NDT Advance Access originally published online on February 13, 2006
Nephrology Dialysis Transplantation 2006 21(5):1355-1364; doi:10.1093/ndt/gfk061
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© The Author [2006]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Original Articles: Dialysis and Transplantation
The role of pre-emptive re-transplant in graft and recipient outcome
1 Division of Nephrology, University of Utah School of Medicine, 2 The Geriatric Research, Education, and Clinical Center, Veterans Affairs Salt Lake City Healthcare System and 3 Division of Epidemiology, University of Utah School of Medicine, 4 Division of Nephrology, Kidney and Pancreas Transplant Program, Stanford University Medical Center, Stanford, 5 RenalService.com, Inc. and 6 Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
Correspondence and offprint requests to: Alexander Goldfarb-Rumyantzev, MD, PhD, Division of Nephrology and Hypertension, University of Utah Health Sciences Center, 85 North Medical Drive, East Rm 201, Salt Lake City, UT 84112, USA. Email: alex.goldfarb{at}hsc.utah.edu
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Abstract |
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Background. The effect of the pre-emptive re-transplant, and of inter-transplant waiting time generally, on graft and recipient survival is not well established.
Methods. Analysis of the United States Renal Data System (USRDS) data (1/1/90 through 12/31/00; n = 92 844) was performed. Cox regression was used to analyse time to event, with an additional analysis to stratify by transplant era.
Results. Having a prior transplant, as well as the total number of transplants, was related to an increased risk of graft failure [hazard ratio (HR) 1.24, P<0.001 for history of prior transplant; HR 1.35 per transplant, P<0.001], but not to recipient death. The time waiting for re-transplant slightly worsened the risk for recipient mortality in the entire patient population and in the recipients of single re-transplant (HR 1.003 and 1.004 per month respectively, P<0.001), and for graft failure only in recipients of single re-transplant (HR 1.001 per month, P<0.05). Pre-emptive re-transplant (dialysis-free re-transplant or transplant within 6 days of last graft failure) increased the risk of graft failure (HR 1.36, P<0.001) and did not have any statistically significant effect on recipient survival. The longer duration of prior graft survival but not the type of the graft (living vs deceased) had protective effect on the consecutive graft and recipient survival.
Conclusions. With the potential caveats associated with retrospective data analysis, these results suggest that pre-emptive re-transplantation is associated with increased risk of graft failure, while longer time on dialysis in between transplants is associated with negative effect upon graft and recipient survival in most patient subgroups. The optimal time in between graft failure and re-transplant was not evaluated in this study. Further prospective studies might be needed to confirm the observed effects.
Keywords: pre-emptive transplant; re-transplant; transplant graft survival; transplant recipient survival
Kidney transplantation improves survival for patients with end-stage renal disease (ESRD), compared with remaining on the transplant waiting list [1]. Recent improvements in immunosuppression have reduced the incidence of acute rejection but have had little effect on chronic allograft nephropathy and late graft loss [2]. This is important for two reasons. First, the loss of a primary renal allograft is associated with significant mortality, especially in recipients with type-I-diabetes mellitus (DM) [3]. Second, allograft failure is now one of the leading causes of being listed for transplant, one whose influence will continue to grow as more transplant recipients cycle back through renal replacement therapy (RRT). Strikingly, recipients with repeat transplantation after graft failure showed a substantial improvement in survival over their wait-listed counterparts on dialysis: a 45% reduction in the 5-year mortality for Type I DM patients and a 23% reduction for non-diabetic patients [4].
Still, an important question remains about the timing of the re-transplant. In general, pre-emptive transplant (i.e. without exposing the patient to dialysis) seems to be advantageous for graft survival [5–7]. The length of time on dialysis prior to the first transplant is a predictor of the graft and recipient survival, however relatively short duration of dialysis does not change either graft or recipient outcome [5]. It is unclear if the general advantage associated with pre-emptive transplantation holds for patients with a prior kidney transplant. There is no clear evidence in the literature whether patients who failed a previous transplant should be re-transplanted pre-emptively or be allowed to ‘cool down’ on dialysis before the next transplant. Since the role of RRT in the period between graft failure and a follow-up transplant is understudied in this important and growing set of patients, the aim of this project was to evaluate the effect of pre-emptive re-transplantation on graft and recipient survival.
