21 (2007) 189–194www.elsevier.com/locate/trre

Transplantation Reviews

Prognostic indexes in kidney procurement and allocationDomingo Hernándeza,⁎, Margarita Rufinoa, José Manuel González-Posadaa, Sara Estupiñána,

Germán Péreza, Domingo Marrero-Mirandaa, Armando Torresa, Julio PascualbaDepartment of Nephrology, Hospital Universitario de Canarias, Tenerife, Spain

bDepartment 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 graftsurvival. 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 systemsbased upon a recipient risk score and deceased-donor score may improve outcomes after KT. The aim of this review is to assess thecontribution 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 kidneytransplant (KT) represents one the most crucial factorsaffecting graft survival [1]. Indeed, there have beennumerous investigations evaluating potential donor riskfactors for graft loss in KT highlighting the importance of theorgan characteristics independent of the transplant recipients[2,3]. The expanded criteria donor (ECD) designation hasserved major importance to the transplant community bylabeling those deceased-donor kidneys with a high risk forgraft loss as well as shortening waiting times for patientswho consented to receive these organs [4,5]. Obviously, theECD definition has an importance in the allocation process,and the transplant community confronts a challenge as itstruggles to distribute deceased-donor kidneys in a mannerthat is both biologically and socially equitable. Thus, arecipient risk score (RRS) for improving deceased-donorrenal allocation is required.

Prognostic indexes involving donor characteristics areneeded to make crucial therapeutic decisions at the time oftransplant. In addition, an allocation system based upon anRRS and deceased-donor score (DDS) may improvedeceased renal allocation system and transplant outcomes.

⁎ Corresponding author. Tel.: +34 922678545; fax: +34 922678545.E-mail address: domingohernandez@gmail.com (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 contributionof comorbid conditions, affecting both donor and recipientcharacteristics, grouped in prognostic indexes on KToutcomes. Therefore, we will focus in the following issues:(1) donor score systems to estimate the quality in deceased-donor kidneys and (2) scoring systems for improvingdeceased-donor allocation.

2. Donor score systems to estimate the quality indeceased-donor kidneys

The crisis in organ supply makes up a compellingresponsibility for the transplant community to maximize theuse of organs procured from all deceased donors. As the sizeof the recipient waiting list and the number of waiting-listdeaths increase, older donors and donors with unfavorableclinical characteristics (marginal donors) are being usedmore frequently [6]. Unfortunately, the use of these marginaldonors has a high price. Indeed, the quality of organs fromdeceased donors in KT represents one of the most crucialfactors affecting graft survival. Delayed graft function(DGF), prolonged hospitalization, and graft failure havebeen associated with the use of marginal donors for KT.Subsequently, there have been numerous research articlesevaluating potential donor risk factors for graft losshighlighting the importance of the organ characteristicsindependent of the transplant recipient [2-5]. In this sense,systematic approaches to assess marginal donors have been

Table 1Donor score system for cadaveric renal transplantation

Risk factor Severity Score

Cause of death Trauma, anoxia, other 0Ischemic orhemorrhagic CVA

6

History of hypertension (y) None 01–9 3≥10 6

Donor clearance, Cockcroft-Gaultequation (mL/min)

≥90 080–89 170–79 260–69 350–59 4b50 6

Donor age (y) b50 050–54 155–59 260–64 365–69 4≥70 5

History of diabetes No 0Yes 3

Preservation time (h) b12 012–24 125–36 2N36 3

Renal artery plaque None 0Mild 1Moderate 2Severe 3

Donor score (0–32 points)

To convert values for creatinine clearance to mL/s, multiply by 0.01667.CVA indicates cerebrovascular accident. Reproduced with permission fromBlackwell Publishing [7].

able 2proved scoring system to assess adult donors for deceased renal

ansplantation

ariable Score

ge (y)b30 030–39 540–49 1050–59 1560–69 20≥70 25istory of hypertension (y)None 0Yes, duration unknown 2≤5 26–10 3N10 4reatinine clearance, mL/min≥100 075–99 250–74 3b50 4LA mismatch (no. of antigens)0 01–2 13–4 25–6 3ause of deathNon-CVA 0CVA 3otal points (range) 0–39

VA includes ischemic and hemorrhagic types. CVA indicates cerebrovas-ular accident. Reproduced with permission from Blackwell Publishing [8].

190 D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194

developed during last years to improve outcome after KT.The ECD designation has served major importance to thetransplant community by labeling those deceased-donorkidneys with a high relative risk for graft loss as well asshortening waiting times for patients who consented toreceive these organs [4]. In particular, the ECD label includesthose organs that have an associated relative risk of morethan 1.7 (excluding pediatric donations) from the modelgenerated by the Scientific Transplant Registry for theoutcome of overall graft loss. This model incorporated donorage, donor history of hypertension, donor serum creatininelevel, and donor cause of death. Although this model allowsus to know the tremendous impact of ECD on graft survivalassociated with the magnitude of hazard ratio, there is a greatvariability in the ECD organs (adjusted hazard ratio rangingfrom 1.74 to 2.69) as well as in the non-ECD donors (hazardratio ranging from 1.00 to 1.66), which translates toconsiderable expected survival differences.

