Survival of recipients of cadaveric kidney transplants compared with those receiving dialysis treatment in Australia and New Zealand, 1991–2001

Stephen P. McDonald and Graeme R. Russ

ANZDATA Registry, Adelaide, Australia



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Comparison of mortality rates after kidney transplantation with those treated by dialysis is an important factor is assessing treatment options, but is subject to many pitfalls in selection of appropriate control groups, in particular allowing for varying post-operative risk, and recent changes in mortality rates with better immunosuppression and dialysis techniques. We examined the outcomes following cadaveric renal transplantation and compared them with an appropriate control group of dialysis patients, using contemporary national data from Australia and New Zealand and appropriate statistical methods. In particular, we explicitly addressed the changing risks following transplantation, and looked at both younger (low-risk) and older (higher-risk) recipients, and examined the effect of attribution of deaths in the early period following loss of transplant function to the risk of transplantation.

Methods. We performed a cohort study, initially including 11 560 people aged 15–65 years who began treatment for end-stage renal disease in Australia or New Zealand between 1991 and 2000. Of these, 5144 were recorded at least once as on an active cadaveric transplant waiting list. Survival was analysed with Cox regression, including time-dependent covariates to allow for the violation of proportional hazards with changing mortality risks post-operatively. We also performed stratified analyses on low-risk recipients (<50 years, without co-morbidity) and older recipients.

Results. There was a clear difference in survival between those on the active transplant waiting list and those not listed. Of those who were on the cadaveric transplant waiting list, 2362 (46%) were transplanted in the period to 30 September 2001. Cadaveric transplantation was associated with an initial increase in mortality [during the first 3 months post-transplantation, adjusted HR 2.0 (1.5–2.7), P<0.001]. This fell below the dialysis group at 6 months [adjusted HR 0.27 (0.16–0.47), P<0.001] and from 12 months post-transplantation, the reduction in risk of mortality was ~80% [adjusted HR 0.19 (0.15–0.24), P<0.001]. A secondary analysis showed the excess risk attributed to the period immediately following transplantation was actually due to deaths in the 60 days after loss of transplant function rather than those occurring with a functioning graft.

Conclusions. As well as improved quality of life, cadaveric renal transplantation in Australia and New Zealand is associated with a survival advantage compared with those remaining on the waiting list.

Keywords: cadaveric kidney transplants; end-stage renal disease; haemodialysis; peritoneal dialysis



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The number of people entering treatment programmes for end-stage renal disease (ESRD) is growing steadily. An association of ESRD is an increase in the mortality risk, principally from cardiovascular disease. This risk is not equal across types of renal replacement therapy (RRT). The survival benefit of patients treated with transplantation compared with dialysis has been the subject of a number of reports, including national data [13], reports from single regions [46] and from single centres [710]. Some of these have examined older recipients [3,10] and high-risk recipients [2,5] in particular.

There are several methodological challenges to drawing appropriate conclusions from these comparisons. First, the continued evolution of immunosuppression and dialysis techniques is associated with changing mortality rates in each modality, which renders older reports less relevant. Secondly, the work-up process prior to transplantation makes selection of an appropriate control group important to avoid selection bias. Thirdly, the different waiting times for cadaveric transplants must be accounted for, and fourthly the survival analyses must examine and (if necessary) account for the violation of the proportional hazards assumption, which underlies analyses using standard survival methods.

The present study examines outcomes among transplant recipients in Australia and New Zealand and a comparable group of patients receiving dialysis treatment using data from the Australia and New Zealand Dialysis and Transplant (ANZDATA) registry over the period 1991–2001, using contemporary figures and appropriate controls and methodology.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
ANZDATA collects information from all centres caring for ESRD patients in Australia and New Zealand for all people under their care every 6 months. Although outcomes of patients receiving ESRD therapy have been recorded since 1977, details of listing on the transplant waiting list have only been recorded from 1 November, 1981. The presence of co-morbid conditions has been recorded even more recently (from 1 October, 1991). For each patient, the presence (in the opinion of the treating physician) of coronary artery, cerebrovascular or peripheral vascular disease, lung disease or diabetes at commencement of RRT is sought, on a ‘yes/suspected/no’ basis. For this analysis, ‘suspected’ was pooled with ‘yes’. Severity of co-morbidity is not recorded.