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Methods |
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Dataset
We analysed the records of all kidney and kidney–pancreas allograft recipients (both pediatric and adults) who underwent transplantation during the period of 1 January 1990 through 31 December 1999, using ESRD-course data collected by the United States Renal Data System (USRDS) and transplant-related data collected by the United Network for Organ Sharing (UNOS). The follow-up period was extended through December 31, 2000. For recipients of multiple transplants, the most recent procedure was considered the transplant of interest (termed the ‘study transplant’ throughout this article to minimize confusion with other transplants). Patient records with missing information regarding graft or patient survival were excluded from the study. A total of 92 844 patients with kidney transplant were identified. The subset of patients with multiple kidney transplants numbered 11 714. We performed an additional analysis on recipients of a single re-transplant (those patients who had two transplants total, n = 10 070), since patients who had multiple transplants are likely unique in many aspects that cannot be easily modelled by using simple predictors such as the number of transplants. Subject population and the subgroups of patients used for analysis are illustrated in Figure 1. This retrospective review was approved by the University of Utah's Institutional Review Board.
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Outcome measures and definitions
There were two outcomes in this study. The first outcome was the time between the study transplant and the failure of its graft. The graft failure date was defined as the date of return to dialysis or re-transplant. The second outcome was the time between study transplant and recipient death. Both outcomes were modelled using continuous survival time variables.
The USRDS reports whether or not a graft is still functioning at the time of patient death. Our definition of graft failure excludes these functioning grafts. In cases where this variable was missing, we used ICD-9 codes to impute graft failure (i.e. the cause of death was coded as one of the following: 3200, graft failure: primary failure; 3201, graft failure: rejection; 3202, graft failure: technical; 3299, graft failure: other; or 3903, miscellaneous: renal failure). Allograft outcome was censored at the earliest of the following events: loss to follow-up, patient death, or the study completion date (31 December 2000) and was analysed as days-to-graft-failure or censor. Patient follow-up was censored at the earliest of loss to follow-up or study completion date, and was analysed as day-to-recipient death or censor.
Statistical analysis
Primary variables of interest
The following independent variables were collected: history of transplants prior to the study transplant, total number of transplants, RRT immediately prior to the study transplant, time between last allograft failure and the study transplant surgery.
Our definition for pre-emptive re-transplant included all patients with dialysis-free re-transplant (n = 788), or those who had <7 days between a graft failure and a re-transplant (the data to calculate this variable was complete for the subset of patients with prior kidney transplants) (n = 1609). The latter group was broader and in fact included all of 788 patients with dialysis-free re-transplant; therefore the number of patients with pre-emptive transplant was 1609. Dialysis-free re-transplant was assumed for any case where the RRT immediately prior to transplant was reported itself as ‘transplant’. RRT modality prior to transplant was derived from the RXHIST USRDS file. We considered this source more reliable than the PRTXDIAL variable from the UNOS file (the latter had >30% missing values, where the RXHIST file only had about 6% missing or unknown values). The time between last graft failure and the most recent transplant was calculated as a difference between most recent transplant surgery date and the failure date of the graft prior to the most recent one; this variable was complete for the subset of patients with prior kidney transplants.
Separate Cox models were used to correct for co-linearity between the primary variables. In particular, these pairs of variables were considered to have a high degree of possible co-linearity and were evaluated in separate Cox models: history of prior transplant as a binary variable and total number of transplants; time after last graft failure and pre-emptive transplant as a binary variable; time after last graft failure and duration of ESRD course; and duration of ESRD course and duration of previous graft function. Use of mycophenolate mofetil (MMF) in the maintenance anti-rejection regimen was used as an indicator of transplant era.
Covariates
The Cox models were adjusted for covariates believed to be potential confounders. Based on our previous work [8] and that of others [9,10], all models were adjusted for the following variables:
- Recipient variables: recipient age, gender, race, height, weight, history of hypertension (HTN) and diabetes, history of prior transplant, total duration of ESRD, total number of transplants, mean and peak panel reactive antibody (PRA) levels, education level, primary source of pay, citizenship (the combination of the last three variables was used as a surrogate for socioeconomic status).
