NDT Advance Access published online on September 19, 2007
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfm590
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CKD stage-to-stage progression in native and transplant kidney disease*
1Department of Medicine, Nephrology Section, University of Minnesota and 2University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
Correspondence and offprint requests to: Arjang Djamali, MD, Assistant Professor of Medicine, Department of Medicine, Nephrology Section, University of Wisconsin School of Medicine and Public Health, 3034 Fish Hatchery Road, Ste B, Madison, WI, 53713, USA. Email: axd{at}medicine.wisc.edu
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Abstract |
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Background. Kidney half-life and inter-stage progression rates in native chronic kidney disease (CKD) and CKD-transplant (CKD-T) remain unknown.
Methods. We examined stage-to-stage progression/regression rates in patients with CKD (n = 601) and CKD-T (n = 431) between 1991 and 2001. Kidney function was estimated by Cockcroft–Gault and MDRD eGFR formulae. Kaplan–Meier analyses determined progression and regression half-lives, defined as the time required for 50% of kidneys to advance towards a higher or lower stage of CKD, respectively.
Results. Most (67%) of the patients were in stage 3. Patients with native CKD were more likely to progress compared to CKD-T (inter-stage progression rates 12 vs 4 cases per 100 patient-years, P < 0.0001). Accordingly, estimated glomerular filtration rate (eGFR)-based progression half-lives were significantly shorter in CKD compared to CKD-T [6 vs 9.6 years, P < 0.0001, hazard ratio (HR) 3.1, 95% confidence interval (CI) = 2.5–3.7]. Creatinine clearance (CCR)-based stage half-lives were 7.2 months shorter in each group (5.4 and 9 years in CKD and CKD-T, respectively). Despite slower progression rates in patients with transplant kidney disease, adjusted patient survival rates were significantly decreased in CKD-T compared to CKD. Only Scr and CCR-based formulae were significantly associated with patient and allograft outcomes in the CKD-T group. Moreover, death rates were not different in stage 3 compared to stage 2 CKD-T, suggesting that eGFR and the current staging classification have a limited value to predict patient death in this cohort.
Conclusion. Kidney half-lives per stage of CKD may be a novel tool to examine disease progression.
Keywords: CKD; CKD-T; half-life; kidney transplantation; outcomes; progression
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Introduction |
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Chronic kidney disease (CKD) affects 10–12% of the adult population in the US and is an ever-increasing public health concern [1,2]. In addition to its costs, CKD leads to significant patient morbidity and mortality. Unfortunately, a large number of these patients die prior to the initiation of renal replacement therapy [3–5]. To improve long-term outcomes in this group of patients, the National Kidney Foundation (NKF) published clinical practice guidelines and recommendations based on a five-stage classification of CKD [6].
Kidney transplant recipients (KTR) represent a specific cohort of patients with impaired kidney function and high morbidity and mortality rates. These individuals may differ from native CKD due to their lead-time with CKD and the restoration of some degree of kidney function. Yet, recent evidence demonstrates that despite optimistic earlier estimations, long-term outcomes have not significantly improved in these patients [7–10]. Similar to patients with native CKD, comorbid conditions increase with progression of kidney disease in KTR [11]. Furthermore, despite a slower rate of decline in kidney allograft function, KTR have mortality rates comparable to patients with native CKD [12]. Thus, the Kidney Disease: Improving Global Outcomes (KDIGO) group recently included KTR in their amended CKD classification schema, designating them with the suffix ‘T’ added to each stage of CKD [13].
Progression rates in CKD-transplant (CKD) and CKD-T are unpredictable when considered at an individual level because kidney function may stabilize, improve or deteriorate with time [14,15]. However, large-scale studies examining average rates of function loss in patients with native and transplant CKD have found rates of progression of 4–8 ml/min [12,14,16] and 1.5–2 ml/min [12,15,17] per year, respectively. Because the NKF and KDIGO clinical practice guidelines and recommendations are based on ‘stages’ of CKD/CKD-T, and many patients wonder about the duration of their kidney function, it is helpful to consider kidney survival per stage of CKD. To address this, we examined stage-to-stage progression rates in CKD and CKD-T patients prospectively followed at our institution from January 1991 to March 2001.
