More donors or more delayed graft function? A cost-effectiveness analysis of DCD kidney transplantation

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1 Clin Transplant 2013: 27: DOI: /ctr John Wiley & Sons A/S. More donors or more delayed graft function? A cost-effectiveness analysis of DCD kidney transplantation Snyder RA, Moore DR, Moore DE. More donors or more delayed graft function? A cost-effectiveness analysis of DCD kidney transplantation. Abstract: Expansion of the donor pool with expanded criteria donors and donation after cardiac death (DCD) donors is essential. DCD grafts result in increased rates of primary non-function (PNF) and delayed graft function (DGF). However, long-term patient and graft survival is similar between donation after brain death (DBD) donors and DCD donors. The aim of this study was to evaluate the cost-effectiveness of the use of DCD donors. A Markov-based decision analytic model was created to simulate outcomes for two wait list strategies: (i) wait list composed of only DBD organs and (ii) wait list combining DBD and DCD organs. Baseline values and ranges were determined from the Scientific Registry of Transplant Recipients (SRTR) database and literature review. Sensitivity analyses were conducted to test model strength and parameter variability. The wait list strategy consisting of DBD donors only provided recipients 5.4 Quality-adjusted life years (QALYs) at $65 000/ QALY, whereas a wait list strategy combining DBD + DCD donors provided recipients 6.0 QALYs at a cost of $56 000/QALY. Wait lists with DCD donors provide adequate long-term survival despite more DGF. This equates to an improvement in quality of life and decreased cost when compared to remaining on dialysis for any period of time. Rebecca A. Snyder a,b, Deonna R. Moore c and Derek E. Moore c,d a Department of Surgery, Vanderbilt University School of Medicine, b Veterans Health Administration, Tennessee Valley Healthcare System, c Division of Kidney and Pancreas Transplantation, Vanderbilt University School of Medicine and d Division of Kidney and Pancreas Transplantation, Vanderbilt University School of Medicine, Nashville, TN, USA Key words: cost-benefit analysis economics, medical kidney transplantation qualityadjusted life years tissue and organ procurement Corresponding author: Derek E. Moore, MD, MPH, Division of Kidney and Pancreas Transplantation, Vanderbilt University School of Medicine, 912 Oxford House, st Ave S., Nashville, TN 37212, USA. Tel.: ; fax: ; derek.moore@vanderbilt.edu Conflict of interest: The authors of this manuscript have no conflicts of interest to disclose. Accepted for publication 11 December 2012 Kidney transplantation is known to provide patients with end-stage renal disease with additional years of life and improved quality of life as compared to remaining on dialysis (1, 2). Over the past 10 yr, the number of candidates listed for a kidney transplant has increased by 86%, with over patients currently on the waiting list (3). Time spent on the waiting list has not changed significantly, with most patients waiting an average of 3 4 yr for a transplant (3). To address the increasing need for kidney transplants, the donor pool has been expanded with the use of expanded criteria donors (ECD) and donation after cardiac death (DCD) donors. In the United States, this includes donation from the controlled DCD donor (Maastricht category III) only. In the past decade, the use of DCD donors in the United States has increased tenfold, and DCD donors now make up 10% of all deceased donor kidney transplants (3). Transplantation of DCD kidneys results in higher rates of delayed graft function (DGF), primary non-function (PNF), and by some reports, acute rejection, likely as a result of an increased warm ischemia time awaiting organ recovery (4 9). Rates of delayed graft function reportedly range from 2.5 to 10.3 times higher for recipients of a DCD as compared to a donation after brain death (DBD) kidney transplant (4, 5, 7 9). In one study, recipients of a DCD kidney were ten times more likely to require temporary renal replacement 289

2 Snyder et al. therapy post-transplant than recipients of DBD kidneys (5). However, DGF seen in recipients of DCD kidney transplantation does not appear to result in the same long-term effects, such as worse graft function, acute rejection, or graft loss seen in patients with DGF after DBD kidney transplantation (6). Further, overall long-term graft survival and patient survival are similar among patients who undergo either DCD or DBD kidney transplant (4 11). In a system of limited healthcare resources, it becomes necessary to evaluate the cost-effectiveness of clinical practice. A number of studies have demonstrated that kidney transplantation is more cost-effective than remaining on dialysis for any period of time (12 14). A relevant study using the United Nations for Organ Sharing (UNOS) registry and Medicare data found that the average five-yr cost of transplantation was $ as compared to $ for each year spent on hemodialysis. In this analysis, transplantation became more cost-effective than