Our understanding of factors associated with the progression of CKD has increased substantially over time (1,2). Traditionally, most studies of CKD have provided metrics of disease progression in the form of absolute loss of eGFR or percentage of kidney function decline per year (3–9). However, for most patients with CKD, the most relevant and understandable metric of disease progression is the estimated amount of time before the onset of symptoms, need for dialysis or transplant, or onset of CKD-associated complications. Likewise, providers often need to estimate the amount of time before the need to discuss options for RRT, plan for dialysis access placement or transplantation, and monitor for CKD-related complications (1). Evidence-based estimates of the projected duration of each CKD stage could be valuable to patients and providers, providing critical context for discussions of prognosis and a framework to discuss therapeutic interventions or behavioral changes that could delay disease progression.
Although tools such as the Kidney Failure Risk Equation (10) are now available to predict risk of developing ESKD within specific timeframes (2 or 5 years), these predictions do not address the amount of time a patient may have before ESKD occurs. Furthermore, known risk factors for CKD progression (such as diabetes, hypertension, or proteinuria) may not have uniform associations with disease progression across all CKD stages, and prediction models that provide risk on the basis of fixed timeframes cannot capture such differences (11).
Therefore, our objective was to provide estimates of the time spent in each CKD stage during observational follow-up of participants of the Chronic Renal Insufficiency Cohort (CRIC) study. We also aimed to determine differences in the amount of time spent in each CKD stage, using individual risk factors and combinations of important risk factors.
Materials and Methods
The CRIC study is a national, multicenter, observational cohort study that enrolled patients from clinical centers located throughout the United States (12). Participants with eGFR between 20 and 70 ml/min per 1.73 m2 were recruited for study between 2003 and 2008. The inclusion and exclusion criteria have been previously published (12,13), and data in this study were censored as of March 31, 2013.
Modeling Kidney Function Trajectory across CKD Stages
We estimated kidney function using the Chronic Kidney Disease Epidemiology Collaboration creatinine-based equation (14). We used creatinine to estimate eGFR because it is the biomarker most commonly used in clinical practice and therefore results can be most easily extrapolated to patient care. We used linear mixed modeling to estimate changes in eGFR over time from CRIC study enrollment until ESKD onset (defined as receipt of dialysis or transplant, or predicted time to onset of eGFR<10 ml/min per 1.73 m2 if dialysis or transplant did not occur). Our mixed models included person-specific linear and quadratic time terms to accommodate nonlinearities in eGFR trajectory and provide a flexible fit for each individual. We adjusted these mixed models for age at the time of cohort entry and race (black versus nonblack). We assessed the quality of the model fit for each person by plotting the fitted and actual values (Supplemental Figure 1). We tested for the adequacy of our model fit by determining the percent of variability that was unexplained using this mixed model.
Determination of Estimated Time Spent in Each CKD Stage
To determine the amount of time spent in each stage of CKD (stages 3a, 3b, 4, and 5, indicated by decline of eGFR below 45, 30, and 15 ml/min per 1.73 m2, and ESKD, respectively), we used the person-specific trajectories from the mixed model to identify transitions to each subsequent CKD stage as separate outcomes of interest. We allowed for person-specific trajectories to extend beyond the first or last available eGFR by no more than 25% of the total follow-up duration to ensure reasonable fits. Extrapolation beyond the last available eGFR was only performed among the subset not known to have died during CRIC study follow-up. From these trajectories, we calculated the amount of time spent in each CKD stage before transition to a subsequent stage (if a transition occurred) using our fitted models. We limited this analysis to participants whose entry into each stage of CKD was known (n=257 excluded).
We censored participants who did not transition to a subsequent CKD stage by the end of follow-up. Those who died contributed time to each stage of CKD up until their death. We used Weibull parametric models to determine the median and interquartile ranges of the time spent in each CKD stage.
