1. Top of page
  2. Abstract

Due to organ scarcity and wait-list mortality, transplantation of donation after cardiac death (DCD) livers has increased. However, the group of patients benefiting from DCD liver transplantation is unknown. We studied the comparative effectiveness of DCD versus donation after brain death (DBD) liver transplantation. A Markov model was constructed to compare undergoing DCD transplantation with remaining on the wait-list until death or DBD liver transplantation. Differences in life years, quality-adjusted life years (QALYs), and costs according to candidate Model for End-Stage Liver Disease (MELD) score were considered. A separate model for hepatocellular carcinoma (HCC) patients with and without MELD exception points was constructed. For patients with a MELD score <15, DCD transplantation resulted in greater costs and reduced effectiveness. Patients with a MELD score of 15 to 20 experienced an improvement in effectiveness (0.07 QALYs) with DCD liver transplantation, but the incremental cost-effectiveness ratio (ICER) was >$2,000,000/QALY. Patients with MELD scores of 21 to 30 (0.25 QALYs) and >30 (0.83 QALYs) also benefited from DCD transplantation with ICERs of $478,222/QALY and $120,144/QALY, respectively. Sensitivity analyses demonstrated stable results for MELD scores <15 and >20, but the preferred strategy for the MELD 15 to 20 category was uncertain. DCD transplantation was associated with increased costs and reduced survival for HCC patients with exception points but led to improved survival (0.26 QALYs) at a cost of $392,067/QALY for patients without exception points. In conclusion, DCD liver transplantation results in inferior survival for patients with a MELD score <15 and HCC patients receiving MELD exception points, but provides a survival benefit to patients with a MELD score >20 and to HCC patients without MELD exception points. Liver Transpl,18:630–640, 2012. © 2012 AASLD.

Organ scarcity remains a major challenge in the field of liver transplantation. Each year in the United States, more than 2000 patients with end-stage liver disease (ESLD) die on the wait list.1 Donation after brain death (DBD) donors are currently the largest source of liver grafts. However, brain death represents only a small fraction of all-cause mortality,2, 3 whereas cardiovascular death is the largest cause of mortality and could vastly expand the overall pool of donor organs.2, 4

Federal mandates from the Health Resources and Services Administration and the Centers for Medicare and Medicaid Services5 have been successful in increasing the utilization of donation after cardiac death (DCD) livers.1 However, outcomes after DCD liver transplantation are marred by higher complication rates, inferior survival, and higher costs in comparison with DBD liver transplantation.6-12

Previous studies have failed to identify which group of patients with ESLD might benefit from undergoing transplantation with DCD grafts instead of remaining on the wait list. In an analysis based on donor quality [measured with the donor risk index (DRI) score13] and patient disease severity [measured with the Model for End-Stage Liver Disease (MELD) score], a survival benefit was noted when patients with a MELD score >20 were given a high-risk graft (DRI >1.65).14 However, this analysis failed to distinguish between different types of high-risk grafts, account for quality-of-life differences related to the high complication and retransplantation rates, or consider the impact on costs.

This study considers both the costs and quality-of-life implications after transplantation as well as outcomes beyond 1-year survival to provide a more detailed understanding of the comparative effectiveness of DCD liver transplantation.


  1. Top of page
  2. Abstract

Decision Analytical Model

A Markov model was used to compare 2 treatment strategies available to patients with ESLD: (1) undergoing transplantation with a DCD liver and (2) remaining on the wait list with the possibility of receiving a DBD liver according to the standard MELD-based allocation scheme. The model was based on a patient/transplant center perspective and was intended to identify the optimal choice for an individual patient according to his or her MELD score. The MELD score is a validated predictor of 90-day wait-list mortality (c statistic = 0.83).15, 16 Consequently, the MELD score has been used to establish priority for liver transplantation since 2002; it does not consider the impact of organ acceptance decisions on the overall wait-list cohort but focuses only on maximizing the outcomes of individual patients. In our model, we included health states representing patients on the wait list according to the MELD score and posttransplant patients who (1) had a functioning graft, (2) developed acute or chronic biliary complications, (3) suffered all-cause graft failure that resulted in retransplantation, or (4) died. Costs, life years, and quality-adjusted life years (QALYs) were calculated. The cycle length for the model was 1 month. A 10-year time horizon was chosen because of the limitations of the available survival data probabilities. Half-cycle corrections and discounts for both costs and utilities (3%) were included. Figure 1 is a graphic illustration of the health states and transitions represented in the model. Incremental cost-effectiveness ratios (ICERs), which were based on the differences in costs between the 2 treatment strategies divided by the differences in effectiveness (measured in QALYs), are reported.