Nyberg et al [7,8] have addressed 2 donor scoring systemsfor cadaveric renal transplantation using their local centerdata as well as a national database perspective to identifykidneys at highest risk of early graft dysfunction and failure.The first scoring system was based on 7 donor variablesavailable at the time of organ procurement (Table 1) [7].

Selection of variables and allocation of points were based onresults of combined univariate and multivariate analysesperformed in 90 consecutive KT recipients of a single center,whereas the scoring system was tested and validated on datafrom 151 patients who received a KT in other transplantationcenters. Thus, deceased donors were stratified on the basis ofthe following score: grade A (0–5 points), grade B (6–10points), grade C (16–32 points), and grade D (16–32 points),so that a significant decline in early renal function wasobserved with increasing donor score and grade of cadaverkidney. Obviously, this donor scoring system may be usefulto improve the allocation of marginal donors, but applicationof this system depends on institutional policies on use ofmarginal donors and reliance on cadaveric organs for KT.

Nyberg et al [8] also elaborated an improved scoringsystem to assess adult donors for cadaver KT. Their studyexamining deceased-donor kidneys from the United Net-work for Organ Sharing (UNOS) registry from 1994 to 1999provided a quantitative approach to evaluating these organs.In this scoring system were used the 4 donor variablesapplicable to the ECD designation along with humanleukocyte antigen (HLA) matching. The improved scoringwas developed from data collected 6 months after cadavertransplantation and then was applied to renal function dataobtained 12 months after KT. Consequently, a risk scale of 0

TImtr

V

A

H

C

H

C

T

Cc

191D. 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 cumulativedonor score in grade A (0–9 points), grade B (10–19 points),grade C (20–29 points), and grade D (30–39 points). Donorscore and grade of kidney showed an inverse correlation withrenal function 12 months after KT. Likewise, this donorscore had a significant influence on graft survival during the6 years after cadaver renal transplantation. Thus, theimproved scoring system may provide a proper quantitativeapproach to evaluate marginal donors and may optimizeallocation of these organs in KT.

During last years, a variety of techniques have emerged toevaluate pretransplant evaluation of donors. Machine perfu-sion (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 studiesinvolving kidneys from ECDs [9,10]. Criteria to identifypumped kidneys with high likelihood of graft failure havenot been standardized, although flow rates of less than70 mL/min or RI of more than 0.5 has been suggested inprevious studies. A recent study compared ECD status, DDS,and RI in a cohort of deceased-donor kidneys that underwentMP [11]. Deceased-donor score was superior to ECD statusand RI in its correlation with early and late renal functionafter KT. In particular, DDS identified a subgroup of patientswithout ECD (DDS ≥ 20) that functioned similarly to ECD.Furthermore, MP improved early graft function and graftsurvival, but these benefits were greatest in the group ofkidneys with DDS of at least 20. Accordingly, these authorsproposed an algorithm for the use of a combination of theDDS system and MP (Fig. 1). This algorithm of selectivepumping is evidence based and intended to identify kidneys

Fig. 1. Algorithm for use of DDS to receive machine preservation before transplancold preservation time, biopsy abnormality, concerning medical history, and hypoNyberg et al [11].

most likely to benefit from pumping and, consequently,reduce the overall cost and improve the allocation ofmarginal kidneys by matching expected graft and recipientsurvival. In any case, future investigations are needed toconfirm these aspects.

Schold et al [1] elaborated a donor kidney risk gradebased on significant donor characteristics, donor-recipientmatches, and cold ischemia time, generated directly fromtheir risk for graft loss time, to know the impact of donorquality on transplant outcomes. They created a risk indexfrom the summation of the parameter estimates that wereapplicable for individuals. These authors elegantly used acluster analysis of those variables associated with graft loss(Cox model) and examined the distribution of this indexscore, generating intervals that best defined the naturalgrouping of risk scores. Long-term graft survival wasassociated with donor grade for the cohort of patients witha minimal of 1 year overall graft survival so that theprojected half-lives for overall graft survival in recipients bydonor 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, theassociation of donor gradation was also strongly related tothe incidence of DGF. Thus, the assessment of quality ofdeceased-donor kidney might be enhanced by this scoringsystem in KT recipients.