hemodialysis for standard criteria donors at only 4.4 yr (14). In practice, transplant candidates awaiting a DBD or DCD kidney spend a shorter length of time on the waiting list, thereby decreasing time spent on dialysis. However, little is known about the short- or long-term financial impact of DCD kidney transplantation given the increased rates of PNF, DGF, and possibly rejection among recipients of DCD kidneys. This study was therefore designed to compare the cost-effectiveness of kidney transplantation for patients on a waiting list consisting of DBD donors alone or a wait list including both DBD and DCD donors. The decision point of interest in this model is whether the addition of DCD donors shortens waiting times on hemodialysis enough to justify the increased DGF post-transplant in DCD donors. Materials and methods Markov decision model The Markov-based decision analytic technique is used to model outcomes for groups of hypothetical patients and analyze time, value, and costs of patients in each state of health. These hypothetical patients are assigned to various health states, and outcomes for each patient group are simulated over pre-specified time intervals or cycles. Hypothetical patients can change health states when the model is cycled. The model is run either over a predetermined time horizon (such as 10 yr, as used in our model) or until all hypothetical patients have reached an absorbing state. An absorbing state is a state that the patient cannot leave once it is entered. The most common absorbing state is death. When the time horizon is limited, the model does not run until all patients reach an absorbing state but instead stops when the pre-determined time is complete. A comprehensive literature review is used to determine the likelihood that a patient will either remain in their current state or transition to a new health state within one cycle of the model. A value, most commonly in units of quality-adjusted life years (QALYs), is assigned to a patient within a health state. The costs per value (QALY) and the increases or decreases in QALYs of each health state can be computed and compared with the value of the other treatment strategies (15, 16). These values accumulate over each cycle. At the completion of all the cycles, the cost for each patient is calculated. With this method, a clinically relevant model that closely simulates the outcomes, complications, and effectiveness of kidney transplantation with DCD donors and DBD donors is created. Wait list mortality and complications on the wait list are also factored into this model. Simulated patients are moved through the model on a yearly basis for 10 yr. All outcomes, complications, costs, and QALYs gained are tracked over this 10-yr period and averaged across all the hypothetical patients. The results then demonstrate what the cost-effectiveness would be for taking care of patients who receive these two different types of organs. This allows a head-to-head comparison of patient outcomes, complications, costs, and QALYs gained between the two different donors. The Markov-based decision analytic model was constructed to simulate outcomes from a societal perspective for two different wait list strategies: (i) a wait list that limited potential recipients to receiving DBD kidneys only or (ii) a wait list that allowed potential recipients to receive either DBD or DCD kidneys. In our model, the determined cost of the type of donors was comprised of the direct costs of transplantation to the hospital or the payer, including the procedure, materials, and length of hospitalization. Indirect costs of transplantation were also included such as lost earning potential of the recipient, out of hospital expenses, and immunosuppressive therapy. To construct and run the model, we used TreeAge Pro 2010 (TreeAge Software, Inc, Williamstown, MA, USA), a software specifically designed to create and evaluate decision trees and models. The cost-effectiveness analysis was performed and reported according to the Panel on Cost-Effectiveness in Health and Medicine guidelines (17, 18). 290

3 Cost of DCD kidney transplantation Health states The Markov decision model is shown in Fig. 1. As described above, kidney transplant wait list strategies were either (i) DBD donors alone or (ii) DBD + DCD donors. In each arm of the model, potential recipients waited on the wait list and were then transplanted at different rates depending on the wait list strategy. Following kidney transplantation, the hypothetical patient was subjected to one of the following scenarios based on pre-determined probabilities from the literature: immediate graft function (IGF), delayed graft function (DGF), re-transplantation or death. Model assumptions Several assumptions were necessary in the creation of this model. All assumptions were made to create a conservative model and bias against the use of DCD kidneys. In order to generate a simplified version of kidney transplantation and the potential complications for a patient post-transplant, the hypothetical patient in the base-case scenario in this model is a 55-yr-old man with ESRD secondary