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Prognostic indexes in kidney procurement and allocation
Transplantation Reviews 21 (2007) 189 – 194 www.elsevier.com/locate/trre
Prognostic indexes in kidney procurement and allocation Domingo Hernández a,⁎, Margarita Rufino a , José Manuel González-Posada a , Sara Estupiñán a , Germán Pérez a , Domingo Marrero-Miranda a , Armando Torres a , Julio Pascual b a
Department of Nephrology, Hospital Universitario de Canarias, Tenerife, Spain b Department of Nephrology, Hospital Ramón y Cajal, Madrid, Spain
Abstract The quality of organs from deceased donors in kidney transplant (KT) represents one the most crucial factors affecting kidney graft survival. Older donors and donors with unfavorable clinical characteristics are being used more frequently in the renal transplant field. Thus, new allocation system policies are needed to match donor kidneys with recipients based on similar expected survival. Allocation systems based upon a recipient risk score and deceased-donor score may improve outcomes after KT. The aim of this review is to assess the contribution and utility of allocation scoring systems to predict and improve KT outcomes. © 2007 Elsevier Inc. All rights reserved.
1. Introduction The quality of organs from deceased donors in kidney transplant (KT) represents one the most crucial factors affecting graft survival [1]. Indeed, there have been numerous investigations evaluating potential donor risk factors for graft loss in KT highlighting the importance of the organ characteristics independent of the transplant recipients [2,3]. The expanded criteria donor (ECD) designation has served major importance to the transplant community by labeling those deceased-donor kidneys with a high risk for graft loss as well as shortening waiting times for patients who consented to receive these organs [4,5]. Obviously, the ECD definition has an importance in the allocation process, and the transplant community confronts a challenge as it struggles to distribute deceased-donor kidneys in a manner that is both biologically and socially equitable. Thus, a recipient risk score (RRS) for improving deceased-donor renal allocation is required. Prognostic indexes involving donor characteristics are needed to make crucial therapeutic decisions at the time of transplant. In addition, an allocation system based upon an RRS and deceased-donor score (DDS) may improve deceased renal allocation system and transplant outcomes.
⁎ Corresponding author. Tel.: +34 922678545; fax: +34 922678545. E-mail address: [email protected] (D. Hernández). 0955-470X/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.trre.2007.07.006
Thus, the aim of this review is to assess the contribution of comorbid conditions, affecting both donor and recipient characteristics, grouped in prognostic indexes on KT outcomes. Therefore, we will focus in the following issues: (1) donor score systems to estimate the quality in deceaseddonor kidneys and (2) scoring systems for improving deceased-donor allocation. 2. Donor score systems to estimate the quality in deceased-donor kidneys The crisis in organ supply makes up a compelling responsibility for the transplant community to maximize the use of organs procured from all deceased donors. As the size of the recipient waiting list and the number of waiting-list deaths increase, older donors and donors with unfavorable clinical characteristics (marginal donors) are being used more frequently [6]. Unfortunately, the use of these marginal donors has a high price. Indeed, the quality of organs from deceased donors in KT represents one of the most crucial factors affecting graft survival. Delayed graft function (DGF), prolonged hospitalization, and graft failure have been associated with the use of marginal donors for KT. Subsequently, there have been numerous research articles evaluating potential donor risk factors for graft loss highlighting the importance of the organ characteristics independent of the transplant recipient [2-5]. In this sense, systematic approaches to assess marginal donors have been
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Table 1 Donor score system for cadaveric renal transplantation Risk factor
Severity
Score
Cause of death
Trauma, anoxia, other Ischemic or hemorrhagic CVA None 1–9 ≥10 ≥90 80–89 70–79 60–69 50–59 b50 b50 50–54 55–59 60–64 65–69 ≥70 No Yes b12 12–24 25–36 N36 None Mild Moderate Severe
0 6
History of hypertension (y)
Donor clearance, Cockcroft-Gault equation (mL/min)
Donor age (y)
History of diabetes Preservation time (h)
Renal artery plaque
Donor score
0 3 6 0 1 2 3 4 6 0 1 2 3 4 5 0 3 0 1 2 3 0 1 2 3 (0–32 points)
To convert values for creatinine clearance to mL/s, multiply by 0.01667. CVA indicates cerebrovascular accident. Reproduced with permission from Blackwell Publishing [7].
