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Original Contribution |

Relation Between Kidney Function, Proteinuria, and Adverse Outcomes FREE

Brenda R. Hemmelgarn, MD, PhD; Braden J. Manns, MD, MSc; Anita Lloyd, MSc; Matthew T. James, MD; Scott Klarenbach, MD, MSc; Robert R. Quinn, MD, PhD; Natasha Wiebe, MMath, PStat; Marcello Tonelli, MD, SM; for the Alberta Kidney Disease Network
[+] Author Affiliations

Author Affiliations: Departments of Medicine (Drs Hemmelgarn, Manns, James, and Quinn) and Community Health Sciences (Drs Hemmelgarn, Manns, and James), University of Calgary, Calgary, Alberta, Canada; Departments of Medicine (Mss Lloyd and Wiebe and Drs Klarenbach and Tonelli) and Public Health Sciences (Drs Klarenbach and Tonelli), University of Alberta, Edmonton, Alberta; and Division of Nephrology, Foothills Medical Centre, Calgary, Alberta (Drs Hemmelgarn, Manns, James, and Quinn).


JAMA. 2010;303(5):423-429. doi:10.1001/jama.2010.39.
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Published online

Context The current staging system for chronic kidney disease is based primarily on estimated glomerular filtration rate (eGFR) with lower eGFR associated with higher risk of adverse outcomes. Although proteinuria is also associated with adverse outcomes, it is not used to refine risk estimates of adverse events in this current system.

Objective To determine the association between reduced GFR, proteinuria, and adverse clinical outcomes.

Design, Setting, and Participants Community-based cohort study with participants identified from a province-wide laboratory registry that includes eGFR and proteinuria measurements from Alberta, Canada, between 2002 and 2007. There were 920 985 adults who had at least 1 outpatient serum creatinine measurement and who did not require renal replacement treatment at baseline. Proteinuria was assessed by urine dipstick or albumin-creatinine ratio (ACR).

Main Outcome Measures All-cause mortality, myocardial infarction, and progression to kidney failure.

Results The majority of individuals (89.1%) had an eGFR of 60 mL/min/1.73 m2 or greater. Over median follow-up of 35 months (range, 0-59 months), 27 959 participants (3.0%) died. The fully adjusted rate of all-cause mortality was higher in study participants with lower eGFRs or heavier proteinuria. Adjusted mortality rates were more than 2-fold higher among individuals with heavy proteinuria measured by urine dipstick and eGFR of 60 mL/min/1.73 m2 or greater, as compared with those with eGFR of 45 to 59.9 mL/min/1.73 m2 and normal protein excretion (rate, 7.2 [95% CI, 6.6-7.8] vs 2.9 [95% CI, 2.7-3.0] per 1000 person-years, respectively; rate ratio, 2.5 [95% CI, 2.3-2.7]). Similar results were observed when proteinuria was measured by ACR (15.9 [95% CI, 14.0-18.1] and 7.0 [95% CI, 6.4-7.6] per 1000 person-years for heavy and absent proteinuria, respectively; rate ratio, 2.3 [95% CI, 2.0-2.6]) and for the outcomes of hospitalization with acute myocardial infarction, end-stage renal disease, and doubling of serum creatinine level.

Conclusion The risks of mortality, myocardial infarction, and progression to kidney failure associated with a given level of eGFR are independently increased in patients with higher levels of proteinuria.

Current guidelines classify chronic kidney disease (CKD) into 5 stages, based chiefly on estimated glomerular filtration rate (eGFR) (eTable 1).1 Adoption of the scheme has facilitated large-scale estimates of CKD prevalence, led to multiple studies examining the relation between CKD severity and clinical outcomes, and permitted a global effort to educate physicians and the public about the implications of CKD.2 Despite these benefits, the guidelines have been criticized because they do not incorporate information about the presence and severity of proteinuria, an important marker of CKD that is associated with adverse outcomes.35

As many as 26 million Americans have CKD, of whom almost 50% (10.1 million) have stage 1 or stage 2 CKD—in which eGFR is normal or nearly normal and CKD is defined by abnormal urinalysis or renal imaging studies.6 However, only 25% of Americans with proteinuria have overtly reduced eGFR (<60 mL/min/1.73 m2), and a similar proportion of those with low eGFR have proteinuria.7 Therefore, low eGFR and proteinuria do not always coexist, suggesting that eGFR and proteinuria could be used together to identify individuals at high risk.