to hypertensive nephrosclerosis. The DBD and DCD donors were modeled as 35 yr olds with a BMI of 30 and 12 h of cold ischemia time. All costs associated with kidney transplantation were assumed to be the same for this model for both DBD and DCD kidney transplantation with the exception that costs were increased in patients with DGF to account for post-transplant hemodialysis and inpatient stay. With PNF related to donor quality, it is assumed most centers would pursue re-transplant for these patients. Also, for the DCD donor, all donor qualities such as age and BMI, as well as procedure characteristics such as procurement procedure techniques and warm and cold ischemia time, were not varied. All re-transplanted kidneys came from DBD donors. Probability and cost data Both probabilities and rates for the baseline analysis as well as the ranges of these values for all sensitivity analyses are reported in Table 1. To determine these values, we performed a systematic review of MEDLINE/PubMed database for all reports on deceased donor kidney transplantation Fig. 1. Markov decision tree representing the choice of strategies for organ donation. The two clinical strategies to be chosen from are represented at the square decision node. The probabilities and estimates of their probabilities are listed in Table 1. = the choice between strategies (decision node); M = the point of yearly cycling of patients (Markov node); and = a logic check in the simulation (terminal node). DBD, donation after brain death; DCD, donation after cardiac death; ESRD, end-stage renal disease. 291

4 Snyder et al. Table 1. Literature-based probabilities and costs Baseline parameters Value Range References Five-yr patient survival (%) DBD CRT 82% 70 90% (4, 6, 7, 28 33) DCD CRT 80% 70 90% Probability of 10% 5 25% DCD Donor Primary nonfunction (%) DBD CRT 2% 0 5% (4, 6, 7, 28 33) DCD CRT 4% 0 8% Delayed graft function (%) DBD CRT 20% 5 30% (4, 6, 7, 28 33) DCD CRT 40% 25 50% Wait List Mortality (%) DBD List 8% 3 20% (4, 6, 7, 28 33) DBD/DCD List 5% 1 20% Utility Dialysis on 0.65 QALY QALY (34 43) Wait List CRT 0.85 QALY QALY Re-Tx 0.80 QALY QALY DGF 0.60 QALY QALY Cost Wait List Death $ $ (14, 24, 41 47) CRT $75 000/yr $ CRT with DGF $ /yr $ Indirect Cost $25 000/yr $ DBD, donation after brain death; DCD, donation after cardiac death; QALY, quality-adjusted life year; CRT, cadaveric renal transplant; Re-Tx, re-transplant; DGF, delayed graft function. and DCD kidney transplantation from 2000 to 2011, with a focus on reviews of SRTR data as well as meta-analyses. Survival was modeled as patient survival to allow QALYs to be generated. Table 1 also presents all cost estimates and ranges. Published data on specific institutional costs, the Medicare database, or similar databases were used for the cost analysis. Cost data were also obtained from published studies identified by our systematic review of the literature. The estimates of cost used in the model were abstracted from nine different studies to give the best possible representation of cost. All monetary values were adjusted for inflation to 2010 US dollars using the Consumer Price Index for medical care (19). To account for the cost of spending money now vs. in the future, health benefits and future costs were discounted at a constant rate of 3% (20). All costs were approached from a societal perspective (21). Modeling from the societal perspective allows for comparisons with other similar studies often focused on patient outcomes and costs to society. Societal costs take into account both direct costs and indirect costs. Direct hospital costs include physician services, room and board, supplies, operating room expenses, inpatient medications, and hemodialysis expenses. Indirect costs are valued based on gender- and agespecific average hourly wage rates from the Bureau of Labor Statistics. Both positive and negative cost changes resulting from an intervention into the system were considered. Furthermore, instead of interpreting the findings for a particular patient population, our findings can be interpreted for the public interest. Utilities The effectiveness of different wait list strategies was measured in terms of quality-adjusted life years. This measure of health value incorporates both quality of life and time into a composite statistic that allows for comparison between health interventions. Quality of life is determined by health utilities reported in the literature, which usually range from 0 (utility of death) to 1 (utility of perfect health). Utilities represent the reported health preferences of groups of patients who are either presently ill or may be ill in the future (Table 1) (22). Sensitivity analysis One- and two-way sensitivity analyses were performed to test the model strength based on variations in the range of values and costs reported in the literature. The ranges utilized for these analyses are described in Table 1. Multiway probabilistic sensitivity analyses using Monte Carlo methods, which change all probabilities and costs within the model simultaneously, provided additional tests of model sensitivity to changes in model parameters (23). Results Base-case analysis The model assumptions and the base-case probabilities and costs from Table 1 were used in the base-case analysis as previously described. When the model is run, the program simulates the transition of hypothetical patients through the model. The results of the base-case analysis in the Markov model are essentially the averages associated with the different outcomes of these hypothetical patients and are listed in Table 2. Recipients of DCD kidneys were modeled to have a five-yr survival of 80% vs. 82% for 292