Selection of variables and allocation of points were based on results of combined univariate and multivariate analyses performed in 90 consecutive KT recipients of a single center, whereas the scoring system was tested and validated on data from 151 patients who received a KT in other transplantation centers. Thus, deceased donors were stratified on the basis of the following score: grade A (0–5 points), grade B (6–10 points), grade C (16–32 points), and grade D (16–32 points), so that a significant decline in early renal function was observed with increasing donor score and grade of cadaver kidney. Obviously, this donor scoring system may be useful to improve the allocation of marginal donors, but application of this system depends on institutional policies on use of marginal donors and reliance on cadaveric organs for KT. Nyberg et al [8] also elaborated an improved scoring system to assess adult donors for cadaver KT. Their study examining deceased-donor kidneys from the United Network for Organ Sharing (UNOS) registry from 1994 to 1999 provided a quantitative approach to evaluating these organs. In this scoring system were used the 4 donor variables applicable to the ECD designation along with human leukocyte antigen (HLA) matching. The improved scoring was developed from data collected 6 months after cadaver transplantation and then was applied to renal function data obtained 12 months after KT. Consequently, a risk scale of 0
Table 2 Improved scoring system to assess adult donors for deceased renal transplantation Variable
developed during last years to improve outcome after KT. The ECD designation has served major importance to the transplant community by labeling those deceased-donor kidneys with a high relative risk for graft loss as well as shortening waiting times for patients who consented to receive these organs [4]. In particular, the ECD label includes those organs that have an associated relative risk of more than 1.7 (excluding pediatric donations) from the model generated by the Scientific Transplant Registry for the outcome of overall graft loss. This model incorporated donor age, donor history of hypertension, donor serum creatinine level, and donor cause of death. Although this model allows us to know the tremendous impact of ECD on graft survival associated with the magnitude of hazard ratio, there is a great variability in the ECD organs (adjusted hazard ratio ranging from 1.74 to 2.69) as well as in the non-ECD donors (hazard ratio ranging from 1.00 to 1.66), which translates to considerable expected survival differences. Nyberg et al [7,8] have addressed 2 donor scoring systems for cadaveric renal transplantation using their local center data as well as a national database perspective to identify kidneys at highest risk of early graft dysfunction and failure. The first scoring system was based on 7 donor variables available at the time of organ procurement (Table 1) [7].
Age (y) b30 30–39 40–49 50–59 60–69 ≥70 History of hypertension (y) None Yes, duration unknown ≤5 6–10 N10 Creatinine clearance, mL/min ≥100 75–99 50–74 b50 HLA mismatch (no. of antigens) 0 1–2 3–4 5–6 Cause of death Non-CVA CVA Total points (range)
Score 0 5 10 15 20 25 0 2 2 3 4 0 2 3 4 0 1 2 3 0 3 0–39
CVA includes ischemic and hemorrhagic types. CVA indicates cerebrovascular accident. Reproduced with permission from Blackwell Publishing [8].
D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194
to 40 pertaining to the quality of the organ was developed (Table 2). Cadaveric kidneys were stratified by cumulative donor score in grade A (0–9 points), grade B (10–19 points), grade C (20–29 points), and grade D (30–39 points). Donor score and grade of kidney showed an inverse correlation with renal function 12 months after KT. Likewise, this donor score had a significant influence on graft survival during the 6 years after cadaver renal transplantation. Thus, the improved scoring system may provide a proper quantitative approach to evaluate marginal donors and may optimize allocation of these organs in KT. During last years, a variety of techniques have emerged to evaluate pretransplant evaluation of donors. Machine perfusion (MP) has been shown to reduce the incidence of DGF. Parameters of MP such as flow rate and resistive index (RI) correlated with early graft function in several studies involving kidneys from ECDs [9,10]. Criteria to identify pumped kidneys with high likelihood of graft failure have not been standardized, although flow rates of less than 70 mL/min or RI of more than 0.5 has been suggested in previous studies. A recent study compared ECD status, DDS, and RI in a cohort of deceased-donor kidneys that underwent MP [11]. Deceased-donor score was superior to ECD status and RI in its correlation with early and late renal function after KT. In particular, DDS identified a subgroup of patients without ECD (DDS ≥ 20) that functioned similarly to ECD. Furthermore, MP improved early graft function and graft survival, but these benefits were greatest in the group of kidneys with DDS of at least 20. Accordingly, these authors proposed an algorithm for the use of a combination of the DDS system and MP (Fig. 1). This algorithm of selective pumping is evidence based and intended to identify kidneys
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most likely to benefit from pumping and, consequently, reduce the overall cost and improve the allocation of marginal kidneys by matching expected graft and recipient survival. In any case, future investigations are needed to confirm these aspects. Schold et al [1] elaborated a donor kidney risk grade based on significant donor characteristics, donor-recipient matches, and cold ischemia time, generated directly from their risk for graft loss time, to know the impact of donor quality on transplant outcomes. They created a risk index from the summation of the parameter estimates that were applicable for individuals. These authors elegantly used a cluster analysis of those variables associated with graft loss (Cox model) and examined the distribution of this index score, generating intervals that best defined the natural grouping of risk scores. Long-term graft survival was associated with donor grade for the cohort of patients with a minimal of 1 year overall graft survival so that the projected half-lives for overall graft survival in recipients by donor risk grade were as follows: I, 10.7 years; II, 10 years; III, 7.9 years; IV, 5.7 years; and V, 4.5 years. In addition, the association of donor gradation was also strongly related to the incidence of DGF. Thus, the assessment of quality of deceased-donor kidney might be enhanced by this scoring system in KT recipients. Finally, prediction models using logistic regression and tree-based algorithms have been developed to identify risk factors for graft survival. In particular, GoldfarbRumyyantzev et al [12] elaborated a prediction algorithm to identify pretransplant predictors of 3-year allograft survival using a large data set (UNOS) of patients with end-stage renal disease who received a cadaveric kidney or
Fig. 1. Algorithm for use of DDS to receive machine preservation before transplantation. Two asterisks indicate that risk factors for graft failure include long cold preservation time, biopsy abnormality, concerning medical history, and hypotension or oliguria during procurement. Reproduced with permission from Nyberg et al [11].