The exact date of listing on the transplant waiting list is not provided in the 6 monthly ANZDATA returns. Instead, the beginning of the 6 month survey period when listing on the ‘active’ list was first noted was used as the date of listing. This date (rather than the date of first dialysis) was used as the start point for survival analyses. Each transplanting centre in Australia and New Zealand maintain their own waiting list, and each determines their own acceptance criteria although broad guidelines have been agreed ([11] http://www.racp.edu.au/tsanz/oap7a.htm). Allocation of kidney transplants is performed separately in each Australian state and New Zealand, with an overriding national allocation scheme in Australia in the case of excellent HLA matches ([12] http://www.racp.edu.au/tsanz/oap7c.htm). The exact criteria vary from state to state, but all include consideration of both HLA matching and waiting time on dialysis.

All incident cases of ESRD in the Registry were included in this analysis, if the date of first treatment was between 1 April 1991 and 30 September 2001, and were reported as ‘on active transplant list’ at least once, regardless of whether they subsequently remained on the transplant waiting list. Exclusion criteria were age at entry to ESRD programme <15 or >=65 years. The pre- and post-transplant course of recipients of grafts from living donors, multiple organ grafts or grafts performed overseas was excluded from analysis. The pre-transplant dialysis modality was categorized into peritoneal versus haemodialysis on the basis of the modality in use 90 days after ESRD entry.

The outcome assessed was mortality, and follow-up was available to 30 September 2001. The principal cause of death was coded by ANZDATA from information from the treating unit and for this analysis was divided into four broad groups—‘cardiac and vascular’, ‘infective’ ‘malignancy’ and ‘other’. Patients were censored at the date of last known contact if lost to follow-up. For the primary analysis, deaths were attributed to the period of transplant function if they occurred within 60 days of return to dialysis after loss of transplant function. Subsequent deaths were attributed to the post-transplant dialysis category. Supplementary analyses were also performed with a separate examination of deaths in first 60 days after failure of transplant function. Survival was analysed using Kaplan–Meier curves. The commencement date for survival analyses used was 6 months prior to the survey date, i.e. the beginning of the 6 month survey period when listing on the transplant waiting list was first recorded. For multivariate analyses, Cox regression with transplantation modelled as a time-dependent covariate was used [13]. The possibility of changing relationships due to changes in treatment patterns over the period of the study was addressed by stratifying the cohort into three groups, based on the year in which they were listed as waiting for a transplant (1991–1994, 1994–1997, 1997–2001). Post-transplant survival was also compared between similar groups based on date of transplant operation. The non-proportional nature of the post-transplant mortality risk was addressed by using four categorical time-dependent covariates for the time periods following transplantation, and an additional time-dependent covariate for dialysis after graft failure. Cox analyses were stratified by the three periods of ‘waitlisting’, to account for differences in the baseline hazard rate. In addition, terms were also added to assess interaction between the time-dependent covariates and the year of transplantation, and their effect assessed by a Wald test [13]. Age and co-morbid conditions were inserted into the model as a series of categories. Odds and hazard ratios are presented with 95% confidence intervals, and a P value of 0.05 was used as the indicator of statistical significance. All analyses were performed using Stata version 7.0 [14].

A number of subgroups were identified for specific analysis, including older recipients (>=50 years), younger recipients without co-morbidities (<50 years), and people of Aboriginal or Maori or Pacific Islander origin racial origin.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
A total of 11 560 people in the Registry met the age-related inclusion criteria. Excluded were recipients of grafts from live donors (n=1147), multiple organ grafts (n=177), recipients of grafts performed in countries other than Australia or New Zealand (n=13) and recipients of cadaveric transplants transplant without prior dialysis (n=27). Of the remaining 10 194 people, 5144 (50%) were reported as being on the active transplant waiting list at least once. The survival of the group who were waitlisted was significantly better than those not placed on the waiting list in the same age group (Figure 1Go, P<0.001, log rank test). Those placed on the waiting list were also younger, more likely to be male, but less likely to have coronary artery disease, lung disease, type 2 diabetes or be of indigenous racial origin (Table 1Go).



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Fig. 1.  Kaplan–Meier graph of patients who began ESRD treatment, 1991–2001, by placement on active transplant waiting list, excluding recipients of live related grafts, adjusted for gender (to 50% male) and age (to 40 years).

 

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Table 1.  Characteristics of cohort at ESRD entry, divided by placement on waiting list and ultimate receipt of cadaveric kidney graft

 
Recipients of cadaveric grafts received their transplant 584 [255–1042] days (median [interquartile range]) after estimated listing on the ‘active’ list (and 640 [349–1122] days after starting dialysis). Of those who were recorded as placed on a waiting list and did not receive a kidney from a living donor, 2043 (40%) remained on dialysis at 30 September 2001, 739 (12%) had died without receiving a transplant and 2362 (46%) had received a cadaveric kidney transplant. In the transplanted group there were 161 deaths with graft function, a further 39 deaths within 60 days of graft failure and 218 cases of loss of graft function. A further 20 deaths occurred between 60 and 365 days after graft failure and 45 deaths >1 year after loss of graft function. One person was lost to follow-up (in the post-transplant group). In addition there were 103 grafts performed from cadaver donors in people who had not been previously recorded in ANZDATA as ‘on the cadaveric waiting list’, and these were excluded from analysis. This included one person transplanted outside Australasia and 43 recipients of multiple organ grafts.