We studied a large cohort of individuals receiving routine clinical care in a single Canadian province, in which all residents are covered by government-sponsored health insurance. We examined the association between reduced eGFR, proteinuria, and adverse clinical outcomes, including all-cause mortality, myocardial infarction, and progression to kidney failure. We hypothesized that patients with both reduced eGFR and proteinuria would be at higher risk of these outcomes than participants with one or neither characteristic.

The study population included all adults 18 years and older with at least 1 outpatient serum creatinine measurement in the province of Alberta, Canada, between May 1, 2002, and December 31, 2006, for 7 of the 9 geographically based provincial health regions, and between July 1, 2003, and January 1, 2005, and December 31, 2006, respectively, for the other 2 regions. Patients were excluded if they were treated with dialysis or a kidney transplant at baseline or if the baseline estimate of kidney function was clinically implausible (serum creatinine <0.28 mg/dL [multiply by 88.4 to get μmol/L]). To be eligible for inclusion, patients also had to have had at least 1 outpatient measure of proteinuria as described in this section. This study was facilitated by a previously described8 provincial laboratory repository: the Alberta Kidney Disease Network (AKDN).

Measurement of Kidney Function, Proteinuria, and Albuminuria

The eGFR for each patient was estimated using the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation.9 Although data on race were not available, misclassification of eGFR was expected to be minimal because less than 1% of the Alberta population is black.10 Baseline kidney function (index eGFR) was estimated using all outpatient serum creatinine measurements taken within a 6-month period of the first creatinine measurement, with the index eGFR defined as the mean of the measurements in this 6-month period. The date of the last serum creatinine measurement in the 6-month period was used as the index date for individuals with more than a single measurement.8 Index eGFR was categorized as 60 mL/min/1.73 m2 or greater, 45 to 59.9 mL/min/1.73 m2, 30 to 44.9 mL/min/1.73 m2, and 15 to 29.9 mL/min/1.73 m2. Because of inaccuracies in assessment of kidney function using the MDRD Study equation at higher levels of kidney function, and to permit comparisons with related studies,11 we categorized individuals with higher levels of function into 1 category (eGFR ≥60 mL/min/1.73 m2).

Proteinuria was captured by urine dipstick as well as albumin-creatinine ratio (ACR) based on outpatient random spot urine measurements. In the primary analysis, we included all patients with at least 1 urine dipstick measurement and defined proteinuria as normal (urine dipstick reading negative), mild (urine dipstick reading trace or 1+), or heavy (urine dipstick reading ≥2+).12 In sensitivity analyses, we considered an alternate definition of proteinuria based on ACR, defined as normal (ACR <30 mg/g), mild (ACR 30-300 mg/g), or heavy (ACR >300 mg/g).12

All outpatient urine dipstick and ACR measurements in the 6-month periods before and after the index eGFR were used to establish baseline proteinuria and albuminuria. Analyses used proteinuria or albuminuria as an ordinal variable according to these 3 categories, with the median of all respective measurements selected for each patient with multiple measurements.

Covariates

Demographic data were determined from the administrative data files of the provincial health ministry (Alberta Health and Wellness). Aboriginal race/ethnicity was determined from First Nations status in the registry file; it was not possible to identify other race/ethnic groups, although more than 85% of the Alberta population is white.10 Socioeconomic status was categorized as high income (annual adjusted taxable family income ≥CaD $39 250 [US $37 695]), low income (annual adjusted taxable family income <CaD $39 250), and low income with subsidy (receiving social assistance) based on government records.13 Diabetes mellitus and hypertension were identified from hospital discharge records and physician claims based on validated algorithms.14,15 Other comorbid conditions were identified using validated International Classification of Diseases, Ninth Revision, Clinical Modification, and International Statistical Classification of Diseases, Tenth Revision (ICD-10), coding algorithms applied to physician claims and hospitalization data.16 The presence of 1 or more diagnostic code in any position up to 3 years prior to cohort entry was used for identification of comorbidities.