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kidney-pancreas transplantation between 1990 and 1998. Donor and recipient demographic characteristics and body mass index showed a nonlinear relationship. On the contrary, HLA match showed strong linear relationships with 3-year graft survival. Likewise, prediction of probability of graft survival from the model achieved a good match with the observed survival of the separate data set (correlation of r = 0.998 for logistic regression and r = 0.984 for tree-based model). This prediction model could be used in clinical practice to know kidney allograft survival, but other important postransplant variables such as rejection and immunosuppression were not included in the model. In addition, this model requires much time, which may limit its use in the most transplant centers.
3. Scoring system for improving deceased renal allocation Currently, there is a significant gap between supply and demand of deceased-donor renal allografts. To close this gap, the use of organs from marginal or ECD has increased at most transplant centers during the last years. Indeed, the rate of deceased kidney donations is increasing slowly but remains disproportionate to the number of waiting-list patients. Moreover, the current rate of donation of deceased-donor kidneys makes it difficult to design a kidney allocation scheme that balances optimal utility of this scarce resource with justice. Younger renal transplant recipients often outlive their allografts, whereas older recipients often die before their grafts fail. Thus, the transplant community faces a challenge as it struggles to properly distribute deceased-donor kidneys in a manner that is biologically rational and socially equitable. Several strategies have been suggested to improve the allocation system by decreasing the disproportion in expected survival between transplant recipient and their grafts, such as matching high-risk transplant recipients with high-risk renal allografts and vice versa [13]. The Eurotransplant Senior Program (ESP), which started in 1999, was created to ensure efficient use of kidneys from older donors and increase transplantation in older recipients [14]. In particular, the ESP is an allocation scheme that matches donors with recipients of the same group, with the goal of kidney graft outliving the recipients. After 3 years of collecting patient data, the ESP reported that older kidneys were doing well if kidney graft function was not initially delayed immediately posttransplantation. In addition, the 3-year data showed no differences in kidney graft survival between recipients of kidneys from older donors compared with recipients of donor kidneys obtained through the usual standardized HLA-driven allocation procedures. If other risk factor may be avoided (eg, longer cold ischemia time, surgical damage, retransplantation), an allocation schema using kidneys from older donors for transplantation into older recipients can be successful.
Donor age is a significant risk factor for graft loss after KT. Meier-Kriesche et al [15] examined the question of whether significant graft years were being lost through transplantation of younger donor kidneys into older recipients with potentially shorter life span than the organs they receive. They evaluated, using Kaplan-Meier plots, patient and graft survival for deceased-donor KTs performed in the United States between the years 1990 and 2002. Distribution of deceased-donor kidneys was categorized by donor and recipient age. The actual and projected graft survival of transplanted kidneys from younger donors with the patient survival of transplant recipients of varying ages was calculated. As expected, the survival of grafts from younger donors significantly exceeded the patient survival of recipients older than 60 years. Consequently, the overall projected improvement in graft survival, by excluding transplantation of younger kidneys to older recipients, was approximately 3 years per transplant. Thus, avoiding the policy allocation of young donor kidneys to elderly recipients could significantly increase the overall graft life and, additionally, cost saving. Using the Schold et al [16] 5-risk strata, the propensity of transplant recipients to receive lower-quality kidneys in a cumulative logit model was evaluated in a retrospective cohort study of all deceased-donor adult renal transplant recipients in the United States from 1996 to 2002. Older patients were progressively more likely to receive lowerquality organs (age, ≥65 years, odds ratio, 2.1) compared with recipients aged 18 to 24 years. Likewise, a relatively large association between donor quality and race/ethnic group was also found. In particular, African American and Asian recipients had a greater likelihood of receiving lowerquality organs relative to non-Hispanic white recipients. Neither recipient sex nor patient's primary disease was associated with donor quality. Thus, disparities in the quality of deceased-donor kidneys among transplant recipients seem to exist among certain patient groups that have previously documented access barriers, at least in the United States. Baskin-Bey et al [17] developed a quantitative approach to measure the supply and demand of renal function on an annual basis. In addition, they assessed if the use of deceased-donor kidneys, including ECD kidneys, could be increased by an allocation system that matches donor grafts to wait-listed candidates based on similar years of expected survival. A unit referred to as a renal year (RY) was used to quantify allograft survival. An RY was defined as satisfactory function of a renal allograft for 1 year without the need for renal replacement therapy. Thus, RYs were used to quantify the supply of renal function provided by a transplanted renal allograft and the demand for renal function by a transplanted patient. In particular, annual supply of renal function was determined by the product of the number of deceased-donor kidneys transplanted in that year by the median expected recipient survival. After reviewing the records of 49 206 patients, provided by the UNOS Scientific Renal Transplant Registry, these authors
D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194 Table 3 Median transplant recipient and graft survival time and group size by deceased-donor grade and recipient group Donor grade A
B
C
Median transplant recipient survival (y) RG1 30.0 27.0 RG2 17.3 14.8 RG3 11.4 9.6 RG4 8.0 6.8 Median graft survival (y) a, c RG1 32.9 18.8 RG2 38.1 24.7 RG3 35.3 23.4 RG4 34.6 22.8
D
P
a, b
24.6 13.4 8.6 5.8
22.6 11.7 7.4 5.0
b.0001 b.0001 b.0001 b.0001
11.8 17.6 16.8 16.4
7.80 12.1 11.9 12.0
b.0001 b.0001 b.0001 b.0001
Donor grade A
B
C
Group size: deceased-donor renal transplantations, RG1 855 711 378 RG2 667 599 444 RG3 380 400 338 RG4 47 72 59 Total 1949 1782 1269
D
Total
2002 (n) 20 59 94 18 191
1964 1969 1262 196 5191
Reprinted from Am J Kidney Dis, volume 49, Baskin-Bey ES, Kremers W, Nyberg SL. A recipient risk score for deceased donor renal allocation, pages 28493, copyright 2007, with permission from Elsevier [18]. a Half-life determined by Cox model. b Not censored by graft loss. c Censored by death.