- Donor variables: type of donor (cadaveric or living), heartbeating donor or not, donor age, gender, race, height, weight.
- Transplant procedure variables: day of the week the transplant was done, the year of the transplant, number of matched HLA antigens, and cold storage time.
To adjust for recipient comorbidities, we calculated a comorbidity score similar to one proposed by Davis, which has been shown to be strongly associated with the survival in a prospective study of 97 peritoneal dialysis (PD) patients [11]. The comorbidity score used in this study was calculated based on the following coexisting conditions, each of them contributing one point to the score: cardiovascular disease (defined in USRDS as symptomatic cardiovascular disease or angina/coronary artery disease), symptomatic peripheral vascular disease, diabetes mellitus and HTN. Information about coexisting conditions was obtained from the TXUNOS file (that file's data come from the Transplant Candidate Registration Form), therefore the comorbidities used for this study are those that the patient had at the time of listing for the study transplant. We did not use data from CMS-2728 form in order not to exclude patients who were not Medicare eligible prior to 1995 (prior to 1995 dialysis units and transplant centres were required to fill the Medicare evidence form only for Medicare-eligible patients). To reduce lead time bias the models were also adjusted for total duration of ESRD prior to the follow-up time included in the Cox models. Unrealistic values of the independent variables used in the study were eliminated. In particular, for donors and recipients younger than 13 years of age, the United States CDC Growth Charts were used as a guide for determining valid ranges. The heights and weights of recipients and donors aged 13 and older were based on the acceptable ranges: height (122–274 cm), weight (23–180 kg).
Statistical models
Kaplan–Meier graphs are used to display hypothesized relationships and Cox regression models are used to analyse time to event. To address co-linearity between the primary variables of interest were analysed in separate Cox models, and to evaluate if the outcomes were different in two different immunosuppressive-transplant eras, additional Kaplan–Meier and Cox analyses were performed with stratification by the use of MMF. Data were analysed using SAS (SAS Institute, Cary, NC).
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Baseline characteristics
The characteristics of the study population are presented in Table 1. The recipients were 60% male, 70% white and 27% diabetic, with an average age of 43 years at the time of the study transplant. Roughly one-in-eight (12.6%) had at least one prior transplant. The subset of patients with more than one transplant (re-transplants, n = 11 714) is described separately in Table 1. These recipients were 59% male, 78% white and 16% diabetic, with an average age of 38.5 years at the time of the study transplant. The median time between last graft failure and current transplant surgery was 21.9 months; 13.7% had pre-emptive re-transplants (n = 1609).
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Multivariate analysis
The role of prior transplants
The role of previous transplants was analysed for the entire dataset (n = 92 844); a total of 11 714 patients had been re-transplanted. The Kaplan–Meier curves representing the role of prior transplant in the graft and recipient survival are presented in Figure 2 for any transplant history (Panels A and B) and for the total number of prior transplants (Panels C and D). The analysis represented by Kaplan–Meier graphs is not adjusted for confounding factors and suggest that patients with prior transplants do significantly better than those with the first transplant, and that patients with total of two transplants might survive longer than the recipients of a single transplant. However, when the analysis is adjusted for confounding factors in the Cox model (next) the association between prior transplant and recipient survival loses its statistical significance. In the Cox model, any history of prior transplant was associated with a significant increased risk of failure of the study graft (HR 1.24, P<0.001) but not the risk of recipient death (Table 2); the risk of graft failure (but not the recipient mortality) increases as the number of past transplants increases (increase in HR of 1.35 per transplant, P<0.001). The longer duration of prior graft survival but not the type of the graft (living vs deceased) had protective effect on the consecutive graft and recipient survival (Table 3).