Ascertainment of Outcomes

Patients were followed up from their index date until study end (March 31, 2007). The primary outcome of interest was all-cause mortality, as identified from the Alberta Health and Wellness Registry file. Secondary outcomes were first hospitalization for acute myocardial infarction17; occurrence of end-stage renal disease, defined as the date of registration for chronic dialysis or renal transplantation18; and the occurrence of an outpatient serum creatinine measurement that was twice as high as the first creatinine measurement during the study period (corresponding to a 50% decline in kidney function), assessed at the end of follow-up.

Statistical Analyses

Poisson regression was used to evaluate the association between the renal risk factors and each of the outcomes of interest, with output expressed as the rate per 1000 person-years. If the Poisson assumption that variance equals the mean was not met, a negative binomial model was used. We first calculated age-adjusted rates for each of the outcomes (all-cause mortality, hospitalization for myocardial infarction, end-stage renal disease, and doubling of serum creatinine) by level of eGFR and proteinuria, considering urine dipstick reading and ACR separately to classify proteinuria. We then calculated fully adjusted event rates for each of the outcomes, adjusting for the sociodemographic variables and comorbidities listed in Table 1. Two-way interactions between eGFR and proteinuria were assessed for all 4 clinical outcomes.

Table Graphic Jump LocationTable 1. Demographic and Clinical Characteristics of Participants by Level of Kidney Function or Proteinuriaa

The primary analysis was based on the cohort of participants who had data for proteinuria available from dipstick urinalysis. This analysis had greater than 99% statistical power (for α = .05) to detect a 10% increase in the likelihood of death among (1) individuals with eGFR of 60 mL/min/1.73 m2 or greater compared with those with eGFR of 15 to 29.9 mL/min/1.73 m2 and (2) individuals with heavy proteinuria compared with those with no proteinuria. In sensitivity analyses, we repeated statistical models for the subset of participants who had data for proteinuria based on urinary ACRs. We repeated analyses examining the relation between proteinuria and adverse outcomes in 2 subgroups of clinical interest: those with eGFR 45 to 59.9 mL/min/1.73 m2 (who account for the large majority of people with CKD) and those with “mildly reduced eGFR” as defined by current guidelines (eGFR 60-89.9 mL/min/1.73 m2).

We performed sensitivity analyses in strata defined by participant age (≥65 and <65 years). In all analyses, we performed tests for linear trend across categories of proteinuria and eGFR. The variables used to calculate the tests for trend in eGFR and ACR were defined by the median values of these parameters in each category. The variable used to calculate the test for trend in dipstick-measured proteinuria was defined by values of 1, 2, and 3 for normal, mild, and heavy proteinuria, respectively.20 Finally, we repeated the analysis using eGFR and ACR as continuous variables, with ACR log-transformed because of its skewed distribution. Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina) and Stata version 10.1 (StataCorp, College Station, Texas). A P value of <.05 was used to indicate statistical significance without adjustment for multiple comparisons. The institutional review boards of the University of Calgary and University of Alberta approved the study and granted waiver of patient consent.

A total of 1 530 447 participants had at least 1 outpatient serum creatinine measurement during the study period. We excluded 2345 people with end-stage renal disease prior to cohort entry and 1383 with index eGFR of less than 15 mL/min/1.73 m2. An additional 282 individuals were excluded because they either died or reached end of follow-up on their index date. Of the 1 526 437 participants, 920 985 (60.3%) had at least 1 urine dipstick measurement and 102 701 (6.7%) had at least 1 ACR measurement. Characteristics of the participants by level of eGFR and proteinuria are shown in Table 1. The majority of individuals (89.1%) in the primary analysis with proteinuria measured by urine dipstick had an eGFR of 60 mL/min/1.73 m2 or greater.

A total of 102 701 participants had at least 1 urinary ACR measurement performed; individuals in this subset were older (mean [SD] age, 57.0 [15.0] years vs 48.0 [16.6] years) and more likely to be male (54.5% vs 43.4%) or diabetic (54.1% vs 3.4%) and had a higher mean (SD) Charlson score (0.94 [1.6] vs 0.43 [1.1]) than those without such measurements (all P < .001; χ2 test and t test for categorical and continuous variables, respectively). A higher proportion of participants in this subset had mild (19.7% vs 7.3%) or heavy proteinuria (5.1% vs 1.1%) than in those without measurements of urinary ACR (both P < .001, χ2 test).