showed that matching transplant recipients and graft survival, based on a system of transplant recipient age and DDS previously described [8], could significantly decrease the gap between renal supply and demand. For patients who underwent transplant in 2002, grade A kidneys were allocated to recipients aged up to 49 years, grade B kidneys to recipients aged 40 to 59 years, grade C kidneys to recipients aged at least 50 years, and D kidneys to recipients aged at least 70 years. This reallocation process resulted in a 14% (9.483 RY) increase in supply compared with nonoptimized or actual RY supply for 2002. This optimization of donor allocation reduced the renal supply deficit by 22%. These data support the theory that marginal donors allotted to older recipients may improve the allocation process. In a more recent study, these authors elaborated an RRS by incorporating additional recipient variables shown to predict recipient survival [18]. This RRS could be used with the DDS to maximize the total number of years of renal allograft function as a means to improve donor allocations. Multivariate Cox regression models were used to derive an RRS and estimate recipient and graft survival as a function of RRS using clinical data of 47 535 adult recipients of deceased-donor renal transplants between 1995 to 2002 from the UNOS. The strongest predictors of recipient survival after KT used in the RRS were recipient age, history of diabetes mellitus, history of angina, and
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time on dialysis. Transplant recipients were stratified, using the RRS, into 4 recipient groups (RG1 to RG4), with decreasing median survival from RG1 to RG4. An optimized allocation was considered when grade A kidneys were allocated to RG1, grade B to RG2, grade C to RG3, and grade D to RG4. The benefit of this allocation was evaluated by using an RY analysis and transplant data from 2002 (Table 3). Accordingly, the demand (95.144 RY) outweighed the supply (84.180 RY) for renal function in 2002 by 10.964 RY. Thus, an allocation scheme based on matching the DDS with RG resulted in a 15% (12.532 RY) increase in supply compared with the nonoptimized or actual RY supply for 2002. Therefore, the RRS combined with a method to assess donor organs may improve donor renal allocation and, consequently, this strategy may increase the effective deceased-donor kidney supply and minimize the number of patients waiting for KT. Despite this evidence, transplantation in older donors has remained controversial because of the scarcity of donated organs and scientific doubts about the success and costeffectiveness of KT in this age group. Obviously, morbidity, mortality, and costs increase, and clinical benefits decrease in older patients and with longer waiting times for KT. Under this view, Jassal et al [19] elaborated a decision analysis model to determine the cost benefits of deceased donors KT vs hemodialysis therapy for older patients. Deceased-donor KT increased overall life expectancy for KT recipients of all ages and comorbidities. As expected, costs associated with KT and the presence of comorbidities increased. Thus, results of this analysis suggest that if a kidney becomes available within a timely period, it may offer substantial clinical benefits to older patients at a more reasonable cost.
4. Conclusion The quality of organs from deceased donors in KT represents one the most crucial factors affecting kidney graft survival. Older donors and donors with unfavorable clinical characteristics are being used more frequently in the renal transplant field. Thus, new allocation system policies are needed to match donor kidneys with recipients based on similar expected survival. Allocation systems based upon an RRS and DDS may improve outcomes after KT. Acknowledgments We thank the renal transplant team from the Canary Islands for their collaboration. References [1] Schold JD, Kaplan B, Baliga RS, et al. The broad spectrum of quality in deceased donor kidneys. Am J Transplant 2005;5:757-65.
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[2] Alexander JW, Zola JC. Expanding the donor pool: use of marginal donor for solid organ transplantation. Clin Transplant 1996; 10:1-19. [3] Schnitzler MA, Whiting JF, Brenan DC, et al. The expanded criteria donor dilemma in cadaveric renal transplantation. Transplantation 2003;75:1940-5. [4] Port FK, Bragg-Gresham JL, Metzger RA, et al. Donor characteristics associated with reduced graft survival: an approach to expanding the pool of kidney donors. Transplantation 2002;74:1281-6. [5] Mandal AK, Kalligonis AN, Ratner LE. Expanded criteria donors: attempts to increase the renal transplant donor pool. Adv Ren Replace Ther 2000;7:117-30. [6] Metzger RA, Delmonico FL, Feng S, et al. Expanded criteria donors for kidney transplantation. Am J Transplant 2003;3(Supp.4): 114-25. [7] Nyberg SL, Matas AJ, Rogers M, et al. Donor scoring system for cadaveric renal transplantation. Am J Transplant 2001;1:162-70. [8] Nyberg SL, Matas AJ, Kremers WK, et al. Improved scoring system to assess adult donors for cadaver renal transplantation. Am J Transplant 2003;3:715-21. [9] Suarez J, Riera L, Franco E, et al. Preservation of kidneys from marginal donors with pulsatile perfusion machine. Transpl Proc 1999; 31:292. [10] Polyak M, Boykin J, Arrington B, et al. Pulsatile preservation characteristics predict early graft function in extended criteria donor kidneys. Transpl Proc 1997;29:3582.