Compared with those who were never grafted, those who received cadaveric grafts at some stage were younger (at ESRD entry), more likely to be male, and receiving haemodialysis rather than peritoneal dialysis (Table 1Go). The odds ratio (OR) for receiving a graft for the 35% of people aged >=50 years on the waiting list, compared with those <50 years, was 0.60 [0.55–0.67]. Both types of diabetes were associated with a reduced chance of receiving a cadaveric graft. The presence of coronary artery disease at ESRD entry was also associated with a reduced chance of ever being grafted, as was lung disease and smoking at ESRD. Indigenous people [Aboriginal/Torres Strait Islander (TSI) or Maori/Pacific Islander (PI) people] were significantly less likely to receive a graft (OR 0.37 [0.29–0.48], P<0.0001 for Aboriginal/TSI, OR 0.34 [0.27–0.42], P<0.0001 for Maori/PI, both compared with non-indigenous group).

Over the time period studied, there was some variation in those accepted onto the active transplant waiting list. The proportion of those listed with diabetes increased, and the proportion with chronic lung disease decreased. The proportion that were males or had coronary artery disease did not vary, nor did the average age (Table 2Go). There was, however, no significant differences across the three time periods examined in survival in either the dialysis or transplant groups (Figure 2Go).


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Table 2.  Comparisons of those waitlisted, by year of placement on active cadaveric transplant waiting list

 


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Fig. 2.  Kaplan–Meier graphs of patient survival, compared across time periods. The top panel shows survival of the group receiving dialysis treatment while waitlisted for transplantation, by year of waitlisting (log rank test, P=0.20). The lower panel shows mortality from time of transplantation, by year of transplantation (log rank test, P=0.14): 1991–4 (solid line), 1994–7 (dotted line) and 1997–2001 (dashed line).

 
In the primary analysis, mortality risk was higher in the immediate post-transplant period (0–3 months), but this steadily reduced until at 12 months there was a considerable survival advantage compared with the dialysis group. The mortality among those receiving dialysis treatment following graft failure did not significantly differ from the comparison group (Figure 3Go). Cox regression analysis was performed to adjust for age, gender and co-morbidity differences between the grafted and non-grafted groups. Smoking, type 1 and type 2 diabetes, coronary artery disease, peripheral vascular disease and indigenous race all were independently predictive of increased mortality. The adjusted model still showed significant survival benefit to the transplanted group (Table 3Go), with similar benefits in both older and younger age groups. There was no interaction between the degree of HLA matching (0–2 versus 3–6 mismatches) and the time-dependent covariates. Mortality rates post-transplantation did not vary with type of dialysis pre-transplantation (peritoneal versus haemodialysis). There was however a statistically significant interaction between indigenous race (Aboriginal and Maori/PI combined) and the term for survival >=12 months post-transplantation. In this model, indigenous status was thus associated with poorer survival overall and, in addition, the interaction term for indigenous (>=12 month follow-up) was significant (P=0.003). This indicated that the survival benefit in the longer term after transplantation was not as great in this group, even after adjustment for other co-morbidities. Finally, a term was fitted to the model examining the interaction between the time-dependent risk after transplantation and the three time periods examined. This indicated there was no overall interaction between the time periods and the time-dependent covariates for risk of transplantation (Wald test, P=0.23).



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Fig. 3.  Mortality rates [95% CI] post-transplantation compared with the dialysis group (which includes pre-transplant course of those later transplanted).

 

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Table 3.  Hazard ratios of time post-transplantation relative to dialysis treatment, from Cox regression

 
As well as rates, the causes of death differed between the dialysis and transplant groups. The long-term benefit in the transplant group accrued from reduction in cardiovascular and infective but not malignant deaths (Table 4Go). Event numbers in some groups were however small.


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Table 4.  Hazard ratios for cardiovascular, infective and malignant causes of death during dialysis versus stages of transplant function

 
In the secondary analysis, the deaths in the 60 days after loss of graft function were analysed separately rather than attributed to the period of transplantation. In this analysis, there was no excess risk of mortality seen during the period of graft function (for first 3 months post-transplantation, HR 1.0 [0.73–1.5], P=0.8). However, there was a substantial increase in mortality risk in the 60 days after graft failure. This difference was more pronounced where the duration of graft function had been short (Table 5Go).