5 Cost of DCD kidney transplantation Table 2. Costs and cost-effectiveness of organ donors Strategy Cost ($) Incremental cost ($) Effectiveness (QALY) Incremental effectiveness (QALY) Cost/effectiveness ($/QALY) DBD + DCD $ QALY $56 000/QALY DBD only $ $ QALY 0.60 QALY $65 000/QALY DBD, donation after brain death; DCD, donation after cardiac death; QALY, quality-adjusted life year. recipients of DBD kidneys. Using a 10-yr time horizon, the wait list strategy using DBD-only donors resulted in costs of $ to achieve 5.4 QALYs, or approximately $65 000/QALY. The wait list strategy employing both DBD and DCD donors resulted in costs of $ to achieve 6.0 QALYs, or approximately $56 000/QALY. Therefore, the DBD + DCD donor wait list strategy was superior to the DBD-only strategy. Sensitivity analysis Baseline probabilities and costs vary between centers and regions performing these procedures; therefore, we performed one-way and two-way sensitivity analyses to test the validity of the conclusions over a range of probabilities and costs. Fig. 2 demonstrates the results of a oneway sensitivity analysis in which the rate of wait list death in the DBD-only strategy is varied. The yearly rate of survival on the wait list was varied between 50% and 99%. The threshold value at which DBD + DCD is no longer the dominant (superior) strategy is 95%. Therefore, wait list mortality would have to be less than 5% annually before DBD only becomes the preferable strategy. A one-way sensitivity analysis was performed in which the probability of receiving a DCD transplant from the wait list is varied, as shown in Fig. 3. The threshold value at which DBD + DCD is no longer the dominant strategy is 73%. Therefore, if a center uses more than 27% DCD kidneys, the DBD-only strategy becomes preferable. Fig. 4 demonstrates the results of a one-way sensitivity analysis in which the probability of re-transplantation in the DCD strategy is varied. The threshold value at which DBD + DCD no longer dominates is 25%. Therefore, if the re-transplantation rate after DCD kidneys is over 25%, then the DBDonly wait list is the better strategy. In two-way sensitivity analyses in Fig. 5, the rates of wait list death in the DBD-only strategy and the probability of receiving a DCD transplant were simultaneously varied. The DBD-only transplant strategy becomes dominant at very low rates of DBD-only wait list death and very high rates of DCD donors in the pool. One-way and two-way sensitivity analyses were performed for a variety of ranges for costs and utilities, and the DBD + DCD wait list was the dominant strategy at all clinically relevant values. Additionally, multiway probabilistic sensitivity Fig. 2. One-way sensitivity analysis where the rate of wait list death is varied in the DBD-only strategy. When the wait list mortality is less than 5% for those awaiting DBD-only donors, it becomes the preferred strategy. DBD, donation after brain death; DCD, donation after cardiac death; CRT, cadaveric renal transplant. Fig. 3. One-way sensitivity analysis in which the probability of receiving a DCD organ is varied. The DBD-only strategy is the best strategy when the rate of DCD transplant exceeds 27%. DBD, donation after brain death; DCD, donation after cardiac death; Tx, transplant. 293

6 Snyder et al. Fig. 4. One-way sensitivity analysis in which the probability of re-transplantation after DCD transplant is varied. The DBDonly strategy would be preferable if re-transplantation after DCD exceeds 25%. DBD, donation after brain death; DCD, donation after cardiac death; Re-Tx, re-transplant; CRT, cadaveric renal transplant. Fig. 5. Two-way sensitivity analysis in which both rates of wait list death in the DBD-only strategy and probability of receiving a DCD transplant were simultaneously varied. The DBD-only transplant strategy becomes dominant at low rates of DBD wait list death and high rates of DCD transplantation. DBD, donation after brain death; DCD, donation after cardiac death; Tx, transplant; CRT, cadaveric renal transplant. analyses using Monte Carlo methods also proved DBD/DCD transplant to be the dominant strategy at all clinically relevant values. Discussion Given the rising numbers of patients waiting for a kidney transplant, the donor pool in the United States has been expanded to include DCD kidneys. However, little is known about the financial impact of including DCD kidneys in the donor pool given increased rates of delayed graft function and primary non-function. In this study, we performed a cost-effectiveness analysis which demonstrated