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Keywords:

  • Era effect;
  • graft survival;
  • half life;
  • kidney transplantation;
  • long term outcomes;
  • projection

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Impressive renal allograft survival improvement between 1988 and 1995 has been described using projections of half-lives based on limited actual follow up. We aimed, now with sufficient follow up available to calculate real half-lives.

Real half-lives calculated from Kaplan-Meier curves for the overall population as well as subsets of repeat transplants and African Americans recipients were examined.

Real half-lives were substantially shorter than projected half-lives. As a whole, half-lives have improved by about 2 years between 1988 and 1995 as compared to the earlier projected 6 years of improvement. The improvement seems to be driven primarily by the improvement in graft survival of re-transplants. First transplants showed a cumulative increase in graft survival of less than 6 months.

Projected half-lives are a risky estimation of long-term survival especially when based on short actual follow up. First transplant survival has only marginally improved during the early years of post transplant follow up while no significant improvement in long-term survival could be detected between 1988 and 1995. Redirection of attention from early endpoints towards the process of long-term graft loss may be necessary to sustain early gains in the long term.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Survival analysis in renal transplantation have traditionally been made by Kaplan-Meier estimates, because of the necessity to evaluate data on outcomes before all patients reach a certain follow-up time (1). Each patient contributes to the survival estimate only up to the time they have available follow up, so that with increasing length of follow up progressively fewer patients contribute to the survival estimate. In this sense Kaplan-Meier survival estimates are only an estimation of the real survival outcome of the entire study population. In fact in order to make somewhat reliable deductions from Kaplan-Meier survival estimate comparisons, sufficient patients have to be at follow up at the point of analysis.

A calculated half-life is a projection of survival which has many assumptions in itself, most importantly that the slope over time follows a predictable function (2). As opposed to survival estimates by Kaplan-Meier analysis (where at least one patient has to have reached the last follow up), projected half-lives are based on predicted survival probabilities beyond any real observations available. Moreover it is important to remember that in general when half-lives are calculated, only a fraction of patients have reached the complete follow up, which the calculated slope is based on. In most cases a half-life uses the estimated survival at a given follow up, as opposed to the real survival (survival based on complete follow up information), and than projects the long-term survival based on this data. Therefore, the resulting projection is based on an estimate with two possible sources of bias and variability; first the incomplete follow up and secondly the variability of the slope. In general, extreme caution is to be advised when making these extrapolations and long-term projections as they result in easy to understand numbers, which for that very reason are easily over interpreted. In other words, when a population is described to have a life expectancy of 14 years, none of the above described potential variability is expressed in that number. As these numbers are simply projections, based on assumptions which may or may not be born out, particular care must be taken when using these statistics. Therefore, without sufficient understanding of the variability incorporated into such projections, these hard numbers can be particularly misleading.

As we now have sufficient follow up to describe actual half-lives (based on actual follow up) for cadaveric transplants until 1995, we analyzed the US Scientific Renal Transplant Registry (SRTR) database for renal transplants executed between 1988 and 1995 to compare previously projected half-lives (3) and real half-lives, and to obtain hard numbers to see how the survival of renal transplants has evolved in that period of time. This analysis should provide a better understanding of long-term results by evaluating if short-term improvements have really translated into a long-term advantage in renal allograft survival.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We analyzed 57 961 solitary deceased donor and 19 976 living donor renal transplants between 1988 and 1995 from the SRTR database to examine the long-term effects of the era of transplant on patient outcomes. Kaplan-Meier survival curves were used to assess the unadjusted half-lives and survival rates for overall and death censored graft survival in this cohort. Subsequently, we examined these effects restricted to first transplants and various additional strata from this era.

In order to quantify the years of graft life gained over the span of the analysis, area under the Kaplan-Meier survival curves were calculated (4). Cumulative graft years per patient were calculated using the summation of the trapezoidal areas between each follow-up time point under the survival curves for the applicable strata. The difference between the plots areas with equivalent follow up were interpreted as the graft years gained between applicable comparisons. Calculations were extended to 8 years post-transplant which represented the maximum period of follow up for the last year of study.

Re-transplants were determined using the indicator variables supplied by the SRTR in the registry database. First year acute rejection was determined by any indication of rejection or treatment for rejection within the initial transplant hospitalization, 6-month, or 1-year follow-up records for transplant recipients. Last follow-up information was available through July 2003.

Cox proportional hazard models were used to estimate the effect of year of transplant adjusted for potential confounding factors; models were corrected for donor and recipient demographic factors, length of dialysis time, donor cause of death, primary diagnosis of recipient, panel reactive antibody (PRA) level, cytomegalovirus (CMV) matching, cold ischemia time, and human leukocyte antigen (HLA)-A, B, and DR matching.