Age-Adjusted Likelihood of Clinical Outcomes by Level of eGFR and Proteinuria

During median follow-up of 35 months (range, 0-59 months), 27 959 participants (3.0%) died, 5772 (0.6%) were hospitalized for myocardial infarction, 771 (0.08%) initiated renal replacement therapy, and 2514 (0.4%) experienced a doubling of serum creatinine. The age-adjusted rates of these outcomes were all increased at lower levels of eGFR and at heavier proteinuria (eTable 2 and eTable 3).

Adjusted Likelihood of Clinical Outcomes by Level of eGFR and Proteinuria

Within each stratum of eGFR, there was substantial variability in risk with participants who had heavier proteinuria having markedly increased adjusted rates of all 4 adverse outcomes (Table 2; eFigure 1). The adjusted mortality risk was more than 2-fold higher among individuals with heavy proteinuria and eGFR of 60 mL/min/1.73 m2 or greater as compared with those with eGFR of 45 to 59.9 mL/min/1.73 m2 and normal protein excretion (rate ratio, 2.5; 95% confidence interval [CI], 2.3-2.7). Significant interactions between eGFR and proteinuria were observed for death, initiation of renal replacement, and doubling of serum creatinine—such that the additional risk of heavier proteinuria was reduced at lower eGFR (all P for interaction statistically significant at <.001)—but not for myocardial infarction (P for interaction, .08). However, the difference in risk associated with moderate or heavy proteinuria (as compared with those without proteinuria) appeared clinically relevant within every eGFR stratum and for all 4 clinical outcomes.

Table Graphic Jump LocationTable 2. Adjusted Rates Per 1000 Person-Years of Clinical Outcomes by Level of eGFR and Proteinuria Measured by Dipsticka
Sensitivity Analyses

Results were consistent when analyses were restricted to the subset of 102 701 participants who had urinary ACR measurements performed (Table 3; eFigure 2). Specifically, risk increased progressively at levels of eGFR below 60 mL/min/1.73 m2 and with mild or heavy proteinuria within all eGFR strata—for all 4 clinical outcomes (adjusted rate ratio for mortality, 2.3 [95% CI, 2.0-2.6] for individuals with heavy proteinuria and eGFR of 60 mL/min/1.73 m2 or greater as compared with those with eGFR of 45 to 59.9 mL/min/1.73 m2 and normal protein excretion). Next we repeated analyses using a more conservative definition of heavy proteinuria (ACR >2000 mg/g). Compared with those without significant proteinuria, participants with ACRs greater than 2000 mg/g had markedly elevated rates of adverse outcomes. For example, among participants with eGFRs of 45 to 59.9 mL/min/1.73 m2, those with heavy proteinuria by this definition had adjusted rates of 21.5 (95% CI,15.5-29.9), 11.2 (95% CI, 6.4-19.8), and 27.9 (95% CI, 17.6-44.2) per 1000 person-years, respectively, for mortality, myocardial infarction, and initiation of renal placement therapy, as compared with rates of 7.0 (95% CI, 6.3-7.6), 3.7 (95% CI, 3.2-4.3), and 0.3 (95% CI, 0.2-0.6), respectively, for those without proteinuria.

Table Graphic Jump LocationTable 3. Adjusted Rates Per 1000 Person-Years of Clinical Outcomes by Level of eGFR and Proteinuria Measured by Albumin-Creatinine Ratioa