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Subjects and methods |
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Patients
We reviewed data on patients with native and transplant CKD at the University of Wisconsin (UW) Hospital and Clinics from January of 1991 to March of 2001. Traditionally, the UW nephrologists have been involved in the care of KTRs after the first post-transplant year. They created a data set (separate from the transplant surgery database) gathering information on estimated Cockcroft–Gault formula creatinine clearance [CCR] = [(140-age) x (body weight)]/(serum creatinine x 72) [18] and urinalysis values from the early 80s. Although this data set does not represent the entire cohort of individuals transplanted at the UW, it included a significant number of patients and has aided our understanding of disease progression and outcomes in KTRs after the first post-transplant year [12,19].
A similar data set was created for patients with native CKD once they were followed for at least 1 year from the first elevated creatinine value in the out-patient clinic. The current study evaluated all KTRs and CKD patients from the UW nephrology data sets covering the period of 1991–2001. All analyses were performed with the approval of the University of Wisconsin Hospital and Clinics Institutional Review Board.
Determination of kidney function, staging, disease progression, patient and allograft survival
CCR was estimated using the Cockcroft–Gault formula [18]. The 4-variable equation from the Modification of Diet in Renal Disease [MDRD estimated glomerular filtration rate (eGFR) = 175 x standardized Scr–1.154 x age–0.203 x 1.212 (if black) x 0.742 (if female)] [20] was used to estimate GFR. T1 was the date of the first visit with the nephrologist (at one year post-transplantation in the case of KTRs), while CCR1 and eGFR1 were the corresponding estimated creatinine clearance and MDRD glomerular filtration rates. T2 was the date of the last visit with the nephrologist, kidney failure or death with a functioning kidney.
Disease progression was determined based on three different methods: (i) slopes of CCR or eGFR between T1 and T2 (
ml/min/year) [12], (ii) stage half-lives defined by the median time required for 50% of kidneys to progress from one stage to another and (iii) patient death and death-censored kidney loss. We separately analysed the predictive value of CCR and eGFR on stage progression by staging all patients according to the Cockcroft–Gault and MDRD estimation formulae.
Because quantitative proteinuria measurements were not available for most patients, we evaluated proteinuria according to a semi-quantitative scale based on urinalysis results at the date of the first visit: 0 for no proteinuria, 0.5 for trace, 1 for 1+, 2 for 2+ and 3 for 3+ or greater. Mean arterial pressure was defined as MAP = [(2 x diastolic) + systolic]/3.
Etiology of kidney disease
CKD and CKD-T groups were divided into subgroups based on aetiology of native kidney disease: diabetes mellitus (DM), hypertension (HTN), glomerular disease (GD), polycystic kidney disease (PKD) and ‘other’ nephropathies including interstitial disease, other solid organ transplants (lung, heart or liver), congenital disease, cancer, human immunodeficiency virus (HIV) and unknown disease.
Statistical analyses
Student's t-test and Wilcoxon rank sum tests were performed to compare parametric and non-parametric continuous data, respectively. Similarly, chi-squared and Fisher's exact test were used to analyse categorical data when appropriate. Kaplan–Meier analyses and log-rank tests were used to compare stage-to-stage progression half-lives as well as the probability of patient death and death-censored kidney loss between the two groups. Univariate and multivariable Cox regression analyses were performed to determine independent covariates associated with stage progression, patient and kidney survival. These covariates included the following: age, gender, white ethnicity, CCR, eGFR, semi-quantitative proteinuria, mean arterial pressure (MAP) and angiotensin converting enzyme (ACE) or angiotensin receptor blockers (ARB) use. Variables were retained if P < 0.05 and dismissed if P > 0.1. The associated risk was presented as the hazard ratio (HR) and 95% confidence interval (95% CI). Analyses were performed using the MedCalc Statistical Software (Mariakerke, Belgium, www.medcalc.be).