[11] Nyberg SL, Baskin-Bey ES, Kremers W, et al. Improving the prediction of donor kidney quality: deceased donor score and resistive indices. Transplantation 2005;80:925-9. [12] Goldfarb-Rumyantzev AS, Scandling JD, Pappas L, et al. Prediction of 3-yr cadaveric graft survival based on pre-transplant variables in a large national dataset. Clin Transplant 2003;17:485-97. [13] Merion RM, Ashby VB, Wolfe RA, et al. Deceased-donor characteristics and the survival benefit of kidney transplantation. JAMA 2005;294:2726-33. [14] Cohen B, Smits JM, Haase B, et al. Expanding the donor pool to increase renal transplantation. Nephrol Dial Transplant 2005;20: 34-41. [15] Meier-Kriesche HU, Schold JD, Gaston RS, et al. Kidneys from deceased donors: maximizing the value of a scarce resource. Am J Transplant 2005;5:1725-30. [16] Schold JD, Kaplan B, Chumber NR, et al. Access to quality: evaluation of the allocation of deceased donor kidneys for transplantation. J Am Soc Nephrol 2005;16:3121-7. [17] Baskin-Bey ES, Kremers W, Nyberg SL. Improving utilization of deceased donor kidney by matching recipient and graft survival. Transplantation 2006;82:10-4. [18] Baskin-Bey ES, Kremers W, Nyberg SL. A recipient risk score for deceased donor renal allocation. Am J Kidney Dis 2007;49: 284-93. [19] Jassal SV, Krahn MD, Naglie G, et al. Kidney transplantation in the elderly: a decision analysis. J Am Soc Nephrol 2003;14:187-96.
Prognostic indexes in kidney procurement and allocation Domingo Hernández a,⁎, Margarita Rufino a , José Manuel González-Posada a , Sara Estupiñán a , Germán Pérez a , Domingo Marrero-Miranda a , Armando Torres a , Julio Pascual b a
Department of Nephrology, Hospital Universitario de Canarias, Tenerife, Spain b Department of Nephrology, Hospital Ramón y Cajal, Madrid, Spain
Abstract The quality of organs from deceased donors in kidney transplant (KT) represents one the most crucial factors affecting kidney graft survival. Older donors and donors with unfavorable clinical characteristics are being used more frequently in the renal transplant field. Thus, new allocation system policies are needed to match donor kidneys with recipients based on similar expected survival. Allocation systems based upon a recipient risk score and deceased-donor score may improve outcomes after KT. The aim of this review is to assess the contribution and utility of allocation scoring systems to predict and improve KT outcomes. © 2007 Elsevier Inc. All rights reserved.
1. Introduction The quality of organs from deceased donors in kidney transplant (KT) represents one the most crucial factors affecting graft survival [1]. Indeed, there have been numerous investigations evaluating potential donor risk factors for graft loss in KT highlighting the importance of the organ characteristics independent of the transplant recipients [2,3]. The expanded criteria donor (ECD) designation has served major importance to the transplant community by labeling those deceased-donor kidneys with a high risk for graft loss as well as shortening waiting times for patients who consented to receive these organs [4,5]. Obviously, the ECD definition has an importance in the allocation process, and the transplant community confronts a challenge as it struggles to distribute deceased-donor kidneys in a manner that is both biologically and socially equitable. Thus, a recipient risk score (RRS) for improving deceased-donor renal allocation is required. Prognostic indexes involving donor characteristics are needed to make crucial therapeutic decisions at the time of transplant. In addition, an allocation system based upon an RRS and deceased-donor score (DDS) may improve deceased renal allocation system and transplant outcomes.
⁎ Corresponding author. Tel.: +34 922678545; fax: +34 922678545. E-mail address: [email protected] (D. Hernández). 0955-470X/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.trre.2007.07.006
Thus, the aim of this review is to assess the contribution of comorbid conditions, affecting both donor and recipient characteristics, grouped in prognostic indexes on KT outcomes. Therefore, we will focus in the following issues: (1) donor score systems to estimate the quality in deceaseddonor kidneys and (2) scoring systems for improving deceased-donor allocation. 2. Donor score systems to estimate the quality in deceased-donor kidneys The crisis in organ supply makes up a compelling responsibility for the transplant community to maximize the use of organs procured from all deceased donors. As the size of the recipient waiting list and the number of waiting-list deaths increase, older donors and donors with unfavorable clinical characteristics (marginal donors) are being used more frequently [6]. Unfortunately, the use of these marginal donors has a high price. Indeed, the quality of organs from deceased donors in KT represents one of the most crucial factors affecting graft survival. Delayed graft function (DGF), prolonged hospitalization, and graft failure have been associated with the use of marginal donors for KT. Subsequently, there have been numerous research articles evaluating potential donor risk factors for graft loss highlighting the importance of the organ characteristics independent of the transplant recipient [2-5]. In this sense, systematic approaches to assess marginal donors have been
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D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194
Table 1 Donor score system for cadaveric renal transplantation Risk factor
Severity
Score
Cause of death
Trauma, anoxia, other Ischemic or hemorrhagic CVA None 1–9 ≥10 ≥90 80–89 70–79 60–69 50–59 b50 b50 50–54 55–59 60–64 65–69 ≥70 No Yes b12 12–24 25–36 N36 None Mild Moderate Severe
0 6
History of hypertension (y)
Donor clearance, Cockcroft-Gault equation (mL/min)
Donor age (y)
History of diabetes Preservation time (h)
Renal artery plaque
Donor score
0 3 6 0 1 2 3 4 6 0 1 2 3 4 5 0 3 0 1 2 3 0 1 2 3 (0–32 points)
To convert values for creatinine clearance to mL/s, multiply by 0.01667. CVA indicates cerebrovascular accident. Reproduced with permission from Blackwell Publishing [7].