Analyses were performed on SAS software (v9.0, Cary, NC, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The overall graft survival Kaplan-Meier plots for first deceased donor transplant recipients are displayed in Figure 1. The curves displayed separation in the early post-transplant period, but seemed to narrow with additional follow-up time resulting in relatively similar half-lives. Graft loss not due to patient death also displayed relatively minor variation in this period. The Kaplan-Meier survival curves for death censored graft survival with the applicable 8-year estimated survival rates are displayed in Figure 2. For all transplants (including re-transplants), we found the real half-lives of the univariate survival curves by the year of transplant and compared them to previously published (3) projected half-lives (Figure 3). The actual half-lives were significantly lower, particularly for more recent years as the trend for improvement in the projections did not appear to correspond with extended amount of follow up for this patient population. Of note, the actual half-life for 1995 was estimated as this cohort only reached 51% survival for the latest follow-up data at the time of this analysis. The total per patient average years with a functioning transplant, as calculated by the area under the survival curve, by year of transplant through 8 years of follow up for first living and deceased donor recipients are illustrated in Table 1. The difference in graft years between the extremes of 1988–1995 accounted for a cumulative impact in the 8-year follow up of 4.7 months per patient (Figure 4). Repeating the area under the curve calculations for the multivariate models revealed a wider disparity from 1988 to 1995 relative to the univariate equivalent (Figure 5), the adjusted impact of the year of transplant was approximately 8.4 months cumulatively for the 8-year follow up. Re-transplant rates significantly decreased over this era; from 1988 to 1995 the re-transplant rates, respectively, were 23.8%, 20.9%, 19.5%, 19.2%, 17.3%, 14.2%, 14.2%, and 14.2%. Survival rates for re-transplants did appear to significantly improve associated with the more proximate year of transplant (Figure 6).

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Figure 1. Kaplan-Meier overall graft survival by year of transplant: first deceased donor transplants 1988–1995.

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Figure 2. Kaplan-Meier death censored graft survival by year of transplant: first deceased donor transplants 1988–1995.

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Figure 3. Projected half-lives vs. actual half-lives in deceased donor transplants: 1988–1995 (including re-transplant recipients).

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Table 1.  Eight-year univariate and multivariate cumulative graft years per patient in deceased donor transplant recipients by year of transplant*
Year of TransplantUnivariate EstimatesMultivariate Estimates
  1. *calculated as area under the respective survival curves.

19885.35.2
19895.45.4
19905.55.5
19915.75.7
19925.65.6
19935.65.7
19945.65.8
19955.75.9
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Figure 4. Increased cumulative graft years per patient in deceased donor transplant recipients Kaplan-Meier survival curves from transplants in 1988 and 1995.

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Figure 5. Increased cumulative graft years per patient: Cox proportional hazard model survival curves from first deceased donor transplants in 1988 and 1995.

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The multivariate model effects of the year of transplant did display a mild risk associated with transplants in earlier years. The adjusted relative risks and 95% confidence intervals for overall graft loss by year of transplant for both deceased donor and living transplants are displayed in Table 2. Significant covariates in the deceased donor transplant recipient model are displayed in Table 3. Our examination of the association of the year of transplant in the presence or absence of first year acute rejection did not reveal any specific trend by year of transplant in either strata for first transplant recipients. In a subset analysis we found that the impact of the year of transplant in this era on overall graft survival appeared more impressive in African American recipients relative to Caucasians (Figure 7).