that employing a wait list strategy including both DCD and DBD donors over a 10-yr time frame results in costs of $56 000/QALY as compared to a waiting list strategy of DBD donors alone, which results in costs of $65 000/QALY. Sensitivity analyses demonstrated that including DCD donors in the donor pool is superior to a wait list of DBD kidneys alone as long as annual wait list mortality remains 5% or greater and as long as DCD kidneys comprise fewer than 27% of the transplanted kidneys. Based on this model, our findings suggest that from a societal perspective, it is cost-effective to include DCD kidneys in the kidney transplant donor pool. Few studies have looked at the specific costs of kidney transplantation for recipients of either DBD or DCD kidneys, and there are no studies evaluating the overall societal cost-benefit of including DCD kidneys in the donor pool. To determine the cost of kidney transplantation for recipients of either ECD kidneys or non-ecd kidneys, one study merged UNOS data with Medicare data (14). ECD kidneys were defined as kidneys from donors less than five yr or 55 yr of age, with a history of hypertension, diabetes, and/or non-heart-beating status. Not surprisingly, the five-yr cost of transplantation for non-ecd recipients was $ compared to $ for ECD recipients (p < 0.001). However, given the one-yr average cost of hemodialysis of $28 666, transplantation was less expensive for both groups compared to hemodialysis over time (14). Another study calculated the initial hospital charge for patients who underwent kidney transplantation and found that the average cost for recipients of standard criteria donor (SCD) kidneys was $ compared to $ for recipients of DCD kidneys (24). The authors attributed this cost to increased need for dialysis and longer length of stay (24). Although this analysis demonstrates a significant increase in initial cost for DCD kidney transplantation, the study did not include long-term clinical follow-up such as graft function and did not calculate total cost over time. Thus, our study provides a new and informative perspective on the overall societal cost-effectiveness of expanding the donor pool to include DCD kidney transplantation. A few studies have suggested that increasing the number of DCD kidney donors may not actually expand the overall deceased donor pool, but rather simply redistribute donor types within the pool (25, 26). A study from the Netherlands 294

7 Cost of DCD kidney transplantation demonstrated that while an increase in the number of DCD kidney transplants was observed, a simultaneous decrease in the number of traditional DBD kidney transplants took place, resulting in no change in the total number of deceased donor kidney transplants over a 10-yr period (26). However, it is unclear whether this is the result of allowing earlier procurement of organs from donors who previously would have progressed to brain death had they not been selected for DCD donation. Alternatively, a study from Belgium demonstrated that the addition of DCD kidneys did not affect the number of DBD kidney transplants performed and instead enlarged the overall donor pool, resulting in more deceased donor kidney transplants (27). There are several limitations to this study. The analysis is based on the progression of a cohort of hypothetical patients through cycles of the model, which does not account for the variability in patient, donor, disease, and procedural factors seen in clinical practice. This study also does not account for cold or warm ischemia time, which is known to impact graft function. The method of preservation, cold vs. machine perfusion, was also not considered in this study. Follow-up studies are underway to look at more specific recipient and donor factors and their contribution to survival and cost. Additionally, all probabilities and rates were determined from a comprehensive literature review. Although this is the best known evidence to date, any potential bias that exists in the peerreviewed literature would thereby be incorporated into the model as well. The purpose of this study was to develop a model to determine the optimal economic strategy of kidney transplantation in the setting of limited resources and long waiting periods for patients with end-stage renal disease. As healthcare resources become increasingly limited, it is critical to understand the financial impact of treatment options in order to determine guidelines and policies that can focus efforts on optimizing patient care while minimizing costs to society. Our study suggests that despite a potential increase in shortterm complications including DGF and PNF, the addition of DCD kidneys to the donor pool is cost-effective from a societal perspective. Acknowledgements This material is based upon work supported in part by the Office of Academic Affiliations, Department of Veterans Affairs, VA National Quality Scholars Program and with use of facilities at VA Tennessee Valley Healthcare System, Nashville Tennessee. References 1. EVANS RW, MANNINEN DL, GARRISON LP Jr et al. 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