Selection of variables and allocation of points were based on results of combined univariate and multivariate analyses performed in 90 consecutive KT recipients of a single center, whereas the scoring system was tested and validated on data from 151 patients who received a KT in other transplantation centers. Thus, deceased donors were stratified on the basis of the following score: grade A (0–5 points), grade B (6–10 points), grade C (16–32 points), and grade D (16–32 points), so that a significant decline in early renal function was observed with increasing donor score and grade of cadaver kidney. Obviously, this donor scoring system may be useful to improve the allocation of marginal donors, but application of this system depends on institutional policies on use of marginal donors and reliance on cadaveric organs for KT. Nyberg et al [8] also elaborated an improved scoring system to assess adult donors for cadaver KT. Their study examining deceased-donor kidneys from the United Network for Organ Sharing (UNOS) registry from 1994 to 1999 provided a quantitative approach to evaluating these organs. In this scoring system were used the 4 donor variables applicable to the ECD designation along with human leukocyte antigen (HLA) matching. The improved scoring was developed from data collected 6 months after cadaver transplantation and then was applied to renal function data obtained 12 months after KT. Consequently, a risk scale of 0
Table 2 Improved scoring system to assess adult donors for deceased renal transplantation Variable
developed during last years to improve outcome after KT. The ECD designation has served major importance to the transplant community by labeling those deceased-donor kidneys with a high relative risk for graft loss as well as shortening waiting times for patients who consented to receive these organs [4]. In particular, the ECD label includes those organs that have an associated relative risk of more than 1.7 (excluding pediatric donations) from the model generated by the Scientific Transplant Registry for the outcome of overall graft loss. This model incorporated donor age, donor history of hypertension, donor serum creatinine level, and donor cause of death. Although this model allows us to know the tremendous impact of ECD on graft survival associated with the magnitude of hazard ratio, there is a great variability in the ECD organs (adjusted hazard ratio ranging from 1.74 to 2.69) as well as in the non-ECD donors (hazard ratio ranging from 1.00 to 1.66), which translates to considerable expected survival differences. Nyberg et al [7,8] have addressed 2 donor scoring systems for cadaveric renal transplantation using their local center data as well as a national database perspective to identify kidneys at highest risk of early graft dysfunction and failure. The first scoring system was based on 7 donor variables available at the time of organ procurement (Table 1) [7].
Age (y) b30 30–39 40–49 50–59 60–69 ≥70 History of hypertension (y) None Yes, duration unknown ≤5 6–10 N10 Creatinine clearance, mL/min ≥100 75–99 50–74 b50 HLA mismatch (no. of antigens) 0 1–2 3–4 5–6 Cause of death Non-CVA CVA Total points (range)
Score 0 5 10 15 20 25 0 2 2 3 4 0 2 3 4 0 1 2 3 0 3 0–39
CVA includes ischemic and hemorrhagic types. CVA indicates cerebrovascular accident. Reproduced with permission from Blackwell Publishing [8].
D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194
to 40 pertaining to the quality of the organ was developed (Table 2). Cadaveric kidneys were stratified by cumulative donor score in grade A (0–9 points), grade B (10–19 points), grade C (20–29 points), and grade D (30–39 points). Donor score and grade of kidney showed an inverse correlation with renal function 12 months after KT. Likewise, this donor score had a significant influence on graft survival during the 6 years after cadaver renal transplantation. Thus, the improved scoring system may provide a proper quantitative approach to evaluate marginal donors and may optimize allocation of these organs in KT. During last years, a variety of techniques have emerged to evaluate pretransplant evaluation of donors. Machine perfusion (MP) has been shown to reduce the incidence of DGF. Parameters of MP such as flow rate and resistive index (RI) correlated with early graft function in several studies involving kidneys from ECDs [9,10]. Criteria to identify pumped kidneys with high likelihood of graft failure have not been standardized, although flow rates of less than 70 mL/min or RI of more than 0.5 has been suggested in previous studies. A recent study compared ECD status, DDS, and RI in a cohort of deceased-donor kidneys that underwent MP [11]. Deceased-donor score was superior to ECD status and RI in its correlation with early and late renal function after KT. In particular, DDS identified a subgroup of patients without ECD (DDS ≥ 20) that functioned similarly to ECD. Furthermore, MP improved early graft function and graft survival, but these benefits were greatest in the group of kidneys with DDS of at least 20. Accordingly, these authors proposed an algorithm for the use of a combination of the DDS system and MP (Fig. 1). This algorithm of selective pumping is evidence based and intended to identify kidneys
191
most likely to benefit from pumping and, consequently, reduce the overall cost and improve the allocation of marginal kidneys by matching expected graft and recipient survival. In any case, future investigations are needed to confirm these aspects. Schold et al [1] elaborated a donor kidney risk grade based on significant donor characteristics, donor-recipient matches, and cold ischemia time, generated directly from their risk for graft loss time, to know the impact of donor quality on transplant outcomes. They created a risk index from the summation of the parameter estimates that were applicable for individuals. These authors elegantly used a cluster analysis of those variables associated with graft loss (Cox model) and examined the distribution of this index score, generating intervals that best defined the natural grouping of risk scores. Long-term graft survival was associated with donor grade for the cohort of patients with a minimal of 1 year overall graft survival so that the projected half-lives for overall graft survival in recipients by donor risk grade were as follows: I, 10.7 years; II, 10 years; III, 7.9 years; IV, 5.7 years; and V, 4.5 years. In addition, the association of donor gradation was also strongly related to the incidence of DGF. Thus, the assessment of quality of deceased-donor kidney might be enhanced by this scoring system in KT recipients. Finally, prediction models using logistic regression and tree-based algorithms have been developed to identify risk factors for graft survival. In particular, GoldfarbRumyyantzev et al [12] elaborated a prediction algorithm to identify pretransplant predictors of 3-year allograft survival using a large data set (UNOS) of patients with end-stage renal disease who received a cadaveric kidney or
Fig. 1. Algorithm for use of DDS to receive machine preservation before transplantation. Two asterisks indicate that risk factors for graft failure include long cold preservation time, biopsy abnormality, concerning medical history, and hypotension or oliguria during procurement. Reproduced with permission from Nyberg et al [11].