Table 2.  Cox proportional hazard model estimates for association of year of transplant and overall graft loss: adult first solitary transplants 1988–1995
Year of transplantDeceased donor transplantsLiving transplants
Hazard estimate (1995 as reference)95% confidence intervalHazard estimate (1995 as reference)95% confidence interval
19881.359(1.286, 1.436)1.097(0.977, 1.233)
19891.287(1.218, 1.359)1.091(0.976, 1.220)
19901.238(1.174, 1.306)1.067(0.956, 1.192)
19911.176(1.114, 1.240)1.074(0.964, 1.197)
19921.213(1.150, 1.279)1.098(0.987, 1.220)
19931.125(1.067, 1187)1.087(0.981, 1.205)
19941.063(1.008, 1.121)0.984(0.889, 1.090)
Table 3.  Cox proportional hazard model estimates for significant covariates associated with overall graft loss in deceased donor transplant recipients (1988–95)
ParameterHazard ratioConfidence intervalPr > chi square
CMV D+/R- (ref: D-/R-)1.074(1.035, 1.114) 0.0002
Donor race African American (ref: Caucasian)1.132(1.088, 1.178)<0.0001
Donor age 60–70 (ref: 17–29)1.806(1.708, 1.910)<0.0001
HLA-A 2 MM (ref: 0 MM)1.070(1.029, 1.112) 0.0006
HLA-B 2 MM (ref: 0 MM)1.067(1.024, 1.111) 0.0020
HLA-DR 2 MM (ref: 0 MM)1.144(1.104, 1.185)<0.0001
Recipient age 65 + (ref: 17–29)1.686(1.572, 1.807)<0.0001
Diabetes as primary diagnosis (ref: GN)1.564(1.510, 1.619)<0.0001
Recipient Female0.939(0.915, 0.963)<0.0001
Recipient race African American (ref: Caucasian)1.377(1.338, 1.419)<0.0001
Dialysis time 36 + months (ref: preemptive)1.137(1.091, 1.184)<0.0001
Donor CV COD1.041(1.010, 1.073) 0.0094
Cold ischemia time (h)1.027(1.014, 1.040)<0.0001
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Figure 6. Kaplan-Meier overall graft survival by year of transplant: deceased donor re-transplants 1988–1995.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Considerations regarding long-term results after kidney transplantation are often based on projections of early results. Now, with sufficient follow up available to evaluate real half-lives in patients transplanted between 1988 and 1995, we compared actual half-lives to previously used projected half-lives (3).

Based on projected half life calculations, it was assumed and has become part of the current thinking in the transplant community, that the half-lives of transplanted kidneys had close to doubled from 1988 to 1995 (3). This effect seems to be largely due to an overestimation by the long-term projection of the survival data available at the time of publication. As evident from the data shown in Figure 3, with now sufficient data available to describe real half-lives, between 1988 and 1995 there has been merely a 2-year increase in the half-life of transplanted kidneys. We did this analysis combining first and re-transplants in order to most closely replicate the earlier half-lives estimated as published in the New England Journal of Medicine (3). On closer examination we found that in fact almost the entire improvement in half-lives during the mentioned time period was due to the improvement in re-transplants. As it is evident from the data displayed in Figure 1 and Table 4 the real half-life for first transplants has only marginally changed between 1988 and 1995. The overall trend toward improved half-lives is therefore driven by the improvement in re-transplants and by the lower percentage of re-transplants in more recent era. In fact the impressive improvement in the half-life of re-transplants during the same time period is depicted in Figure 6.

Table 4.  Univariate and multivariate half-lives (hl) by year of transplant (first deceased donor transplants 1988–1994*)
Year of transplantKaplan-Meier HLCox multivariate HL
  1. *insufficient follow up to generate half-lives for 1995 for these models.

19887.57.0
19897.97.7
19907.97.7
19918.07.9
19927.77.7
19937.98.2
19947.98.8

This is not to say that primary renal transplant survival has not increased at all during this time period. In fact there is a significant yet modest survival benefit early on after transplantation which over time translates into an approximate 5-month average gain in first graft survival between 1988 and 1995 as presented in Figure 4. This improvement is far more modest than the originally proposed and widely cited almost doubling in transplant half-lives.

We calculated this modest overall survival benefit by comparing the area under the curve of the survival curves in order to get a cumulative estimate as opposed to an assessment at a single point in time. When survival curves diverge early on and subsequently converge as in this case, comparison of the area under the survival curve may be the most accurate estimate of a cumulative benefit or change in outcome.

The very fact that the real yet modest improvement in first renal transplants has occurred during the earlier follow up after transplantation, while half-lives are close to identical, explains why extrapolations and projections from early data can lead to significant over estimations. This example documents very well how misleading projected half-lives are when based on short follow up.

The other subgroup of patients, who in addition to the re-transplants have had a significant improvement over the time period in question, is African Americans (Figure 7). We believe that the reason for this is that in these 2 groups, African Americans and re-transplants, there was more room for improvement especially in preventing graft loss secondary to acute rejection. In fact it has been shown repeatedly that improvements in immunosuppressive regimens have a proportionally higher beneficial impact in high risk recipients like African Americans (5–7). Now that sufficient data has accumulated to calculate half-lives of transplant kidneys based on real data, it becomes clear that earlier projections of half-lives were overestimating the half-lives but also implying a trend which is really only true for a fraction of patients.

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Figure 7. Eight-year univariate overall graft survival estimates for first deceased donor transplants by recipient race by year of transplant.