Because current guidelines for the classification of CKD describe eGFR between 60 and 90 mL/min/1.73 m2 as “mildly reduced,” we examined the prognostic value of proteinuria within this category specifically. Among the 597 870 participants, a graded increase in the adjusted rate of all-cause mortality was seen with rates of 2.2 (95% CI, 2.1-2.3), 4.3 (95% CI, 4.1-4.6), and 5.1 (95% CI, 4.7-5.6) per 1000 person-years among participants with no, mild, or heavy proteinuria, respectively (P for trend <.001). Similar findings were seen for the outcomes of myocardial infarction (rates, 1.0 [95% CI, 0.9-1.0], 1.4 [95% CI, 1.2-1.5], and 1.6 [95% CI, 1.2-1.9]; P for trend <.001), initiation of renal replacement therapy (rates, 0.02 [95% CI, 0.02-0.03], 0.04 [95% CI, 0.02-0.09], and 0.8 [95% CI, 0.5-1.3]; P for trend <.001), or doubling of serum creatinine (rates, 0.3 [95% CI, 0.3-0.4], 0.9 [95% CI, 0.7-1.1], and 2.8 [95% CI, 2.2-3.6]; P for trend <.001).

Because there has been controversy about whether the prognostic implications of CKD vary in younger and older populations, we repeated analyses stratifying on age. All findings were similar among participants who were 65 years and older as compared with those who were younger. Specifically, the risk of all 4 clinical outcomes increased significantly in both age strata with declining eGFR (all P for trend <.001), as well as with heavier proteinuria (all P for trend <.001).

Finally, results with eGFR and ACR as continuous variables were consistent with categorical analyses. The increase in adjusted rate per 10–mL/min/1.73 m2 decrease in eGFR was most pronounced for the outcome end-stage renal disease, followed by doubling of serum creatinine, myocardial infarction, and all-cause mortality (increase in rates, 2.17 [95% CI, 2.02-2.34], 1.15 [95% CI, 1.10-1.19], 1.09 [95% CI, 1.05-1.12], and 1.04 [95% CI, 1.03-1.06], respectively). Similar findings were seen per 10-fold increase in ACR with an increase in adjusted rates of 1.92 (95% CI, 1.81-2.04), 1.76 (95% CI, 1.70-1.82), 1.18 (95% CI, 1.14-1.21), and 1.22 (95% CI, 1.21-1.24), respectively, for initiation of renal replacement therapy, doubling of serum creatinine, myocardial infarction, and all-cause mortality.

In this large, community-based cohort of all adults undergoing laboratory testing in a single Canadian province, we demonstrated that prognosis associated with a given level of eGFR varies substantially based on the presence and severity of proteinuria. In fact, patients with heavy proteinuria but without overtly abnormal eGFR appeared to have worse clinical outcomes than those with moderately reduced eGFR but without proteinuria. Results were consistent for 2 different measures of proteinuria; consistent for several clinically relevant outcomes, including all-cause mortality, myocardial infarction, and the need for renal replacement; and robust to multivariable adjustment and a variety of sensitivity analyses.

These findings are important because current guidelines for the classification and staging of CKD are based on eGFR without explicit consideration of the severity of concomitant proteinuria.1 In addition, computerized reporting of eGFR (generally without consideration of proteinuria) is increasingly used to assist physicians in identifying patients at high risk of adverse outcomes—or those who might benefit from specialist care.21 Although our findings do not directly address which patients would benefit from referral to a nephrologist, they do suggest that risk stratification performed in terms of eGFR alone is relatively insensitive to clinically relevant gradients in risk.

Staging systems for the classification of disease are often used to group affected persons into categories that are associated with similar prognoses, generally in a fashion that assigns people with worse prognoses to more advanced stages.22 Although the introduction of the NKF-K/DOQI (National Kidney Foundation/Dialysis Outcomes Quality Initiative) scheme for classification of CKD represented a major advance for researchers and clinicians, our findings suggest that this scheme does not meet these 2 criteria. For example, the age-adjusted rates of all-cause mortality and kidney failure appear to vary up to 4- and 50-fold (depending on the severity of proteinuria) within a given stage as defined by the current scheme. Similarly, a patient with an eGFR of 80 mL/min/1.73 m2 and 3+ proteinuria on dipstick reading (or ACR of 400 mg/g) would be assigned to stage 1 CKD under the current system—even though his or her age-adjusted risks of death and the need for renal replacement therapy would be approximately 2 and 10 times higher, respectively, than an otherwise similar patient with an eGFR of 50 mL/min/1.73 m2 but no evidence of proteinuria (stage 3 disease).

