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Department of Medicine (Division of Nephrology), University of Alberta,
Edmonton, Alberta, Canada.
Department of Public Health Sciences, University of Alberta, Edmonton,
Alberta, Canada.
Correspondence to Dr. Philip F. Halloran, Division of Nephrology and Immunology, #303, 8249-114 Street, Edmonton, Alberta, Canada T6G 2R8. Phone: 780-407-8880; Fax: 780-431-0461; E-mail: phil.halloran{at}ualberta.ca
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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55 yr,
with borderline effects of recipient age and female gender. These risks were
studied in each of three intervals posttransplantation: 
6 mo, 6 mo to 5
yr, and >5 yr. Of the 135 graft failures, 53 occurred 6 mo, 61 between
6 mo and 5 yr, and 21 beyond 5 yr. By multivariate analysis, the risks for
graft failure in interval 6 mo were AR (hazard ratio (HR) = 4.86,
P < 0.001); delayed graft function (HR = 1.47, P = 0.06);
and high panel-reactive antibodies (HR = 2.04, P = 0.03). Between 6
mo and 5 yr, the risks for graft loss were AR (HR = 2.87, P <
0.001) and serum creatinine at 6 mo 150 µmol/L (HR = 3.69, P
< 0.001). Beyond 5 yr the risk factors were donor age 55 yr (HR = 5.87,
P = 0.002), with a borderline effect of kidneys from female donors
(HR = 2.28, P = 0.07). HLA-A, -B, and -DR matching and
presensitization had most of their effect through early AR and impaired
function. The results indicate that risks for graft loss are time-dependent:
early losses correlate with injury and rejection, but late events correlate
with donor age and possibly workload. |
Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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We know less about the factors that cause very late loss of transplanted kidneys, e.g., after 5 yr or more. Some early risks may be so high that they preclude survival into the later intervals. The influences of these early events will decline as the kidneys carrying this risk are lost. Thus, the role of early events in late graft loss may be different from their role in early or intermediate graft failure, and assumptions about the causes of graft failure in the early interval may not continue to be valid for the very late era.
The present study aimed to define whether known risks for graft survival
operated differentially over time. We retrospectively analyzed all cadaver
transplants in our center by dividing the course into three posttransplant
intervals: 6 mo, 6 mo to 5 yr, and >5 yr. We identified the risk
factors for the entire course, and then analyzed the relative impact of these
risk factors on the probability of graft loss in each interval. The results
indicate that the risk factors operate differentially over time. Graft loss
before 5 yr is predominantly associated with early AR and acute renal injury
(DGF), whereas graft loss after 5 yr does not reflect these factors, but is
affected by donor age. The results suggest that processes causing CAN and late
graft loss are dependent on the time posttransplant, and that management
should be potentially tailored to these changing risks.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The study was performed by dividing the time posttransplant into three
intervals: 1: 6 mo; 2: 6 mo to 5 yr; and 3: >5 yr. We included 522
consecutive cadaver donor (CD) transplants, of which 522, 463, and 187
transplants were available for analysis in intervals 1, 2, and 3,
respectively. There were 53 graft failures in interval 1, 61 in interval 2,
and 21 in interval 3. Comparisons of all the risk factors were performed. The
analyses included first transplants and retransplants, but not living donor
transplants. Graft survival was censored for patients who died or still had
functioning grafts in each interval or who were lost to follow-up. Note that
although the risks were all either pretransplant or early post transplant, the
risks were analyzed for their impact on graft loss in the early, intermediate,
and late intervals. For the analysis of graft survival in the initial 6 mo,
only those rejection episodes in the first 6 mo were included; for overall
survival analysis and for survival in intervals beyond 6 mo, all rejections
occurring before or after 6 mo were included.
Statistical Analyses
Baseline characteristics among groups were compared using 
2
test and ANOVA for categorical and continuous variables. Survival analysis was
performed using the Kaplan-Meier and Cox regression. Variables were screened
using Kaplan-Meier survival curves and Cox regression, with the variable of
interest as a single main effect. Multivariate models were built in a stepwise
hierarchical manner, testing the significance of added terms using the
likelihood ratio method.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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6 mo). Of
these, 59 were lost in the first interval, 53 due to graft loss and six due to
death with function. Thus, 463 grafts (88.7%) were functioning at the
beginning of the second interval. During the second interval, 61 failed due to
graft loss and 25 due to death with function, while 179 had not reached the
end of the interval but were still functioning at the time of analysis, and 11
were lost to follow-up. Thus, 187 (35.8%) functioning grafts were available
for analysis at the beginning of the third interval (>5 yr). Death with a
functioning graft (n = 51) was censored as a cause of graft loss. Of
the 135 transplants that failed with a surviving patient at the time of graft
failure, 53 (39.3%) failed in interval 1, 61 (45.2%) in interval 2, and 21
(15.6%) in interval 3. Most graft loss after the initial 6 mo was due to CAN.
We analyzed all 522 grafts for graft loss, with continued function at the time
of the analysis censored at the time of the analysis, and loss to follow-up or
death with function censored at the time of the event.