Finally, prediction models using logistic regression andtree-based algorithms have been developed to identify riskfactors for graft survival. In particular, Goldfarb-Rumyyantzev et al [12] elaborated a prediction algorithmto identify pretransplant predictors of 3-year allograftsurvival using a large data set (UNOS) of patients withend-stage renal disease who received a cadaveric kidney or

tation. Two asterisks indicate that risk factors for graft failure include longtension or oliguria during procurement. Reproduced with permission from

192 D. Hernández et al. / Transplantation Reviews 21 (2007) 189–194

kidney-pancreas transplantation between 1990 and 1998.Donor and recipient demographic characteristics and bodymass index showed a nonlinear relationship. On thecontrary, HLA match showed strong linear relationshipswith 3-year graft survival. Likewise, prediction of prob-ability of graft survival from the model achieved a goodmatch 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 couldbe used in clinical practice to know kidney allograftsurvival, but other important postransplant variables suchas rejection and immunosuppression were not included inthe model. In addition, this model requires much time,which may limit its use in the most transplant centers.

3. Scoring system for improving deceasedrenal allocation

Currently, there is a significant gap between supply anddemand of deceased-donor renal allografts. To close this gap,the use of organs from marginal or ECD has increased atmost transplant centers during the last years. Indeed, the rateof deceased kidney donations is increasing slowly butremains disproportionate to the number of waiting-listpatients. Moreover, the current rate of donation ofdeceased-donor kidneys makes it difficult to design a kidneyallocation scheme that balances optimal utility of this scarceresource with justice. Younger renal transplant recipientsoften outlive their allografts, whereas older recipients oftendie before their grafts fail. Thus, the transplant communityfaces a challenge as it struggles to properly distributedeceased-donor kidneys in a manner that is biologicallyrational and socially equitable. Several strategies have beensuggested to improve the allocation system by decreasing thedisproportion in expected survival between transplantrecipient and their grafts, such as matching high-risktransplant recipients with high-risk renal allografts and viceversa [13].

The Eurotransplant Senior Program (ESP), which startedin 1999, was created to ensure efficient use of kidneys fromolder donors and increase transplantation in older recipients[14]. In particular, the ESP is an allocation scheme thatmatches donors with recipients of the same group, with thegoal of kidney graft outliving the recipients. After 3 years ofcollecting patient data, the ESP reported that older kidneyswere doing well if kidney graft function was not initiallydelayed immediately posttransplantation. In addition, the3-year data showed no differences in kidney graft survivalbetween recipients of kidneys from older donors comparedwith recipients of donor kidneys obtained through the usualstandardized HLA-driven allocation procedures. If other riskfactor may be avoided (eg, longer cold ischemia time,surgical damage, retransplantation), an allocation schemausing kidneys from older donors for transplantation intoolder recipients can be successful.

Donor age is a significant risk factor for graft loss afterKT. Meier-Kriesche et al [15] examined the question ofwhether significant graft years were being lost throughtransplantation of younger donor kidneys into olderrecipients with potentially shorter life span than the organsthey receive. They evaluated, using Kaplan-Meier plots,patient and graft survival for deceased-donor KTs performedin the United States between the years 1990 and 2002.Distribution of deceased-donor kidneys was categorized bydonor and recipient age. The actual and projected graftsurvival of transplanted kidneys from younger donors withthe patient survival of transplant recipients of varying ageswas calculated. As expected, the survival of grafts fromyounger donors significantly exceeded the patient survival ofrecipients older than 60 years. Consequently, the overallprojected improvement in graft survival, by excludingtransplantation of younger kidneys to older recipients, wasapproximately 3 years per transplant. Thus, avoiding thepolicy allocation of young donor kidneys to elderlyrecipients could significantly increase the overall graft lifeand, additionally, cost saving.

Using the Schold et al [16] 5-risk strata, the propensity oftransplant recipients to receive lower-quality kidneys in acumulative logit model was evaluated in a retrospectivecohort study of all deceased-donor adult renal transplantrecipients in the United States from 1996 to 2002. Olderpatients were progressively more likely to receive lower-quality organs (age, ≥65 years, odds ratio, 2.1) comparedwith recipients aged 18 to 24 years. Likewise, a relativelylarge association between donor quality and race/ethnicgroup was also found. In particular, African American andAsian recipients had a greater likelihood of receiving lower-quality organs relative to non-Hispanic white recipients.Neither recipient sex nor patient's primary disease wasassociated with donor quality. Thus, disparities in the qualityof deceased-donor kidneys among transplant recipients seemto exist among certain patient groups that have previouslydocumented access barriers, at least in the United States.