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Table 5.  Hazard ratios of risk of death by time post-transplantation

 



   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The overall results here for graft recipients compared with the comparison dialysis group demonstrate that, after an initial period of increased risk, recipients of kidney transplants enjoy a substantially lower mortality rate than the comparison control group. The early increased mortality is likely to be due to peri-operative mortality and infections, the latter related to the immunosuppression required post-transplantation. This excess is short lived; by 3 months after operation, the mortality rate is less than the comparison group and in the longer-term rates are ~20% of the comparable control group. This excess is attributable to those deaths which occur in the early period after loss of transplant function, especially when the duration of graft function was short. When these deaths were analysed separately there was no excess mortality in the early post-transplant period. In contrast, when there had been a long period of graft function (>=12 months), removal of the deaths early after loss of graft function from this group did not greatly affect the survival benefit seen.

The aetiology of the excess risk immediately after loss of graft function is clearly related to the transplantation process. Whether both the loss of graft function and subsequent deaths arose from the same circumstances or whether the loss of graft function itself caused the increased risk was not examined here.

The cause-specific analyses (Table 4Go) show reduction in mortality from all causes other than malignancy. Increased numbers of malignancies have been reported post-transplantation [15] but were not shown statistically in the duration of follow-up of this cohort. The mortality rates for those receiving dialysis treatment after loss of graft function were not significantly different from the comparison group.

The better survival of patients placed on the waiting list for kidney transplantation compared with those not on the list is not surprising, given that the selection process appeared to exclude those at high risk of adverse outcomes due to poor health. Although crude age-specific rates have been compared for dialysis versus transplant patients [16], this is the first time overall Australasian results have been compared in the framework of a formal survival analysis with appropriate comparators. The reduced rate of waitlisting of Aboriginal and Maori people is substantial, but not explained by the reported co-morbidities.

Comparisons of this type always generate discussion about methodology and in particular the appropriate comparison group, given that the ‘ideal’ design of randomizing people to transplantation or dialysis treatment is impossible. The analysis here is by ‘intention-to-treat’, and people remained in the dialysis cohort even if they were subsequently removed from the active transplant list. The transplanting process is in some ways analogous to ‘randomization’ insofar as, at time of listing, whether and when a person ultimately receives a transplant is not known. However, because older people and those with co-morbid conditions are less likely to receive a graft, statistical adjustment is required for differences between groups to obtain an unconfounded estimate of the effects of transplantation.

The best approach to the comparison of the two types of treatment has been discussed both in clinical [1,5] and statistical [17] fora. Inclusion of those treated by dialysis but not listed for transplantation raises issues of selection bias. It is also important to analyse survival from time of listing to avoid the ‘time-to-treatment’ bias from the mortality in the early phase of ESRD treatment, as those listed are ‘survivors' of this period. Here we are reliant on the data as reported 6 monthly by the caring clinicians, and have therefore used a surrogate date. The listing provided to ANZDATA was incomplete, as demonstrated by the transplants performed on recipients not reported as ‘listed’. This raises the issue of ascertainment bias, but this would exclude both ultimate recipients and non-recipients from the analysis, and only bias results if there were systematic under or over-reporting of higher-risk patients. As the number of cadaveric grafts performed in this group was so small we do not feel this will influence the outcomes.

Some have suggested dialysis patients without co-morbidities as an ideal comparison population [2], but this has not been adopted elsewhere and does not address the issue of whether the survival benefit extends across all ages and co-morbidity profiles. Although we are able to account for co-morbidities to some extent, were are limited by the level of detail available. In particular, ANZDATA does not collect information about severity of disease. Among those with co-morbid diseases, if the severity were greater in the group who did not receive a transplant (as may have occurred because of survivor or ‘attrition’ bias) then the statistical adjustment on the basis of available information will not have fully corrected for this. Other limitations relate to the recording of whether people are on the active transplant list. For part of the follow-up period, details were sought as to whether patients had been temporarily or permanently suspended from the list, but this data was incomplete as the details of the time period spent de-listed were not available. To remove people from analysis at removal from the waiting list would also create further biases in the analysis, and fail to attribute to dialysis treatment morbidity which developed during that time. Others with a similar propensity to develop similar disease will have been transplanted and they would not have been censored from the transplant group. There will also be a second selection process once on the list, whereby those who become unwell become less likely to receive a graft (by exclusion from the list). Our analysis has attributed this increased mortality to dialysis on the basis all people met the initial criteria for the waiting list, but there is potential for ‘attrition bias’.