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kidney-pancreas transplantation between 1990 and 1998. Donor and recipient demographic characteristics and body mass index showed a nonlinear relationship. On the contrary, HLA match showed strong linear relationships with 3-year graft survival. Likewise, prediction of probability of graft survival from the model achieved a good match with the observed survival of the separate data set (correlation of r = 0.998 for logistic regression and r = 0.984 for tree-based model). This prediction model could be used in clinical practice to know kidney allograft survival, but other important postransplant variables such as rejection and immunosuppression were not included in the model. In addition, this model requires much time, which may limit its use in the most transplant centers.
3. Scoring system for improving deceased renal allocation Currently, there is a significant gap between supply and demand of deceased-donor renal allografts. To close this gap, the use of organs from marginal or ECD has increased at most transplant centers during the last years. Indeed, the rate of deceased kidney donations is increasing slowly but remains disproportionate to the number of waiting-list patients. Moreover, the current rate of donation of deceased-donor kidneys makes it difficult to design a kidney allocation scheme that balances optimal utility of this scarce resource with justice. Younger renal transplant recipients often outlive their allografts, whereas older recipients often die before their grafts fail. Thus, the transplant community faces a challenge as it struggles to properly distribute deceased-donor kidneys in a manner that is biologically rational and socially equitable. Several strategies have been suggested to improve the allocation system by decreasing the disproportion in expected survival between transplant recipient and their grafts, such as matching high-risk transplant recipients with high-risk renal allografts and vice versa [13]. The Eurotransplant Senior Program (ESP), which started in 1999, was created to ensure efficient use of kidneys from older donors and increase transplantation in older recipients [14]. In particular, the ESP is an allocation scheme that matches donors with recipients of the same group, with the goal of kidney graft outliving the recipients. After 3 years of collecting patient data, the ESP reported that older kidneys were doing well if kidney graft function was not initially delayed immediately posttransplantation. In addition, the 3-year data showed no differences in kidney graft survival between recipients of kidneys from older donors compared with recipients of donor kidneys obtained through the usual standardized HLA-driven allocation procedures. If other risk factor may be avoided (eg, longer cold ischemia time, surgical damage, retransplantation), an allocation schema using kidneys from older donors for transplantation into older recipients can be successful.
Donor age is a significant risk factor for graft loss after KT. Meier-Kriesche et al [15] examined the question of whether significant graft years were being lost through transplantation of younger donor kidneys into older recipients with potentially shorter life span than the organs they receive. They evaluated, using Kaplan-Meier plots, patient and graft survival for deceased-donor KTs performed in the United States between the years 1990 and 2002. Distribution of deceased-donor kidneys was categorized by donor and recipient age. The actual and projected graft survival of transplanted kidneys from younger donors with the patient survival of transplant recipients of varying ages was calculated. As expected, the survival of grafts from younger donors significantly exceeded the patient survival of recipients older than 60 years. Consequently, the overall projected improvement in graft survival, by excluding transplantation of younger kidneys to older recipients, was approximately 3 years per transplant. Thus, avoiding the policy allocation of young donor kidneys to elderly recipients could significantly increase the overall graft life and, additionally, cost saving. Using the Schold et al [16] 5-risk strata, the propensity of transplant recipients to receive lower-quality kidneys in a cumulative logit model was evaluated in a retrospective cohort study of all deceased-donor adult renal transplant recipients in the United States from 1996 to 2002. Older patients were progressively more likely to receive lowerquality organs (age, ≥65 years, odds ratio, 2.1) compared with recipients aged 18 to 24 years. Likewise, a relatively large association between donor quality and race/ethnic group was also found. In particular, African American and Asian recipients had a greater likelihood of receiving lowerquality organs relative to non-Hispanic white recipients. Neither recipient sex nor patient's primary disease was associated with donor quality. Thus, disparities in the quality of deceased-donor kidneys among transplant recipients seem to exist among certain patient groups that have previously documented access barriers, at least in the United States. Baskin-Bey et al [17] developed a quantitative approach to measure the supply and demand of renal function on an annual basis. In addition, they assessed if the use of deceased-donor kidneys, including ECD kidneys, could be increased by an allocation system that matches donor grafts to wait-listed candidates based on similar years of expected survival. A unit referred to as a renal year (RY) was used to quantify allograft survival. An RY was defined as satisfactory function of a renal allograft for 1 year without the need for renal replacement therapy. Thus, RYs were used to quantify the supply of renal function provided by a transplanted renal allograft and the demand for renal function by a transplanted patient. In particular, annual supply of renal function was determined by the product of the number of deceased-donor kidneys transplanted in that year by the median expected recipient survival. After reviewing the records of 49 206 patients, provided by the UNOS Scientific Renal Transplant Registry, these authors