Baskin-Bey et al [17] developed a quantitative approachto measure the supply and demand of renal function on anannual basis. In addition, they assessed if the use ofdeceased-donor kidneys, including ECD kidneys, could beincreased by an allocation system that matches donor graftsto wait-listed candidates based on similar years of expectedsurvival. A unit referred to as a renal year (RY) was usedto quantify allograft survival. An RY was defined assatisfactory function of a renal allograft for 1 year withoutthe need for renal replacement therapy. Thus, RYs wereused to quantify the supply of renal function provided by atransplanted renal allograft and the demand for renalfunction by a transplanted patient. In particular, annualsupply of renal function was determined by the product ofthe number of deceased-donor kidneys transplanted in thatyear by the median expected recipient survival. Afterreviewing the records of 49206 patients, provided by theUNOS Scientific Renal Transplant Registry, these authors

Table 3Median transplant recipient and graft survival time and group size bydeceased-donor grade and recipient group

Donor grade

A B C D P

Median transplant recipient survival (y) a, b

RG1 30.0 27.0 24.6 22.6 b.0001RG2 17.3 14.8 13.4 11.7 b.0001RG3 11.4 9.6 8.6 7.4 b.0001RG4 8.0 6.8 5.8 5.0 b.0001Median graft survival (y) a, c

RG1 32.9 18.8 11.8 7.80 b.0001RG2 38.1 24.7 17.6 12.1 b.0001RG3 35.3 23.4 16.8 11.9 b.0001RG4 34.6 22.8 16.4 12.0 b.0001

Donor grade

A B C D Total

Group size: deceased-donor renal transplantations, 2002 (n)RG1 855 711 378 20 1964RG2 667 599 444 59 1969RG3 380 400 338 94 1262RG4 47 72 59 18 196Total 1949 1782 1269 191 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, pages284­93, copyright 2007, with permission from Elsevier [18].

a Half-life determined by Cox model.b Not censored by graft loss.c Censored by death.

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showed that matching transplant recipients and graftsurvival, based on a system of transplant recipient ageand DDS previously described [8], could significantlydecrease the gap between renal supply and demand. Forpatients who underwent transplant in 2002, grade Akidneys were allocated to recipients aged up to 49 years,grade B kidneys to recipients aged 40 to 59 years, grade Ckidneys to recipients aged at least 50 years, and D kidneysto recipients aged at least 70 years. This reallocationprocess resulted in a 14% (9.483 RY) increase in supplycompared with nonoptimized or actual RY supply for 2002.This optimization of donor allocation reduced the renalsupply deficit by 22%. These data support the theory thatmarginal donors allotted to older recipients may improvethe allocation process.

In a more recent study, these authors elaborated an RRSby incorporating additional recipient variables shown topredict recipient survival [18]. This RRS could be usedwith the DDS to maximize the total number of years ofrenal allograft function as a means to improve donorallocations. Multivariate Cox regression models were usedto derive an RRS and estimate recipient and graft survivalas a function of RRS using clinical data of 47535 adultrecipients of deceased-donor renal transplants between1995 to 2002 from the UNOS. The strongest predictors ofrecipient survival after KT used in the RRS were recipientage, history of diabetes mellitus, history of angina, and

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. Anoptimized allocation was considered when grade Akidneys were allocated to RG1, grade B to RG2, gradeC to RG3, and grade D to RG4. The benefit of thisallocation was evaluated by using an RY analysis andtransplant data from 2002 (Table 3). Accordingly, thedemand (95.144 RY) outweighed the supply (84.180 RY)for renal function in 2002 by 10.964 RY. Thus, anallocation scheme based on matching the DDS with RGresulted in a 15% (12.532 RY) increase in supplycompared with the nonoptimized or actual RY supply for2002. Therefore, the RRS combined with a method toassess donor organs may improve donor renal allocationand, consequently, this strategy may increase the effectivedeceased-donor kidney supply and minimize the numberof patients waiting for KT.

Despite this evidence, transplantation in older donors hasremained controversial because of the scarcity of donatedorgans and scientific doubts about the success and cost-effectiveness of KT in this age group. Obviously, morbidity,mortality, and costs increase, and clinical benefits decrease inolder patients and with longer waiting times for KT. Underthis view, Jassal et al [19] elaborated a decision analysismodel to determine the cost benefits of deceased donors KTvs hemodialysis therapy for older patients. Deceased-donorKT increased overall life expectancy for KT recipients of allages and comorbidities. As expected, costs associated withKT and the presence of comorbidities increased. Thus,results of this analysis suggest that if a kidney becomesavailable within a timely period, it may offer substantialclinical benefits to older patients at a more reasonable cost.

4. Conclusion

The quality of organs from deceased donors in KTrepresents one the most crucial factors affecting kidney graftsurvival. Older donors and donors with unfavorable clinicalcharacteristics are being used more frequently in the renaltransplant field. Thus, new allocation system policies areneeded to match donor kidneys with recipients based onsimilar expected survival. Allocation systems based upon anRRS and DDS may improve outcomes after KT.

Acknowledgments

We thank the renal transplant team from the CanaryIslands for their collaboration.

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