We excluded recipients of grafts from living donors from analysis. This group differs from those on the cadaveric waiting list: there is no element of chance about availability of an organ, and the duration of the time spent pre-dialysis is under direct medical control and usually very short. Similarly, we excluded recipients of grafts performed outside the countries covered by the ANZDATA Registry, and recipients of multiple organ grafts, as in both cases there may have been substantial differences in aspects of the transplant assessment process, and risk of various outcomes.

The use of time-dependent covariates in the Cox survival models here performs two functions. First it allows for differing times spent on the waiting list (avoiding time-to-treatment bias) and secondly by using a series of terms for post-operative time, it avoids the assumption of proportional hazards, which is not warranted in the post-transplant period when the mortality risk relative to dialysis can be expected to change. Although several reports have incorporated the first point, the modelling of the changing rates after transplantation has often not been included, despite being clearly demonstrated by reports from USRDS and UNOS data [5,18], confirmed more recently [1,4,9]. Most have used discrete time periods for varying risks, although Port et al. [5] attempted to use an exponential decay term for survival over this period.

The results (in terms of relative risk) here are similar to those reported elsewhere, although the absolute rates of mortality (particularly for the dialysis group) differ between countries. In particular, Wolfe et al. [1] using US data showed a relative risk of mortality in the immediate post-operative period was 2.8, falling to a long-term relative rate of 0.32 from 12 months.

The interaction between indigenous status and the mortality risk post-transplantation suggests that the benefit from transplantation is not as great among this group, as well as the reduced access to transplantation. Nevertheless, even after allowing for the interaction, there still remains a survival benefit for this group compared with dialysis treatment.

Despite the limit on follow-up of this cohort to a decade, mortality rates among transplant recipients are stable beyond 5 years post-transplantation [20] and the survival advantage is likely to be maintained for the duration of graft function. The mortality rates in the group who returned to dialysis following loss of graft function were comparable with those in the comparison group. This comparison however was restricted by the relatively small number of events in this group. With longer follow-up of this cohort, there will be both a greater number of people returning to dialysis treatment and also an increase in the length of graft function (and associated immunosuppression) which may alter the pattern of mortality seen when transplant function is lost.

Outcomes rates for both dialysis and transplant treatment are however changing. Although the cohort we studied included only the past decade, there have been considerable changes in immunosuppressive therapy, especially over the past 5 years with the availability of tacrolimus, mycophenolate and sirolimus. While these agents may affect rejection rates and graft function, whether they have an effect on patient mortality (the endpoint considered here) is unclear. Changes in dialysis treatment may also affect this comparison, as there have been considerable changes in beliefs about optimal doses of both haemodialysis and peritoneal dialysis over the past 10 years. Despite the changes in those accepted onto waiting lists over the time period here, no significant difference in either transplant or dialysis outcomes over time was demonstrated, nor did the relative advantage change over time. Given that similar survival advantages were shown regardless of degree of co-morbidiy or age, changing acceptance practices would not be expected to influence the overall result. These subgroups also reflect limitations of power of the analyses here; while the cohort sizes are large, survival analyses derive their power from the number of outcomes, which is considerably smaller, particularly for those who were waitlisted or transplanted in recent years. The effect of trends over the time period of this study has also been examined in US data by Meier-Kriesche et al. [21] who demonstrated that the improvement in outcome from both dialytic and transplant therapies were comparable, and the relative difference therefore maintained.

Rates of transplantation are limited by availability of suitable organs. Despite a number of changes in recent years, rates of cadaveric organ donation have remained static at the national level for both Australia and New Zealand [22]. The data here suggest that improving the availability of organs for cadaveric renal transplantation would lead to improved life expectancy in this group, as well as the lower cost of therapy and better quality of life associated with this form of RRT.



   Acknowledgments
 
ANZDATA relies on the conscientious contributions of all renal units throughout Australia and New Zealand for the regular collection of data. ANZDATA is supported by grants from the Australian Commonwealth and State governments, the New Zealand government, the Australian Kidney Foundation and has received support as donations from Novartis Pharmaceuticals, AMGEN Australia, Janssen Cilag, Wyeth-Ayerst and Fresenius Medical Care.



   Notes
 
Correspondence and offprint requests to: ANZDATA Registry, The Queen Elizabeth Hospital, 28 Woodville Road, Woodville South, SA 5011, Australia. Email: stephenm{at}anzdata.org.au Back

Conflict of interest. Dr McDonald's salary is supported by a grant from AMGEN Australia to the ANZDATA Registry. AMGEN played no part in the Registry operations or preparation or submission of this report. Back



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

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Received for publication: 6. 2.02
Accepted in revised form: 8. 7.02