Examination of Time Spent in Each CKD Stage according to Risk Factors of Interest
To determine the amount of time spent in each CKD stage on the basis of the presence or absence of risk factors known to be associated with CKD progression, we used each factor of interest as a predictor of transition to the next CKD stage in separate, univariable Weibull parametric survival models. We chose to use univariable analyses as our primary models because we were interested in estimating differences in the amount of time spent in each CKD stage on the basis of the presence or absence of individual risk factors of interest, as might be provided to patients during a clinic visit. We examined age at entry into each CKD stage (≥60 years versus <60 years), sex, race, and ethnicity (non-Hispanic white, black, or Hispanic) (15). We also examined time-varying factors of interest including hemoglobin A1c (<7.5% or ≥7.5% among participants with diabetes, excluding participants without diabetes), urine protein-to-creatinine ratio (<1 g/g or ≥1 g/g), obese (body mass index ≥30 kg/m2) versus not obese (body mass index <30 kg/m2), current smoker versus nonsmoker, use of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARB), and systolic BP ≥140 or <140 mm Hg. For these time-varying covariates, we used the first values available upon entry into each CKD stage to define their presence or absence. We used bootstrapping with 1000 repetitions to derive 95% confidence intervals (95% CIs) for differences in the median times derived using our parametric models by various risk factors of interest.
Combined Risk Factors
We also examined the time spent in each CKD stage according to different combinations of the risk factors associated with the greatest differences in time spent in each CKD stage, including hemoglobin A1c, systolic BP, and proteinuria, adjusted for age (as a categorical variable) and sex.
Interaction between Risk Factors and Stage of CKD
To determine whether risk factors had varying associations with the risk of CKD progression by stage of disease, we used multivariable Cox models including all risk factors described above to determine the risk of transition to each subsequent stage of CKD. We then tested for the presence of interaction between each risk factor and CKD stages in univariable analysis. We also derived equations from our Weibull models to predict the median amount of time spent in each stage of CKD on the basis of a combination of demographic factors (age and sex), along with the strongest clinical factors (diabetes, proteinuria, and systolic BP) associated with progression of CKD.
We used deidentified data from the National Institute of Diabetes and Digestive and Kidney Disease Central Repository for analysis. The Instiutional Review Board of the University of California, San Francisco considers this study to be exempt human subjects research. All analyses were conducted using Stata 14 (StataCorp., College Station, TX).
Participant Characteristics and Survival Analysis across Stages of CKD
A total of 3682 of 3939 CRIC study participants (93%) were included in our analyses. We excluded 257 participants from our analysis if their entry into at least one stage of CKD was not known. Baseline characteristics of included participants are shown in Table 1. Overall, 42% were black and half had diabetes at the time of enrollment. Nearly half of participants in CKD stages 3a, 3b, and 4 transitioned to the subsequent stage, and almost 90% of participants in stage 5 transitioned to ESKD (Supplemental Figure 2, Table 2).
Table 1. -
Baseline characteristics of Chronic Renal Insufficiency Cohort participants (n
=3682) included for analysis
||Mean±SD or N (%)
|Body mass index, kg/m2
|Any cardiovascular disease
|Median proteinuria (g/g creatinine) [IQR]
|ACE inhibitor or ARB use
Number in each stage of CKD
| Stage 2
| Stage 3a
| Stage 3b
| Stage 4
| Stage 5
IQR, interquartile range; ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker.
aMissing in n=8.
bMissing in n=26.
cStage 2: eGFR= 60 to <90 ml/min per 1.73 m2; stage 3a: eGFR= 45 to <60 ml/min per 1.73 m2; stage 3b: eGFR= 30 to <45 ml/min per 1.73 m2; stage 4: eGFR= 15 to <30 ml/min per 1.73 m2; stage 5: eGFR<15 ml/min per 1.73 m2.
Table 2. -
Time to progression to more advanced CKD stage among participants in the Chronic Renal Insufficiency Cohort study, according to initial CKD stage
|Stage of CKD
||Median Time [IQR] or N
| Entered CKD stage 3a
| Transitioned to stage 3b
| Median time spent in each stage, yr
||7.9 [2.3 to >12
| Entered CKD stage 3b
| Transitioned to stage 4
| Median time spent in each stage, yr
| Entered CKD stage 4
| Transitioned to stage 5
| Median time spent in each stage, yr
| Entered CKD stage 5
| Transitioned to ESKD
| Median time spent in each stage, yr
IQR, interquartile range.
aStage 2: eGFR= 60 to <90 ml/min per 1.73 m2; stage 3a: eGFR= 45 to <60 ml/min per 1.73 m2; stage 3b: eGFR= 30 to <45 ml/min per 1.73 m2; stage 4: eGFR= 15 to <30 ml/min per 1.73 m2; stage 5: eGFR<15 ml/min per 1.73 m2.
bFollow-up time censored at 12 yr.