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Results |
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Clinical characteristics
Table 1 displays the clinical characteristics in 1032 patients, including 601 patients with native CKD and 431 KTRs. Most patients (690) were in stage 3 (67%). HTN and GDs were the most prevalent causes of kidney disease in patients with native and transplant CKD, respectively. Average age, serum creatinine, MAP, semi-quantitative proteinuria and use of ACE-I/ARB were significantly decreased in KTR, whereas these patients were followed for a significantly longer period (6.7 vs 4.2 years, P < 0.0001). Baseline kidney function estimated by CCR or eGFR was significantly higher in CKD-T compared to CKD. The rate of kidney function loss evaluated by the slope of CCR (–2.2 ± 0.3 vs –6.3 ± 0.4 ml/min/year, P < 0.0001) or eGFR (–1.4 ± 0.2 vs –4.5 ± 0.3 ml/min/1.73 m2/year, P < 0.0001) was also significantly slower in KTRs.
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Stage progression in CKD and CKD-T
Figure 1 displays the overall stage-to-stage progression rates according to baseline CCR and eGFR (CCR1 and eGFR1, respectively, panels A and B). Panel (C) shows kidney half-lives according to CCR1 and eGFR1. Briefly, the median time for 50% of kidney allografts to progress from one stage to the next was 9 years, compared to 5.4 years in the CKD group when kidney function was evaluated by CCR. Kidney half-lives were still significantly different between the two groups when we used the MDRD eGFR estimation formula. In fact, half-lives were increased by an average of 7.2 months when eGFR was used compared to CCR. Best fit polynomial curve analyses revealed that the slopes were not parallel (data not shown).
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Because the KDOQI staging system is based on eGFR rather than CCR levels, we performed additional stage survival and regression analyses according to levels of eGFR. Univariate Cox regression analyses showed that serum creatinine, proteinuria and diabetes were significantly associated with stage progression in CKD-T compared to CKD (Table 2). The first two were retained by the multivariable analyses (Table 3). Yet, stage progression half-lives remained significantly longer in CKD-T after adjustment for both serum creatinine and proteinuria (Figure 2A).
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The analyses of annualized, stage progression and regression rates (defined as an improvement in kidney function from a higher to a lower stage) confirmed that patients with native CKD were more likely to progress than KTR (inter-stage progression rates: 12 vs 4 per 100 patient-years, P = 0.001) (Table 1). Conversely, regression was more likely to occur in CKD-T group (2 vs 0.9 per 100 patient-years, P = 0.001).
Stage progression based on the cause of kidney disease: CKD vs CKD-T
Progression half-lives based on the cause of CKD/ESKD are shown in Figure 3. Patients were divided into four groups: diabetes (DM, Panel A), HTN, (panel B), GD (panel C) and PKD (panel D). In all groups, stage progression half-lives were significantly shorter in patients with native CKD.
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Stage progression based on the cause of kidney disease: within group analyses
To examine whether the cause of CKD or kidney failure (in the CKD-T group) had an impact on the rate of disease progression regardless of the transplant vs native kidney status, we compared stage survival rates in patients with DM, HTN, GD and PKD in the CKD and CKD-T cohorts separately. Kidney half-lives were 4.2, 6.8, 6.6 and 6 years in patients with native CKD and DM, HTN, GD and PKD, respectively (P = 0.001 for DM). In contrast, there was no significant difference in allograft half-lives per stage of CKD in the transplant group: 9.2, 9.2, 9.4 and >11 years in KTRs with a history of kidney failure due to DM, HTN, GD and PKD, respectively.
Kidney failure and death rates
The prevalence of unadjusted death-censored kidney loss was significantly greater in CKD (32% vs 14.4%, Table 1, P < 0.0001). Univariate Cox regression analyses showed that age, DM, HTN, CCR, eGFR and proteinuria were significantly associated with death-censored kidney loss in the CKD-T group compared to patients with native CKD (Table 2). Multivariable Cox regression analyses retained a significant association between age, CCR, proteinuria and death-censored kidney loss (Table 3). We, therefore, compared survival rates adjusted for these three variables using Kaplan–Meier survival analyses and demonstrated that kidney survival remained significantly greater in the CKD-T group (Figure 2B).