This latter finding is particularly striking given the high prevalence of stage 3 CKD (defined by eGFRs of 30-59.9 mL/min/1.73 m2 with or without proteinuria) in our study, which accounts for the large majority of North American individuals with CKD.6 An additional finding of our analysis is that the risk is heterogeneous within this stage, even when it is defined by eGFR alone. As previously reported, the risk of all-cause mortality in our study was markedly higher among participants with eGFRs of 30 to 44.9 mL/min/1.73 m2 than among those with eGFRs of 45 to 59.9 mL/min/1.73 m2.11 Our data extend this finding to other adverse outcomes, including myocardial infarction and progression to kidney failure. The heterogeneity of risk among the large number of people currently classified as having stage 3 CKD (even when stratified by proteinuria) suggest that consideration should be given to subdividing this stage as done in our analysis, as well as by proteinuria. Focusing clinical attention on people at highest risk (as defined by the intersection of eGFR and proteinuria) may prove to be a more cost-effective approach to preventing the complications of CKD, although further work is required to confirm this hypothesis.

Although other equations23 and serum markers24 are available for estimating GFR, we used the MDRD Study equation because it is the most widely used at present. Current practice in Western countries emphasizes the use of ACR rather than dipstick urinalysis in the assessment of CKD.1 Although dipstick urinalysis has less favorable diagnostic properties than ACR for the assessment of proteinuria,25 it is also considerably less expensive. Our results suggest that dipstick urinalysis adds considerable prognostic information to that associated with eGFR alone—and the magnitude of excess risk observed with heavy proteinuria appeared similar whether assessed by dipstick or by ACR. Because the majority of people with CKD worldwide live in low- or middle-income nations,26 our data support the further study of dipstick urinalysis as a valid alternative to ACR for risk stratification in resource-limited settings.

Our study has limitations due to its observational nature. First, the cohort was limited to individuals who had an outpatient serum creatinine measurement and a measure of urinary protein performed as part of routine care—and therefore does not include individuals who did not use medical services. However, since we studied nearly 1 million individuals, and considering the universal nature of health care coverage in Alberta, this limitation is unlikely to invalidate our finding that proteinuria adds substantial prognostic value to that associated with eGFR alone. Second, proteinuria and albuminuria may have been misclassified because of the known variability of these measurements based on a single measurement.12 However, we attempted to reduce this misclassification by using all urine measurements in a 6-month period before and after the index eGFR. In addition, results were robust to use of 2 different measures of proteinuria and were consistent for multiple clinically relevant outcomes.

Third, we assessed the incidence of doubling of serum creatinine during follow-up, which may have included some participants with acute renal failure as well as those with progression of CKD. However, since we excluded inpatient measurements of serum creatinine, and given that the prevalence of acute renal failure in the community is less than 1%,27 this likely accounted for the minority of such events—although this degree of kidney function loss is clinically relevant whether due to acute or chronic disease.28 Fourth, the follow-up in our study was relatively short to assess progression to kidney failure, especially for people with higher levels of baseline eGFR, although this is unlikely to have threatened the validity of our conclusions. Finally, we did not have information on characteristics such as use of alcohol, tobacco, and antihypertensive medications, which may have resulted in residual confounding. However, given the magnitude of the effect sizes observed in our study, it is unlikely that further adjustment for these covariates would negate the observed associations.

In conclusion, we found that the risks of death, myocardial infarction, and progression to kidney failure at a given level of eGFR were independently increased in individuals with higher levels of proteinuria. These findings suggest that future revisions of the classification system for CKD should incorporate information from proteinuria.

Corresponding Author: Brenda R. Hemmelgarn, MD, PhD, Division of Nephrology, Foothills Medical Centre, 1403 29th St NW, Calgary, AB T2N 2T9, Canada (brenda.hemmelgarn@albertahealthservices.ca).

Author Contributions: Dr Hemmelgarn had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Hemmelgarn, Klarenbach, Wiebe, Tonelli.

Acquisition of data: Hemmelgarn, James, Quinn.

Analysis and interpretation of data: Hemmelgarn, Manns, Lloyd, James, Klarenbach, Quinn, Wiebe, Tonelli.

Drafting of the manuscript: Hemmelgarn, Tonelli.