D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194 Table 3 Median transplant recipient and graft survival time and group size by deceased-donor grade and recipient group Donor grade A
B
C
Median transplant recipient survival (y) RG1 30.0 27.0 RG2 17.3 14.8 RG3 11.4 9.6 RG4 8.0 6.8 Median graft survival (y) a, c RG1 32.9 18.8 RG2 38.1 24.7 RG3 35.3 23.4 RG4 34.6 22.8
D
P
a, b
24.6 13.4 8.6 5.8
22.6 11.7 7.4 5.0
b.0001 b.0001 b.0001 b.0001
11.8 17.6 16.8 16.4
7.80 12.1 11.9 12.0
b.0001 b.0001 b.0001 b.0001
Donor grade A
B
C
Group size: deceased-donor renal transplantations, RG1 855 711 378 RG2 667 599 444 RG3 380 400 338 RG4 47 72 59 Total 1949 1782 1269
D
Total
2002 (n) 20 59 94 18 191
1964 1969 1262 196 5191
Reprinted from Am J Kidney Dis, volume 49, Baskin-Bey ES, Kremers W, Nyberg SL. A recipient risk score for deceased donor renal allocation, pages 28493, copyright 2007, with permission from Elsevier [18]. a Half-life determined by Cox model. b Not censored by graft loss. c Censored by death.
showed that matching transplant recipients and graft survival, based on a system of transplant recipient age and DDS previously described [8], could significantly decrease the gap between renal supply and demand. For patients who underwent transplant in 2002, grade A kidneys were allocated to recipients aged up to 49 years, grade B kidneys to recipients aged 40 to 59 years, grade C kidneys to recipients aged at least 50 years, and D kidneys to recipients aged at least 70 years. This reallocation process resulted in a 14% (9.483 RY) increase in supply compared with nonoptimized or actual RY supply for 2002. This optimization of donor allocation reduced the renal supply deficit by 22%. These data support the theory that marginal donors allotted to older recipients may improve the allocation process. In a more recent study, these authors elaborated an RRS by incorporating additional recipient variables shown to predict recipient survival [18]. This RRS could be used with the DDS to maximize the total number of years of renal allograft function as a means to improve donor allocations. Multivariate Cox regression models were used to derive an RRS and estimate recipient and graft survival as a function of RRS using clinical data of 47 535 adult recipients of deceased-donor renal transplants between 1995 to 2002 from the UNOS. The strongest predictors of recipient survival after KT used in the RRS were recipient age, history of diabetes mellitus, history of angina, and
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time on dialysis. Transplant recipients were stratified, using the RRS, into 4 recipient groups (RG1 to RG4), with decreasing median survival from RG1 to RG4. An optimized allocation was considered when grade A kidneys were allocated to RG1, grade B to RG2, grade C to RG3, and grade D to RG4. The benefit of this allocation was evaluated by using an RY analysis and transplant data from 2002 (Table 3). Accordingly, the demand (95.144 RY) outweighed the supply (84.180 RY) for renal function in 2002 by 10.964 RY. Thus, an allocation scheme based on matching the DDS with RG resulted in a 15% (12.532 RY) increase in supply compared with the nonoptimized or actual RY supply for 2002. Therefore, the RRS combined with a method to assess donor organs may improve donor renal allocation and, consequently, this strategy may increase the effective deceased-donor kidney supply and minimize the number of patients waiting for KT. Despite this evidence, transplantation in older donors has remained controversial because of the scarcity of donated organs and scientific doubts about the success and costeffectiveness of KT in this age group. Obviously, morbidity, mortality, and costs increase, and clinical benefits decrease in older patients and with longer waiting times for KT. Under this view, Jassal et al [19] elaborated a decision analysis model to determine the cost benefits of deceased donors KT vs hemodialysis therapy for older patients. Deceased-donor KT increased overall life expectancy for KT recipients of all ages and comorbidities. As expected, costs associated with KT and the presence of comorbidities increased. Thus, results of this analysis suggest that if a kidney becomes available within a timely period, it may offer substantial clinical benefits to older patients at a more reasonable cost.
4. Conclusion The quality of organs from deceased donors in KT represents one the most crucial factors affecting kidney graft survival. Older donors and donors with unfavorable clinical characteristics are being used more frequently in the renal transplant field. Thus, new allocation system policies are needed to match donor kidneys with recipients based on similar expected survival. Allocation systems based upon an RRS and DDS may improve outcomes after KT. Acknowledgments We thank the renal transplant team from the Canary Islands for their collaboration. References [1] Schold JD, Kaplan B, Baliga RS, et al. The broad spectrum of quality in deceased donor kidneys. Am J Transplant 2005;5:757-65.
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