urine albumin-creatinine ratio (ACR) does not account for these demographic variables, potentially distorting albuminuria prevalence estimates and clinical decision making. Full-Text PDF Estimated Albumin Excretion Rate Versus Urine Albumin-Creatinine Ratio for the Assessment of Albuminuria: A Diagnostic Test Study From the Prevention of Renal and Vascular Endstage Disease (PREVEND) StudyAmerican Journal of Kidney DiseasesVol. 63Issue 3PreviewAlbumin-creatinine ratio (ACR) in spot urine samples is recommended for albuminuria screening instead of measured albumin excretion rate (mAER) in 24-hour urine collections. In patients with extremes of muscle mass, differences in spot urine creatinine values may lead to under- or overestimation of mAER by ACR. We hypothesized that calculating estimated AER (eAER) using spot ACR and estimated creatinine excretion rate (eCER) may improve albuminuria assessment. Full-Text PDF