Critical revision of the manuscript for important intellectual content: Hemmelgarn, Manns, Lloyd, James, Klarenbach, Quinn, Wiebe, Tonelli.

Statistical analysis: Hemmelgarn, Lloyd, James, Quinn, Wiebe, Tonelli.

Obtained funding: Hemmelgarn, Manns, Tonelli.

Administrative, technical, or material support: Hemmelgarn, James, Quinn.

Study supervision: Hemmelgarn, Wiebe, Tonelli.

Financial Disclosures: None reported.

Funding/Support: Drs Hemmelgarn, Tonelli, and Klarenbach were supported by awards from the Alberta Heritage Foundation for Medical Research (AHFMR). Drs Hemmelgarn, Manns, and Tonelli were also supported by awards from the Canadian Institutes of Health Research. Drs Hemmelgarn, Manns, Klarenbach, and Tonelli were supported by a joint initiative between Alberta Health and Wellness and the Universities of Alberta and Calgary. Dr James was supported by a KRESCENT and AHFMR Fellowship. This work was supported by a grant from the AHFMR Interdisciplinary Team Grants Program to Drs Hemmelgarn, Manns, and Tonelli.

Role of the Sponsor: The funding organizations had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

A list of the Alberta Kidney Disease Network members appears at http://www.AKDN.info.

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Akbari A, Swedko PJ, Clark HD,  et al.  Detection of chronic kidney disease with laboratory reporting of estimated glomerular filtration rate and an educational program.  Arch Intern Med. 2004;164(16):1788-1792
PubMed   |  Link to Article
Chen ML, Hsu CY. Should the K/DOQI definition of chronic kidney disease be changed?  Am J Kidney Dis. 2003;42(4):623-625
PubMed   |  Link to Article
Levey AS, Stevens LA, Schmid CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612
PubMed   |  Link to Article
Shlipak MG, Sarnak MJ, Katz R,  et al.  Cystatin C and the risk of death and cardiovascular events among elderly persons.  N Engl J Med. 2005;352(20):2049-2060
PubMed   |  Link to Article
Ciavarella A, Silletti A, Forlani G,  et al.  A screening test for microalbuminuria in type 1 (insulin-dependent) diabetes.  Diabetes Res Clin Pract. 1989;7(4):307-312
PubMed   |  Link to Article
Hossain MP, Goyder EC, Rigby JE, El Nahas M. CKD and poverty.  Am J Kidney Dis. 2009;53(1):166-174
PubMed   |  Link to Article
Feest TG, Round A, Hamad S. Incidence of severe acute renal failure in adults.  BMJ. 1993;306(6876):481-483
PubMed   |  Link to Article
Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long-term risk of mortality and other adverse outcomes after acute kidney injury.  Am J Kidney Dis. 2009;53(6):961-973
PubMed   |  Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. Demographic and Clinical Characteristics of Participants by Level of Kidney Function or Proteinuriaa
Table Graphic Jump LocationTable 2. Adjusted Rates Per 1000 Person-Years of Clinical Outcomes by Level of eGFR and Proteinuria Measured by Dipsticka
Table Graphic Jump LocationTable 3. Adjusted Rates Per 1000 Person-Years of Clinical Outcomes by Level of eGFR and Proteinuria Measured by Albumin-Creatinine Ratioa