Conversely, unadjusted patient death rates were not significantly different between the two groups (7.3% vs 9.3%, P = 0.3, Table 1). Univariate analyses showed a significant association between age, history of DM, CCR, eGFR and their respective slopes and patient death rates in CKD-T compared to CKD (Table 2). Multivariable analyses retained age, DM and CCR slope as significant factors (Table 3). We, therefore, compared survival rates adjusted for these three variables using Kaplan–Meier survival analyses and demonstrated that patient survival was significantly decreased in the CKD-T group (Figure 2C).
To evaluate the role of different CKD stages on patient outcomes, we compared death rates in CKD and CKD-T based on stages with the largest number of patients (stages 3–4 CKD and 2–3 CKD-T). Death rates were significantly greater in stage 4 compared to stage 3 CKD (Figure 4A). However, there was no significant difference in survival rates for KTRs in stage 2 compared to stage 3 CKD-T (Figure 4B).
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Discussion |
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This study utilizes novel methods to quantify progression in the CKD or CKD-T setting—CKD stage progression/regression rates and ‘progression half-lives’ in patients with native and transplant kidney disease. At our centre, 50% of patients with native CKD progressed to the next stage of CKD after 6 years, whereas allograft half-life per stage of CKD was 9.6 years in KTR. Slower rates of kidney function loss in the CKD-T group were confirmed by the evaluation of CCR and eGFR slopes as well as death-censored kidney survival probabilities. However, despite slower progression rates in patients with transplant kidney disease, adjusted patient survival rates were significantly decreased in CKD-T compared to CKD.
KTR are a specific group of patients with CKD who carry pre-existing CKD with additional post-transplant comorbidities. Notably, a large number of these patients achieve only modest kidney function, even early post-transplant. Most of the KTR in our study were indeed in CKD stages 2 and 3 at 1-year post transplant. Similar results were reported in an analysis of 40 963 KTR who had an average GFR of 49.6 ± 15.4 ml/min/1.73 m2 6 months after transplantation [15]. Half of the patients demonstrated either improvement or no change in GFR, whereas half progressed with a decline in GFR during follow-up of nearly 6 years. The mean (± SE) change in GFR was –1.66 ± 6.51 ml/min/1.73 m2/year [15], comparable to our findings (–1.4 ± 0.2 ml/min/1.73 m2/year).
Why would the functional half-life of a single transplanted kidney be longer than two native kidneys? The skillful management of immunosuppressive medications may be in part responsible for these differences. Many immunosuppressive drugs are used to treat native kidney diseases, and despite their potential for negative effects on graft survival when examined in isolation [21–23], calcineurin inhibitors (CNI) may protect against disease recurrence and limit inflammation, let alone prevent rejection. The majority (>80%, data not shown) of our study patients were maintained on CNIs, though at reduced doses in the late post-transplant period. Another possibility could be the difference in the ‘time to kidney function decline’ between the two groups. For example, Figure 1 shows a holding period before the decline of kidney function in the CKD-T group. It is unlikely that this delay results from the difference in baseline eGFR levels as the same trend was observed in stages 1, 2 and 4, where no significant differences were observed in eGFR1 values (data not shown). Furthermore, best fit polynomial curve analyses revealed that the slopes of kidney function decline were not parallel, suggesting that other factors than a postponement in the drop of kidney function were involved. One such factor could be the underlying histopathological injury. Despite having similar baseline kidney functions, transplanted kidneys may present different glomerular, vascular and tubulointerstitial injuries, with distinct influences on the rate of progression compared to native kidneys. In addition, the physiological hypertrophy in the allograft likely happens by 6 months post-transplantation and could serve as a trigger for hyperfiltration injury and initiate a gradual decline in function. Furthermore, all KTRs from this study were included after the first post-transplant year, suggestive of a natural selection bias in favour of these kidneys. Finally, metabolic interactions between the transplanted kidney and the recipient may be another factor to consider. Post-transplant kidney function is determined by the functional capacity of the allograft and the metabolic needs of the recipient. In a situation where the transplant recipient is stable and the organ is rejection-free, that equation may well tilt in favour of the allograft and its functional capacity. Certainly, this is the case for small individuals who receive organs from larger donors [24].