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Overall graft survival has clearly improved significantly, even though only marginally (4.7 months between 1988 and 1995), mainly driven by improvements in early post transplant outcomes. Long-term results in first renal transplant remain largely unchanged between 1988 and 1995. In part this is likely secondary to the increasing risk constellation with higher risk donors and recipients in more recent era. Both the donor and recipient populations are ageing and higher risk patients are being transplanted. In fact in the multivariate analysis correcting for the higher risk of the transplant population in more recent years, we found a slightly more prominent, yet still modest effect on graft survival in terms of half-life (Table 4) and cumulative graft years gained adjusted for risk factors (Figure 5). Given the higher risk recipients and the declining quality of the transplant kidneys over the period of study, the small improvement in half-lives can still be seen as a progress. In fact given the fact that correcting for potential confounding variables the increase in half-life is more sustained and also the increment in average graft years gained during the whole period is up to 8.4 months, the increasing risk constellation of the donor recipient population would have lead to worse results if there had not been an advance in the management of transplant patients. This is clearly though a much more modest success as compared to the previously reported radically changing long-term results (3).

It is important to realize that it is difficult to extrapolate late outcomes based on early follow-up data. As powering clinical trials for differences in long-term graft loss has become more difficult because of the excellent outcomes in kidney transplantation (8), investigators are pushed to use early surrogates for long-term outcomes, like early graft survival as in our example, or even less hard endpoints like renal function and acute rejections (9–11). It is important to remember that it is almost impossible to project long-term outcomes in kidney transplantation based on early trends. In fact trends might not be constant over time and past trends might not apply under changing immunosuppression. Calculated half-lives are therefore often misleading especially when long half-lives are calculated based on short actual follow up.

A good approximation of a potential benefit in graft survival can be obtained by the method used in this paper, which compares the area under the Kaplan-Meier survival curve between two groups and calculates the cumulative life years gained over the time of available follow up. Interestingly this method was already described in the original Kaplan-Meier paper, but has received in kidney transplantation outcomes research relatively little attention (1). Even though this method contains some uncertainty due to the fact that not all patients have reached the end of follow-up period, at least this method is based on real data, and if used with circumspection, i.e. enough patients who have completed the follow-up time, can yield useful estimations of differences in cumulative life years saved.

The other important point which is uncovered now with sufficient follow-up time available is that despite important improvements in the short term we have not really made an impact on the long-term outcomes. Clearly there seem to be two different phases in the follow up of a transplant population, an early phase where acute rejection and technical problems have the major impact on graft survival. This phase has clearly been positively affected and in return the average survival of a transplant kidney has improved to a certain extent. However, there seems to be a second phase where the survival slope is affected by different events, e.g. subacute or chronic rejection, calcineurin inhibitor toxicity, or also recurrent disease.

A shift in attention toward the long-term follow-up of renal transplant patients and the prevention of the most common causes of late graft loss, like cardiovascular death and chronic allograft nephropathy will have to occur to really make an impact on long-term outcomes.

In summary long-term renal allograft survival was only marginally improved between 1988 and 1995. The majority of this modest improvement was secondary to improvements in the high risk subsets of African American and repeat transplants. Greater attention will need to be focused on long-term events to further improve long-term graft survival.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The data reported here were supplied by the US Scientific Renal Transplant Registry (SRTR). The interpretation and reporting of these data are the responsibility of the authors and in no way represent an official policy or interpretation of the U.S. Government.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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    Hariharan S, Johnson CP, Bresnahan BA, Taranto SE, McIntosh MJ, Stablein D. Improved graft survival after renal transplantation in the United States, 1988–96. N Engl J Med 2000; 342: 605612.
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    Lee ET. Nonparametric methods of estimating survival functions. In: LeeET, WangJW (eds). Statistical Methods for Survival Data Analysis (3rd Edition). Hoboken , NJ : John Wiley & Sons, 2003: 7376.
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    Hunsicker LG, Bennett LE. Design of trials of methods to reduce late renal allograft loss. the price of success. Kidney Int 1995; 52 (Suppl X): S120S123.
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    Hariharan S, McBride MA, Cherikh WS, Tolleris CB, Bresnahan BA, Johnson CP. Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int 2002; 62: 311318.
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    Kasiske BL, Andany MA, Danielson B. A thirty percent chronic decline in inverse serum creatinine is an excellent predictor of late renal allograft failure. Am J Kidney Dis 2002; 39: 762768.
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    Paraskevas S, Kandaswamy R, Humar A et al.. Predicting long-term kidney graft survival: can new trials be performed Transplantation 2003; 75: 12561259.