References

 K/DOQI clinical practice guidelines for chronic kidney disease.  Am J Kidney Dis. 2002;39:(2 suppl 1)  S1-S266
PubMed
Hsu CY, Chertow GM. Chronic renal confusion.  Am J Kidney Dis. 2000;36(2):415-418
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Hillege HL, Fidler V, Diercks GF,  et al; Prevention of Renal and Vascular End Stage Disease (PREVEND) Study Group.  Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population.  Circulation. 2002;106(14):1777-1782
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Klausen K, Borch-Johnsen K, Feldt-Rasmussen B,  et al.  Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes.  Circulation. 2004;110(1):32-35
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Mann JF, Gerstein HC, Pogue J, Bosch J, Yusuf S. Renal insufficiency as a predictor of cardiovascular outcomes and the impact of ramipril.  Ann Intern Med. 2001;134(8):629-636
PubMed   |  Link to Article
Coresh J, Selvin E, Stevens LA,  et al.  Prevalence of chronic kidney disease in the United States.  JAMA. 2007;298(17):2038-2047
PubMed   |  Link to Article
Garg AX, Kiberd BA, Clark WF, Haynes RB, Clase CM. Albuminuria and renal insufficiency prevalence guides population screening.  Kidney Int. 2002;61(6):2165-2175
PubMed   |  Link to Article
Hemmelgarn BR, Clement F, Manns BJ,  et al.  Overview of the Alberta Kidney Disease Network.  BMC Nephrol. 2009;10:30
PubMed   |  Link to Article
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D.Modification of Diet in Renal Disease Study Group.  A more accurate method to estimate glomerular filtration rate from serum creatinine.  Ann Intern Med. 1999;130(6):461-470
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 Ethnocultural portrait of Canada highlight tables, 2006 census. Statistics Canada. http://www12.statcan.ca/english/census06/data/highlights/ethnic/index.cfm?Lang=E. Accessed June 15, 2009
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305
PubMed   |  Link to Article
Lamb EJ, MacKenzie F, Stevens PE. How should proteinuria be detected and measured?  Ann Clin Biochem. 2009;46(pt 3):205-217
PubMed   |  Link to Article
Sin DD, Svenson LW, Cowie RL, Man SF. Can universal access to health care eliminate health inequities between children of poor and nonpoor families?  Chest. 2003;124(1):51-56
PubMed   |  Link to Article
Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario.  Diabetes Care. 2002;25(3):512-516
PubMed   |  Link to Article
Quan H, Khan N, Hemmelgarn BR,  et al; Hypertension Outcome and Surveillance Team of the Canadian Hypertension Education Programs.  Validation of a case definition to define hypertension using administrative data.  Hypertension. 2009;54(6):1423-1428
PubMed   |  Link to Article
Quan H, Sundararajan V, Halfon P,  et al.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.  Med Care. 2005;43(11):1130-1139
PubMed   |  Link to Article
Austin PC, Daly PA, Tu JV. A multicenter study of the coding accuracy of hospital discharge administrative data for patients admitted to cardiac care units in Ontario.  Am Heart J. 2002;144(2):290-296
PubMed   |  Link to Article
Manns BJ, Mortis GP, Taub KJ, McLaughlin K, Donaldson C, Ghali WA. The Southern Alberta Renal Program database.  Clin Invest Med. 2001;24(4):164-170
PubMed
 Premium assistance program: premium subsidy. Alberta Health and Wellness. http://www.health.alberta.ca/AHCIP/premium-subsidy.html. Accessed May 2, 2008
Altman D. Practical Statistics for Medical Research (Statistics Texts). New York, NY: Chapman & Hall/CRC; 1990
Akbari A, Swedko PJ, Clark HD,  et al.  Detection of chronic kidney disease with laboratory reporting of estimated glomerular filtration rate and an educational program.  Arch Intern Med. 2004;164(16):1788-1792
PubMed   |  Link to Article
Chen ML, Hsu CY. Should the K/DOQI definition of chronic kidney disease be changed?  Am J Kidney Dis. 2003;42(4):623-625
PubMed   |  Link to Article
Levey AS, Stevens LA, Schmid CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612
PubMed   |  Link to Article
Shlipak MG, Sarnak MJ, Katz R,  et al.  Cystatin C and the risk of death and cardiovascular events among elderly persons.  N Engl J Med. 2005;352(20):2049-2060
PubMed   |  Link to Article
Ciavarella A, Silletti A, Forlani G,  et al.  A screening test for microalbuminuria in type 1 (insulin-dependent) diabetes.  Diabetes Res Clin Pract. 1989;7(4):307-312
PubMed   |  Link to Article
Hossain MP, Goyder EC, Rigby JE, El Nahas M. CKD and poverty.  Am J Kidney Dis. 2009;53(1):166-174
PubMed   |  Link to Article
Feest TG, Round A, Hamad S. Incidence of severe acute renal failure in adults.  BMJ. 1993;306(6876):481-483
PubMed   |  Link to Article
Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long-term risk of mortality and other adverse outcomes after acute kidney injury.  Am J Kidney Dis. 2009;53(6):961-973
PubMed   |  Link to Article

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