The fact that despite longer kidney survival, adjusted patient survival was significantly decreased underlines the significance of lead-time illness in KTR. These patients have already reached kidney failure and endured its related comorbidities prior to transplant [12]. Though successful transplantation undeniably restores kidney function and returns KTR to earlier stages of CKD, these patients are not safe free of past, let alone present comorbid conditions [11,12]. Cardiovascular disease, infections and malignancies are the major causes of death in KTR, and their course and progression are often different compared to native CKD [25]. For example, all stable KTR are on maintenance immunosuppressive therapy, necessary for the long-term survival of the allograft. But these same drugs significantly increase the number cardiovascular infectious and malignant complications [26]. Hyperparathyroidism, bone disease and anaemia may also progress differently in CKD-T compared to native CKD.
It may be appropriate to consider kidney transplantation as a separate form of CKD. These patients have only one kidney, are on life-long immunosuppressive therapies and have greater disease vintage compared to their native CKD counterparts. However, the NKF [6] and KDIGO [13] classification and clinical practice guidelines have the merit to simplify data analyses and allow for standardized physician communication and patient care. Research, nephrology, transplant and primary care communities now have the tools to address the complexity of ‘CKD and CKD-T syndromes’ and to take steps towards improving long-term outcomes. Clinical trials in kidney transplantation have for so long focused on novel immunosuppressive molecules and short-term outcomes including acute rejection and 1–3 year patient survival rates. It may now be time to examine issues related to disease progression especially in the context of long-term outcomes.
We also used both Cockcroft–Gault creatinine clearance and MDRD eGFR estimation formulae [18,20] in this analysis. Some studies suggest that the MDRD equation may perform better in predicting kidney allograft function [27], whereas others have shown that the predictive performance of both formulae is impaired in KTR [28,29]. In our study, stage progression half-lives were only 7.2 months longer with eGFR values compared to CCR. Only Scr and CCR-based formulae were significantly associated with patient and allograft outcomes in the CKD-T group. Moreover, death rates were not different in stage 3 compared to stage 2 CKD-T, suggesting that MDRD eGFR and the current staging classification have limited value in predicting patient death at a certain level of kidney function. As function changes, a threshold effect may be evident demonstrated by the increased rate of death in stage 4 CKD-T patients.
This retrospective, single centre study has attendant shortcomings. Patients were primarily Caucasian and only a few individuals were in stages 1 and 5 CKD or CKD-T. One could also suggest that patients with native CKD were sicker than their transplant counterparts and that they were followed by different health care providers, treatment and follow-up strategies. However, it should be noted that three out of the four health care providers for the transplant group were nephrologists who also cared for the native CKD cohort, and all patients were followed at a single institution (University of Wisconsin Hospital and Clinics).
In conclusion, our study shows that kidney half-life is longer in KTR at every level of CKD. However, that does not translate into improvement in mortality. This reinforces the idea the kidney transplantation is a unique form of CKD. The study illustrates the complexity of CKD-T patients and raises many questions for future investigation including the optimal use of CKD classification schema for KTR, the best modes of care delivery, and what is the target outcome that we should address in our care delivery.
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Acknowledgments |
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This work was supported in part by AHA SDG-0235290N (MS), NIH DK067981-03 (AD) and DK616962-04 (BNB).
Conflicts of interest statement. None declared.
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Notes |
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*Parts of this work were presented in an abstract form at the American Society of Nephrology Annual Meeting 2006, San Diego, USA in Journal of American Society of Nephrology October 2006. F-FC-157.
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Accepted in revised form: 2. 8.07
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