We examined the fit of our person-specific eGFR trajectories and noted that the percent variability that was unexplained by our mixed model was only 9.8%.
Time Spent in CKD Stages
Participants spent progressively less time in each successive stage of CKD; a median of 7.9, 5, 4.2, and 0.8 years in CKD stages 3a, 3b, 4, and 5, respectively (Figure 1, Table 2). The variation in the time spent in each of the CKD stages was larger during the earlier stages compared with the later stages of CKD in univariable analysis (Figure 1, Supplemental Figure 3).
The amount of time spent in CKD stage 3a was shorter for older patients compared with younger patients, but the amount of time spent in CKD stage 3b was longer for older patients compared with younger patients (Figure 1, A and B). Black race and Hispanic ethnicity were both associated with substantially shorter duration of time spent in CKD stages 3a and 3b compared with white race (Figure 1, A and B).
Differences in the median duration of CKD stage 3a and 3b were observed among those with elevated hemoglobin A1c or elevated systolic BP: participants spent 1.8 years (95% CI, 0.4 to 3.2) less time in CKD stage 3a and 1.4 years (95% CI, 0.5 to 2.1) less time in stage 3b if they had poorly controlled diabetes, and those with systolic BP ≥140 mm Hg spent 6.1 years (95% CI, 4.5 to 7.5) less in stage 3a and 3.3 years (95% CI, 2.7 to 4.0) less in stage 3b than those with systolic BP <140 mm Hg (Figure 1, A and B). CKD stage 3a was approximately 8.2 years (95% CI, 6.8 to 9.6) shorter and CKD stage 3b was 5.6 years (95% CI, 5.0 to 6.4) shorter in those with ≥1 g/g of proteinuria compared with those with <1 g/g of proteinuria (Figure 1, A and B).
Considering later stages of CKD, age <60 years and male sex were associated with shorter duration of CKD stages 4 and 5 (Figure 1, C and D). Other prominent differences in time spent in CKD stage 4 and 5 were noted among those with elevated hemoglobin A1c (among those with diabetes), proteinuria, or elevated systolic BP. However, the absolute differences in time were considerably smaller than differences observed in the earlier stages of CKD (Figure 1, C and D). For example, there was a 3.3 year (95% CI, 2.9 to 3.8) difference in the median time spent in stage 4 and a 6 month (95% CI, 3.8 to 8) difference in the median time spent in stage 5 among those with and without substantial proteinuria (Figure 1, C and D).
Select Combined Risk Factors
When we considered hemoglobin A1c and proteinuria in combination in age- and sex-adjusted analyses, we found that participants with diabetes and uncontrolled hemoglobin A1c and significant proteinuria had the shortest duration of CKD across all stages (Figure 2). Participants with diabetes and hemoglobin A1c <7.5% and without significant proteinuria spent the longest time in CKD stages 3a and 3b, but spent a similar duration of time in stage 5 as participants with diabetes with uncontrolled hemoglobin A1c but no significant proteinuria (Figure 2). Hence, the presence of uncontrolled diabetes appeared to be associated with more rapid progression of disease primarily in the earlier stages of CKD (before CKD stage 4).
When we considered elevated systolic BP and proteinuria in combination in age- and sex- adjusted analyses, we found profound differences in the time spent in stages 3a and 3b among those with or without proteinuria and with systolic BP <140 mm Hg (Figure 2). Differences in the amount of time spent in CKD stages 4 and 5 among those with either elevated systolic BP, proteinuria, or their combination were progressively smaller with advancing CKD, such that the presence of elevated systolic BP and proteinuria were not associated with substantially shorter duration of CKD stage 5 (Figure 2).
The equations used to determine the median time spent in each of the CKD stages on the basis of age, sex, diabetes, systolic BP, and proteinuria categories are provided in the Supplemental Appendix.
Interaction between Risk Factors for CKD Progression and Stage of Disease
In multivariable Cox models, we confirmed the associations between several known risk factors and time to transition to a subsequent stage of CKD, but found that these associations varied according to CKD stage (P<0.05 for interaction; Supplemental Table 1). Specifically, older age was associated with a higher risk (shorter time) of transition from stage 3a to stage 3b (on the basis of hazard ratios), whereas after stage 3b the time to transition to each subsequent CKD stage was shorter for older compared with younger age (Supplemental Table 1). We also found presence of diabetes and proteinuria to be associated with higher risk of transition (shorter time, on the basis of hazard ratios) to subsequent stages of CKD in the earlier stages of CKD and less strongly associated with transition to subsequent stages by CKD stage 5. The time to transition (on the basis of hazard ratios) to subsequent stages of CKD was not different across the CKD stages by sex, black race, presence or absence of tobacco use, or elevated systolic BP.