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Table 2 shows some donor and recipient characteristics for the total grafts that entered each interval. The characteristics of the patients available for analysis in each interval were similar, and no differences were statistically significant. The percentage of patients with DGF, with acute rejection as defined, and with elevated serum creatinine at 6 mo is the same in the population available for analysis in all intervals.
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We performed univariate analysis of the factors affecting graft survival in
the three intervals compared to the overall group
(Table 3). In the first 6 mo,
the factors significantly associated with graft failure were HLA-DR mismatch
>1 (hazard ratio [HR] = 1.91, P = 0.019), highest PRA 50% (HR
= 2.64, P = 0.002), DGF (HR = 2.30, P = 0.002), and AR (HR =
4.75, P < 0.001). (Only the AR events before 6 mo are included in
the analysis of survival in the first 6 mo, but all AR was included in the
analysis of the second and third intervals.) The effect of donor age was
borderline at HR = 1.88, P = 0.104. In the intermediate interval (6
to 60 mo), the factors associated with graft failure are DGF (HR = 1.93,
P = 0.011) and AR (HR = 3.81, P < 0.001).
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Note that all DGF and most AR occurred in the first interval but their
influences remained strong in the second interval. Serum creatinine at 6 mo
150 µmol/L strongly correlates with graft loss beyond 6 mo (HR = 4.58,
P < 0.001). Recipient age 55 yr was associated with less graft
loss. In the first interval this was not significant (HR = 0.74, P =
0.41), but it was significant in the second interval (HR = 0.40, P =
0.03) and borderline in the overall group (HR = 0.62, P = 0.055).
The strongest factor after 5 yr is donor age 55 yr (HR = 5.87,
P = 0.002). Kidneys from female donors were associated with increased
risk (HR = 2.31, P = 0.057). Other factors including AR, DGF, PRA
50% at peak, and even a high serum creatinine at 6 mo were not
significantly associated with graft failure beyond 5 yr.
In the overall group, the significant factors were donor age 55 yr, AR,
high PRA, DGF, and high serum creatinine at 6 mo. The protective effect of
older recipient age and the adverse effect of kidneys from female donors just
missed significance.
Figure 1 illustrates three
factors that significantly affected graft survival in the overall population:
AR, DGF, and donor age 55. Graft survival (censoring patient death) in
each interval is presented in Figures
2,3,4,
comparing those grafts with and without the variables DGF, AR, and donor age
55 yr. Figure 2 shows that
graft loss in the first 6 mo reflected the occurrence of AR and DGF but not
older donor age. Similarly, the 6 mo to 5 yr graft survival
(Figure 3) shows effects of DGF
and AR but not older donor age. Figure
3D shows the effect of 6-mo serum creatinine 150 µmol/L on
graft survival from 6 mo to 5 yr. However, graft survival beyond 5 yr shows a
different pattern: DGF, AR, and 6-mo serum creatinine (not shown) were not
significant, but donor age 55 yr was highly significant.
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We used multivariate analysis to examine the independent factors affecting
graft survival in each interval (Table
4). In the first 6 mo, the factors significantly associated with
graft failure were AR (HR = 4.86, P < 0.001), highest PRA 50%
(HR = 2.04, P = 0.026), and DGF (HR = 1.74, P = 0.061). In
the interval 6 to 60 mo, the significant factors for graft failure were AR (HR
= 2.87, P < 0.001) and serum creatinine at 6 mo 150 µmol/L
(HR = 3.69, P < 0.001). (Acute rejections occurring in either the
first or second intervals were included.) Beyond 5 yr, the major prognostic
factor was older donor age (HR = 5.18, P = 0.004). The influence of
having a kidney from a female donor was borderline (P = 0.07) in the
interval >5 yr. The analysis represented in
Table 4 was not altered
significantly when the effect of female donors was included in the model. The
addition or deletion of the 6-mo serum creatinine data did not materially
change the analysis, except that excluding the 6-mo serum creatinine made the
DGF a significant factor in interval 2.
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Table 5 analyzes
interactions among the variables and is included to explain the mechanisms by
which factors such as rejection, mismatch, DGF, and donor age may operate.
Older donor age was associated with a twofold increase in the rate of DGF and
a fourfold increase in serum creatinine 150 at 6 mo but not with increased
AR. The relationship of donor age to serum creatinine at 6 mo is shown in
Figure 5, which illustrates the
rise in serum creatinine at ages as young as 35 to 45. Recipients older than
55 showed a tendency to less rejection (NS), and fewer had a high serum
creatinine at 6 mo (P = 0.014). AB and DR mismatches and high PRA
mainly affected the incidence of rejection. High PRA also increased DGF and
serum creatinine at 6 mo.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Although analysis of time dependency poses the challenge that the patients were transplanted in different eras, it is reassuring that the population entering the late interval was similar to that entering interval 1. One might have expected that the frequency of the strong risks such as rejection would be lower in the patients who entered interval 3 due to selective loss in previous intervals of those carrying these risk factors, and there was a weak trend in this direction. This tendency may be balanced by the declining frequency of acute rejection in the recent era, and by the tendency of rejection to be in younger patients with lower risks of death. Separation of graft loss due to graft failure from that due to death with a functioning graft should now be the preferred analysis of renal transplant survival. In the past, the censoring of patient death was discouraged because many deaths were due to complications of immunosuppression, but this is no longer the case. Death with function is usually due to comorbidities such as heart disease with no obvious relationship to the status of the transplant. The analysis of graft loss independent of death with function permits a more precise description of the influences on graft survival, which therefore should be a more accurate guide to the interventions required to extend graft function.