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2 = 218.4, P<0.001) and recipient (Panel B,
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Pre-emptive re-transplant
Altogether, 1609 or 13.7% of all re-transplanted patients had pre-emptive re-transplant (see the definitions above in Methods). Using the Cox model presented in Table 3, pre-emptive re-transplant was associated with an increased risk of graft failure (HR 1.36, P<0.001) but not in recipient mortality (HR 1.02, P = 0.77). The shape of the Kaplan–Meier curves (Figure 3) suggests early graft failure contributing to the poor graft survival in the recipients of pre-emptive re-transplants. Therefore, we repeated the Cox analysis after eliminating the recipients in whom the graft survived 0 days (primary non-functioning graft) (n = 555) and demonstrated similar results. The risk of pre-emptive re-transplant for graft failure had an HR of 1.29 (P<0.001, 95% CI = 1.13–1.46) and the risk of recipient mortality had an HR of 1.12 (P = 0.084, 95% CI = 0.984–1.283). Furthermore, we analysed the subgroup of patients with previous graft survival of
90 days after excluding recipients with previous graft survival of <90 days. In the remaining dataset (n = 10 053) the risk of pre-emptive re-transplant for the graft survival (HR 1.26, P<0.005, 95% CI = 1.09–1.46) and recipient survival (HR 1.24, P = 0.027, 95% CI = 1.03–1.50) was substantially increased.
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Analysis of the subgroup of patients with only one re-transplant (history of single graft failure in the past, n = 10 070) yielded similar results for the graft survival (HR 1.59, P<0.001), while the recipient survival associated with pre-emptive re-transplant was significantly better (HR 0.83, P<0.05).
The effect of waiting time for re-transplant
To examine the effect of the waiting time for the re-transplant we examined the role of the time between last graft failure and the most recent transplant (median time was 21.9 months). Increasing this time interval had significant association with an adverse outcome of the most recent graft (only in patients with a single re-transplant), and the risk of patient death (Table 3).
Stratification by the transplant era
Major changes in immunosuppressive medications were unveiled in the mid 1990s (e.g. tacrolimus was approved for liver transplantation in 1994 and was used ‘off-label’ in kidney transplant soon after its release, MMF and Neoral were introduced in 1995). Therefore, we report a stratified survival analysis by the use or non-use of MMF as a surrogate for a transplant era. In 5.6% of the population the information about use of MMF was missing, the remaining dataset was used for analysis. In the remaining dataset of single and multiple kidney transplants (n = 87 652), 28 360 (32.4%) patients were on MMF, while 59 292 (67.6%) were not. In the dataset of patients with re-transplant(s) with information of MMF use non-missing (n = 11 565) 3693 (31.9%) patients were on MMF, while 7872 (68.1%) were not (Tables 2 and 4). Most of the associations observed in the entire dataset were also present in both the MMF(+) and MMF(–) strata.
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Characteristics of the patients who received pre-emptive re-transplants
We evaluated the subgroups of preemptive re-transplant patients and non-pre-emptive re-transplant patients to assess their potential distinguishing characteristics. We evaluated the percent of the whole ESRD course prior to the study transplant spent on dialysis or with another transplant. Patients with pre-emptive re-transplants had a greater percentage of their pre-transplant ESRD course up to the time of the study transplant spent with transplant (50.6±43.6% vs 42.0±32.6% in non-pre-emptively re-transplanted patients, P<0.001), but lower percentage time spent on haemodialysis (HD) (34.6±38.1% vs 38.0±31.9%, P<0.001) and PD (9.4±21.8% vs 11.5±21.5%, P = 0.083). The duration of the ESRD was longer in patients without pre-emptive re-transplant. Thirty percent of the pre-emptive re-transplant patients had living donors, while only 17.6% of those without pre-emptive re-transplant had living donors. The rest of the group characteristics are presented in Table 1.
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Discussion |
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The prediction of renal transplant recipient and graft survival is an important clinical issue, especially in view of the growing shortage of donor organs [2]. Based on data from the UNOS and the North American Pediatric Renal Transplant Cooperative Study (NAPRTCS), the factors predicting graft survival have been studied extensively in both adults and children [8,9].
Traditionally, ESRD patients would take a course of HD or PD, or both, possibly followed by one or more transplants. Increasingly, patients are opting for transplantation as the very first ESRD treatment modality, a choice labelled ‘pre-emptive transplant’. Much of the enthusiasm for pre-emptive transplantation stems from reports that they are advantageous for graft and recipient survival [6,7,12,13], while increased time on dialysis prior to a transplant is a predictor of negative short-term graft outcome [5,7].