Considerable effort has been devoted to understanding risk factors for CKD progression. However, our focus on quantifying the differences in time spent in CKD stages according to these risk factors is novel. We found that participants with progressive CKD spent a median of 7.9 years in stage 3a, 5 years in stage 3b, 4.2 years in stage 4, and <1 year in stage 5. Thus, although each interval spanned an eGFR difference of 15 ml/min per 1.73 m2, the overall median time spent in each CKD stage became shorter with later stages of CKD. Although uncontrolled diabetes, elevated systolic BP, and uncontrolled proteinuria are known risk factors for CKD progression (16,17), our strategy of quantifying the median time spent in each CKD stage by these risk factors still revealed profound and surprising differences, especially early in the course of CKD. For example, among those with controlled systolic BP and proteinuria, the median duration of time between CKD stage 3a to ESKD could be over 24 years versus 7 years among those with uncontrolled proteinuria (Supplemental Figure 3).
Although we acknowledge that the factors we examined for the progression of CKD are well known (18–20), we believe that our approach provides complementary information to current data. We note that a considerable amount of time is spent by the majority of patients in CKD stage 3a, but the presence of specific risk factors such as uncontrolled systolic BP, diabetes, or proteinuria substantially shortens the amount of time spent in this stage. Aggressive control of such risk factors during CKD stage 3a could be associated with substantial absolute benefit (in years), and identification of patients who may be at risk of progression in this early stage of CKD could motivate more concerted efforts to prevent progression of CKD (such as through avoidance of nonsteroidal anti-inflammatory medications or iodinated contrast), even if early and mild. We also note that the median time spent in CKD stage 3b was substantially shorter than that spent in CKD stage 3a, and monitoring for CKD-related complications may be warranted in this stage, given the higher propensity for progression of disease (21,22).
Importantly, our results also suggest that traditional risk factors for disease progression, such as uncontrolled BP or elevated hemoglobin A1c among patients with diabetes, are associated with smaller differences in the amount of time spent in CKD stage 4 or 5 despite the fact that, on a relative risk scale (i.e., hazard ratios in Supplemental Table 1), these risk factors were strongly associated with transition to the subsequent stages of disease. We suggest that the examination of risk factors on the basis of a relative risk scale (i.e., with hazard ratios) may not sufficiently capture differences in the associated rate of CKD progression.
Our focus on time spent in each stage of CKD serves to highlight large variations in the importance of risk factors across different stages of CKD. In particular, the association between proteinuria and CKD progression was not consistent across all stages of CKD, with longer absolute and relative time differences associated with proteinuria in the earlier stages of CKD (stages 3a and 3b). CKD stage 3a was approximately 8.2 years shorter and CKD stage 3b was 5.6 years shorter in those with ≥1 g/g of proteinuria compared with those with <1 g/g of proteinuria. Consistent with our observations regarding proteinuria, ACE inhibitor and ARB use also appeared to be more strongly associated with CKD progression during the early (stage 3a) as opposed to later stages of CKD (stages 3b–5). Poorly controlled diabetes was associated with a 10 year earlier transition from CKD stage 3a to 3b. These time-based metrics of disease progression may help convey prognostic information, motivate patient adherence and behavioral change, and guide clinical decisions, such as preparation for RRT.
A number of studies have described the slower progression of CKD among elderly individuals during the later stages of CKD (23–25). The results of our study are consistent with these prior studies. However, using data from the CRIC study, we were able to address the association of age with progression across the full spectrum of CKD. We found that the association differed in the earlier stages, where CKD progression was not statistically significantly different among older and younger individuals before stage 3b. We believe our study helps place the prior observations of slower progression in elderly patients into context, as we observed only a 2.4 month (or 0.2 year) difference among older versus younger individuals who transition from CKD stage 5 to ESKD, suggesting that the slower progression of CKD in the elderly is not as dramatic as one may have gleaned from prior literature. Our findings may be informative for the planning of dialysis access placement among elderly individuals in CKD stage 5, as it appears older individuals only progress more slowly on the order of months, as opposed to years, when compared with younger patients (although our analyses do not account for competing risks of death).