The emergence of donor age as a major factor in cadaver graft survival probably reflects the decline of acute rejection and the increasing reliance on older donors to deal with the donor shortage (18,19). The kidney develops characteristic changes termed senescence, which describes the global pathologic and physiologic changes. These include global sclerosis of glomeruli, hyalinization and fibrous intimal thickening in small arteries, tubular atrophy, and interstitial inflammation and fibrosis. The GFR and renal plasma flow decline, and the filtration fraction rises (20,21), but it is unknown whether these events are due to the histologic abnormalities or reflect a shift in vasomotion toward vasoconstriction and away from vasodilation. Advanced age is a risk for the development of end-stage renal disease of many types (6), presumably reflecting interactions between the disease mechanisms and aging processes. Nevertheless, some elderly normotensive individuals retain GFR in the normal range (22), and some human populations avoid the arterial changes (23), suggesting that neither the physiologic nor the histologic changes are inevitable.
The evidence suggests that the donor age effect on survival of cadaver kidney transplants reflects an interaction between age and the stresses of transplantation (24). In the United Network of Organ Sharing (UNOS) registry from 1987-1995, older donor age was the strongest determinant of the transplant course, predicting more day 1 anuria, dialysis, and lower long-term graft survival. In HLA-matched kidneys, the 5-yr survival was 81% at donor age 21 to 30, but fell to 39% at donor age >60. Indeed, the worst results were with older donor kidneys, regardless of matching. The main cause of failure in kidneys of older donors was CAN, indicating that older donor age increases CAN (19). DGF has more impact in kidneys from older donors (25,26,27,28). As we have recently reviewed, CAN may reflect accelerated senescence changes due to the abnormal nonimmune and immune stresses of transplantation (29). Thus, stresses inherent in the cadaver renal transplant may interact with the endogenous senescence program to increase the probability of developing CAN. Like senescence changes in native kidneys, CAN may be predominantly a disease of small arteries (30), characterized by fibrous intimal thickening. Thus, the effect of donor age may be a reduced ability to withstand and recover from the stresses of brain death, ischemia, rejection, hyperfiltration, proteinuria, nephrotoxicity, hypertension, and excessive workload. Whether older donor age also increases immune recognition is unclear. For example, the lack of HLA effect in kidneys from old donors argues against an immune effect, but rejection is increased in kidneys from older donors in a recent analysis of the UNOS data (M. Cecka, personal communication).
The tendency of graft survival (recipient death censored) to improve with older recipient age may reflect the attenuation of the recipient immune-inflammatory system with age (31), as reflected by the lower frequency of AR in older recipients (32). In the present study, the incidence of AR was 18.6% in older recipients versus 24% in younger recipients, which, although not significant, could still contribute to the lower hazard ratio for graft loss in older recipients (0.62). The possibility of qualitative differences between rejection episodes cannot be excluded and could contribute to the tendency toward better graft survival in older recipients. Lower workload for the renal transplant in older recipients and better compliance may also contribute.
These results suggest a dynamic model for renal transplant survival in which the dominant factors are the quality of the transplanted tissue, rejection, and workload and stresses such as hypertension and nephrotoxins. The well known relationship between measures of renal function and kidney survival (33) is central to this model. The early course of the renal transplant is dominated by donor factors (34), acute injury (DGF), and immune injury. DGF and acute rejection have little long-term impact if the repair mechanisms restore good function. Early rejection episodes have a continuing impact lasting up to 5 yr, by affecting the serum creatinine at 6 mo but also independent of the serum creatinine at 6 mo. In contrast, AR is not an independent predictor of graft loss beyond 5 yr. Older donor age increases DGF and decreases the serum creatinine at 6 mo, and may increase rejection. But in contrast to AR and DGF, donor age has continuing major long-term effects, which are separable from the effects on early function (i.e., serum creatinine at 6 mo) in multivariate analysis. The fact that donor age is significant even in multivariate analysis including 6-mo serum creatinine suggests that the effect of donor age may not simply be due to reduced nephron number but may also reflect the age of the transplanted tissue per se.
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Acknowledgments |
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Dr. Halloran's research is supported by the Medical Research Council of Canada, the Kidney Foundation of Canada, Novartis Pharmaceuticals Canada, Hoffmann-La Roche Canada, the Muttart Foundation Chair in Clinical Molecular Immunology, the Royal Canadian Legion, and the Roche Organ Transplant Research Foundation. We are grateful to the Clinical Trials Program of the University of Alberta Hospital for their ongoing support.
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Footnotes |
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References |
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