More specifically to this study, factors that influence re-transplant graft survival have been identified as well. Notable roles have been identified for the period of survival of the previous graft, the use of cyclosporin A, the level of preformed antibodies, the ESRD cause (in particular, diabetes and analgesic nephropathy), and the patient's gender [14]. Gjertson recently delineated the top five factors affecting 1-year re-graft survival rates (transplant centre, duration of first graft function, donor age, recipient body mass index and year of transplant) [15]. He also showed that long-term allograft outcome depended on donor age, transplant centre, recipient age and race and donor relationship.
In this article we demonstrate a better graft outcome, but not recipient survival, with the first transplant compared with the patients with prior history of transplants; latter had a 24% higher risk of graft failure. Each additional transplant in the past incrementally increased the risk of graft failure. Since re-transplantation patients occupy an ever-increasing portion of the transplant waiting list, it is reasonable to ask whether pre-emptive re-transplantation should be added to these lists of important predictors. While numerous studies, including one by our group [5], demonstrated the positive association between pre-emptive first kidney transplant and graft and recipient survival, the effect of pre-emptive re-transplantation on the graft and recipient outcome has not been established.
Our data suggest that the risk of graft failure is actually higher in pre-emptive re-transplant patients by 36%. We did not find any association between the recipient survival and pre-emptive re-transplant, except for the subgroup of patients with a single re-transplant, where the risk of death decreased by 17%. The result that pre-emptive re-transplant decreases graft survival but increases recipient survival in the subgroup of patients with single re-transplant is somewhat counterintuitive; better allograft outcome should translate into better patient survival. One can imagine that lead time bias, which favours healthier recipients at the outset, may play a role here. This effect would represent a true bias rather than true improvement in survival.
Interestingly, the recipients of the non-pre-emptive transplant in our study on average had significantly more kidney transplants in the past compared with recipients of the pre-emptive re-transplant. Also, there is a greater proportion of deceased donors in the recipients of non-pre-emptive re-transplant. Furthermore, the comorbidity score is higher and total duration of ESRD is significantly greater in the recipients of the non-pre-emptive re-transplant, which should also adversely affect their outcome. The comparison between the groups does indicate that the recipients of the non-pre-emptive re-transplant are at a disadvantage in regards to the classic predictors of the clinical outcome. Despite that, however, in the multivariate model, adjusted for these variables the non-pre-emptively transplanted patients demonstrated a better outcome in respect to the graft survival. Although apparently counterintuitive, this discrepancy is fairly common when the results of simple group comparison are considered separately from multivariate modelling. The multivariate model takes the disparity that exists between the study groups into account and makes an adjustment for it, so these differences become irrelevant to the final outcome of the modelling. The results of the Cox model, therefore should be interpreted as if the study populations were equal in regard to the baseline characteristics included in the model.
We also found that waiting time after the previous graft failure has an association with the worse graft survival in the recipient of single re-transplant and with recipient survival in the whole patient population. However, the effect size of this association is relatively small, and since the recipient survival is calculated as a time period starting at the transplant event, latter association might be confounded by the lead time bias (i.e. patients with pre-emptive re-transplant might have an advantage over those waiting for re-transplant simply because their clock started earlier).
Also to reconcile the better graft survival associated with shorter waiting time and worse survival associated with pre-emptive transplant one might imagine that relatively short period of dialysis in between of the transplants might be beneficial in comparison with pre-emptive re-transplant, while longer time on dialysis might in fact be somewhat detrimental. However, the optimal time on dialysis before re-transplant was not evaluated in this study.
Our findings that suggest that pre-emptive re-transplantation is associated with worse graft outcome seem to contradict the prior reports of beneficial effect of the first transplant on the graft survival [5–7]. The question is, why pre-emptive transplant is good for the first kidney transplant, but negatively affects the outcome of the consecutive transplants. We hypothesized, that this phenomenon could be explained by several different mechanisms based on the difference between the first and subsequent transplants. Recipients of re-transplant have been exposed to immunosuppressive medications for a period of time, might have accumulated additional comorbidities and experienced side effects of the medical treatments. The effect of dialysis on T-cell activity [16–19] and also the withdrawal of immunosuppressive medications with subsequent recovery of the immune system, may be the mechanisms of the positive effect of the dialysis on subsequent graft outcome. Hypothetically, the discontinuation of the immunosuppressive medications and recovery of the immune system in between transplantations might be an important factor in reducing the risk of viral infections and malignancies.