By contrast, some risk factors had a consistent association with the time spent in each of the CKD stages. For example, uncontrolled systolic BP was associated with disease progression across all stages of CKD (i.e., no significant interaction between BP control and stage). However, the absolute difference in the amount of time associated with controlled versus uncontrolled systolic BP declined as disease advanced and was only approximately 2 months by CKD stage 5. These observational data support the importance of proteinuria suppression, ACE inhibitor or ARB use, and BP control in the earlier stages of CKD but suggest that these factors may be less important—by the metric of months or years of slowing of disease progression—in later stages. However, we do acknowledge that these findings are on the basis of observational data, and do not imply causation.
The strengths of our study include the use of a well characterized, nationally representative cohort of patients with CKD with longitudinal follow-up and careful ascertainment of comorbidities and outcomes of interest. In addition, we believe our time-centric approach to CKD progression to be novel, and our data informative for providers and patients, especially for the purposes of prognostication and counseling. Our data may be useful in motivating patients with uncontrolled risk factors, including BP, proteinuria, hemoglobin A1c, and smoking, to improve adherence to therapy and change their behaviors. However, we note the presence of several limitations to our study, including its observational nature, which precludes any conclusions as to whether the mitigation of examined risk factors would result in substantial reductions in the time differences that we observed. We note we cannot rule out the possibility of confounding by indication in the examination of factors such as medication use (e.g., ACE and ARB use). In addition, participants in the CRIC study may not be representative of the general population, and the amount of time spent in CKD that we describe may be characteristic of participants receiving optimal therapy under the care of nephrologists. We also note that we have a limited number of repeated iothalamate-based GFR measurements, which precludes the use of measured GFR trajectory for the determination of the amount of time spent in each stage of CKD. We acknowledge that time spent in CKD stage 5 may be dependent on multiple factors, including provider practice patterns, patient preference, symptoms, and laboratory markers. Further studies using larger cohorts are required to confirm our findings.
In conclusion, we found marked variations in the amount of time spent in various stages of CKD on the basis of risk factors of interest. Elevated systolic BP, proteinuria, and diabetes were associated with large differences in the amount of time spent in CKD stages 2 and 3 but much smaller differences in CKD stages 4 and 5. Addressing progression of CKD from the perspective of time provides a useful context for prognostication and clinical decision-making, and enhances the information available beyond traditional approaches to studies of risk factors for CKD progression.
The Chronic Renal Insufficiency Cohort (CRIC) study was conducted by the CRIC investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The data from the CRIC study reported here were supplied by the NIDDK Central Repositories.
E.K. was funded by National Institutes of Health grant K23 HL131023. This work was also supported by the National Institutes of Health grant K24 DK85153 to K.L.J.
This manuscript was not prepared in collaboration with investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories, or the NIDDK.
1. Stevens PE, Levin A: Evaluation and management of chronic kidney disease
: Synopsis of the kidney disease: Improving global outcomes 2012 clinical practice guideline. Ann Intern Med 158: 825–830, 201323732715
2. Collister D, Ferguson T, Komenda P, Tangri N: The patterns, risk factors
, and prediction of progression in chronic kidney disease
: A narrative review. Semin Nephrol 36: 273–282, 201627475658
3. Driver TH, Shlipak MG, Katz R, Goldenstein L, Sarnak MJ, Hoofnagle AN, et al.: Low serum bicarbonate and kidney function decline: The Multi-Ethnic Study of Atherosclerosis (MESA). Am J Kidney Dis 64: 534–541, 201424953891
4. Matsushita K, Selvin E, Bash LD, Franceschini N, Astor BC, Coresh J: Change in estimated GFR associates with coronary heart disease and mortality. J Am Soc Nephrol 20: 2617–2624, 200919892932
5. Rifkin DE, Shlipak MG, Katz R, Fried LF, Siscovick D, Chonchol M, et al.: Rapid kidney function decline and mortality risk in older adults. Arch Intern Med 168: 2212–2218, 200819001197
6. Young BA, Katz R, Boulware LE, Kestenbaum B, de Boer IH, Wang W, et al.: Risk factors
for rapid kidney function decline among African Americans: The Jackson Heart Study (JHS). Am J Kidney Dis 68: 229–239, 201627066930
7. de Boer IH, Rue TC, Cleary PA, Lachin JM, Molitch ME, Steffes MW, et al.: Diabetes
Control and Complications Trial/Epidemiology of Diabetes
Interventions and Complications Study Research Group: Long-term renal outcomes of patients with type 1 diabetes
mellitus and microalbuminuria: An analysis of the Diabetes
Control and Complications Trial/Epidemiology of Diabetes
Interventions and Complications cohort. Arch Intern Med 171: 412–420, 201121403038
8. Fischer MJ, Hsu JY, Lora CM, Ricardo AC, Anderson AH, Bazzano L, et al.: Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: CKD progression and mortality among Hispanics and non-Hispanics. J Am Soc Nephrol 27: 3488–3497, 201627151925
9. Lambers Heerspink H, Tighiouart H, Sang Y, Ballew S, Mondal H, Matsushita K, et al.: GFR decline and subsequent risk of established kidney outcomes: A meta-analysis of 37 randomized controlled trials. Am J Kidney Dis 64: 860–866, 201425441439
10. Tangri N, Grams ME, Levey AS, Coresh J, Appel LJ, Astor BC, et al.: CKD Prognosis Consortium: Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA 315: 164–174, 201626757465
11. Li L, Astor BC, Lewis J, Hu B, Appel LJ, Lipkowitz MS, et al.: Longitudinal progression trajectory of GFR among patients with CKD. Am J Kidney Dis 59: 504–512, 201222284441
12. Feldman HI, Appel LJ, Chertow GM, Cifelli D, Cizman B, Daugirdas J, et al.: Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: The Chronic Renal Insufficiency Cohort (CRIC) study: Design and methods. J Am Soc Nephrol 14: S148–S153, 200312819321
13. Lash JP, Go AS, Appel LJ, He J, Ojo A, Rahman M, et al.: Chronic Renal Insufficiency Cohort (CRIC) Study Group: Chronic Renal Insufficiency Cohort (CRIC) study: Baseline characteristics and associations with kidney function. Clin J Am Soc Nephrol 4: 1302–1311, 200919541818
14. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al.: CKD-EPI Investigators: Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367: 20–29, 201222762315
15. Genovese G, Friedman DJ, Ross MD, Lecordier L, Uzureau P, Freedman BI, et al.: Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329: 841–845, 201020647424
16. de Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al.: Proteinuria
, a target for renoprotection in patients with type 2 diabetic nephropathy: Lessons from RENAAL. Kidney Int 65: 2309–2320, 200415149345
17. Peterson JC, Adler S, Burkart JM, Greene T, Hebert LA, Hunsicker LG, et al.: Blood pressure control, proteinuria
, and the progression of renal disease. The modification of diet in renal disease study. Ann Intern Med 123: 754–762, 19957574193
18. Muntner P, Anderson A, Charleston J, Chen Z, Ford V, Makos G, et al.: Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: Hypertension
awareness, treatment, and control in adults with CKD: Results from the Chronic Renal Insufficiency Cohort (CRIC) study. Am J Kidney Dis 55: 441–451, 201019962808
19. Turin TC, James M, Ravani P, Tonelli M, Manns BJ, Quinn R, et al.: Proteinuria
and rate of change in kidney function in a community-based population. J Am Soc Nephrol 24: 1661–1667, 201323833255
20. Wheeler DC, Becker GJ: Summary of KDIGO guideline. What do we really know about management of blood pressure in patients with chronic kidney disease
? Kidney Int 83: 377–383, 201323325075
21. Kirsztajn GM, Suassuna JH, Bastos MG: Dividing stage 3 of chronic kidney disease
(CKD): 3A and 3B. Kidney Int 76: 462–463, author reply 463–464, 2009
22. Winearls CG, Glassock RJ: Dissecting and refining the staging of chronic kidney disease
. Kidney Int 75: 1009–1014, 200919242501
23. Eriksen BO, Ingebretsen OC: The progression of chronic kidney disease
: A 10-year population-based study of the effects of gender and age. Kidney Int 69: 375–382, 200616408129
24. Evans M, Fryzek JP, Elinder CG, Cohen SS, McLaughlin JK, Nyren O, et al.: The natural history of chronic renal failure: Results from an unselected, population-based, inception cohort in Sweden. Am J Kidney Dis 46: 863–870, 200516253726
25. O’Hare AM, Choi AI, Bertenthal D, Bacchetti P, Garg AX, Kaufman JS, et al.: Age affects outcomes in chronic kidney disease
. J Am Soc Nephrol 18: 2758–2765, 200717855638