Furthermore, to explain the negative role of pre-emptive re-transplantation, we hypothesized that better clearance of toxic products and drug metabolites might be achieved with dialysis, as compared with the failing kidney allograft, so that patients with a failed graft receiving the pre-emptive re-transplant might be more ‘uraemic’ at the time of transplantation than those, who were dialysed prior to re-transplantation. It is also possible, that patients with failing graft might try to hold on to the poorly functional kidney and delay the initiation of dialysis. By the same token, patients with failing graft might be in general more ‘uraemic’ than those with native kidney failure, which would explain the opposite effect of the pre-emptive transplantation on the graft survival in recipients of the first vs subsequent transplantations.
It is worth mentioning, that since using the year of the transplant as an indicator of transplant era may introduce bias in the statistical analysis, we selected the use of MMF, that came on the market in mid-1990s, as a surrogate of the transplant era. Evaluating particular immunosuppressive medications was not the focus of our investigation. Since MMF is only an indicator of the transplant era, we do not necessarily think that it has direct effect on the outcome. However, the question remains, why is it that in patients transplanted in the late era (on MMF) the negative effect of the pre-emptive re-transplant is not statistically significant. Since the HR between the groups is very similar, the non-significant effect in patients on MMF may simply be a reflection of the smaller sample size (among patients with re-transplant 3693 were on MMF and 7872 patients were not on MMF).
Finally, since this study was based on the analysis of the large dataset, there are some issues in interpreting the results that need to be pointed out. The power of the large amount of data leads to the small and clinically non-significant associations still demonstrating statistical significance in the analysis. Therefore, the associations with the borderline P-value (<0.05), while technically significant, should be interpreted with caution.
Also, in this analysis only patients who survived from the graft failure to the next transplant were included in the non-pre-emptively transplanted group, which potentially introduces a survivor bias. In other words, study excludes those patients who were transplanted once, failed the graft and died while being on the waiting list, before getting the next graft. That potentially may allow for selection of the healthiest recipients in the group with non-pre-emptive re-transplant. The multivariate model adjusted for the specific covariates such as comorbidity index may reduce this bias, but it is certainly problematic to exclude patients who died on the waiting list from the analysis altogether. We considered including patients into the analysis with failed graft, who were on the waiting list for re-transplant into the analysis and classifying them as non-pre-emptive re-transplant. However, that would have introduced additional problems: some of the patients on the waiting list might have never been transplanted for reasons other than death, also the analysis of the graft outcome for this subpopulation would have been impossible, because the re-transplant have never happened. Therefore, the analysis was done only based on the patients, who received the re-transplant, while those, who could have potentially received it, but had not survived, were excluded. Therefore the results of the study should be interpreted with caution due to the potential survivor bias, where only patients who survived from the last graft failure to the next transplant were included in the study. In other words, regarding the recipient survival the results of the study should be interpreted as applicable only to those patients who survived to re-transplant; one should also keep in mind the potential residual survivor bias (even after adjusting for comorbidity index) while interpreting the results of the graft survival.
With the potential caveats associated with retrospective data analysis, these results suggest that pre-emptive re-transplantation is associated with increased risk of graft failure, while longer time on dialysis in between transplants is associated with negative effect upon graft and recipient survival in most patient subgroups. The optimal time in between graft failure and re-transplant was not evaluated in this study. Further prospective studies might be needed to confirm the observed effects.
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Acknowledgments |
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This study was supported in part by the Dialysis Research Foundation (Ogden, UT) and the Department of Veterans Affairs (under TRP 02-147). The data reported here have been supplied by the USRDS. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as official policy or interpretation of the US government.
Conflict of interest statement. None declared.
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Accepted in revised form: 21.12.05
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