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Figure 1. Health states and transitions represented in the Markov model. This figure presents a simplified schematic of the health states and possible transitions in the model. Each MELD quintile is actually a discrete health state. All depicted health states include a possible transition to death in the constructed model.

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Patient Population

A 50-year-old Caucasian male with ESLD was used as the index patient. Patients with fulminant liver failure were excluded, and patients were assumed to not have undergone transplantation previously. Patients were grouped and analyzed by MELD quintiles (<11, 11-14, 15-20, 21-30, and >30). Patients with hepatocellular carcinoma (HCC), including subgroups of patients with and without MELD exception points, were considered in a separate analysis. Additionally, this analysis was focused on those individuals without a living donor option. These patients represent the majority of patients because more than 96% of liver transplants are performed with organs from deceased donors.1

Model Inputs

Wait-List Probabilities

The probabilities of DBD transplantation, death on the wait list, and disease progression (according to the MELD score) were based on national data available from Table 9.2b of the 2007 Scientific Registry of Transplant Recipients (SRTR) annual report.1 These probabilities are presented in Table 1. The 30-day probability of receiving a DBD transplant according to the MELD score was calculated for each region from monthly event counts for 2007-2009, which were provided by the United Network for Organ Sharing (UNOS).

Table 1. Wait-List Probabilities Used in the Model
Starting MELD ScoreDBD TransplantationDeathMELD Progression
  1. NOTE: The probabilities are taken from the 2007 SRTR annual report.1 All probabilities are 30-day probabilities.

Posttransplant Probabilities

The risks of posttransplant complications, including early graft failure secondary to primary nonfunction, vascular complications, and biliary complications, were incorporated. Acute, resolvable biliary complications and chronic, unremitting ischemic cholangiopathy (IC) were included in the model. Acute biliary complications include biliary leaks and anastomotic strictures, which often resolve after 1 or more therapeutic endoscopic, percutaneous, or operative procedures. IC is characterized by the diffuse, intrahepatic formation of strictures of a more chronic nature and is associated with a higher rate of retransplantation. IC has been demonstrated to result in significant health care costs and to affect the quality of life.7, 10-12, 17 The probabilities of acute biliary complications and IC were derived from data published in a meta-analysis of 11 single-institution studies.18 These complications are not adequately reported to the SRTR national registry; as such, this represents the most comprehensive analysis currently available. Pooled odds ratios (ORs) from this meta-analysis were used to reflect the increased risk of complications for DCD transplants.18

The probabilities of death after transplantation and graft failure requiring retransplantation were based on national SRTR standard analysis files. Posttransplant patient survival and retransplantation rates were estimated with life table survival analyses based on the SRTR data set for DCD liver transplants (n = 1113) and DBD liver transplants (n = 42,254) performed between April 1, 1996 and August 1, 2008.19 Pediatric patients (<18 years old), patients receiving multiorgan transplants (except for simultaneous liver-kidney transplants), patients not undergoing primary transplantation, and patients with no follow-up (297 or 0.7%) were excluded. SRTR data were also used to analyze survival after retransplantation (n = 5153). Patients were censored at the date of their last known follow-up. Only 1 retransplant per patient was represented in the model. Actuarial survival based on age-, sex-, and race-specific estimates2 was subtracted from posttransplant survival probabilities and was modeled separately from disease-specific survival.20 Baseline estimates for posttransplant probabilities are listed in Table 2. Survival analyses were performed with Stata SE 10 (Stata Corp., College Station, TX).

Table 2. Posttransplant Event Probabilities
  • *

    The values in parentheses are 95% confidence intervals.

  • The probabilities used in the model are based on monthly transitions from a time-to-event survival analysis of the SRTR data set. The 1- and 3-year survival and overall retransplantation rates are provided here to enable comparisons with previously published literature.

DCD patient survival (%)
 1 year82.4 (80.1-84.6)SRTR (4/1/96-8/1/08): n = 1113 for DCD and n = 42,254 for DBD79.7Abt et al.4 (2004)
 3 years71.2 (68.1-74.1)72.1Abt et al.4 (2004)
DBD patient survival (%)
 1 year85.9 (85.5-86.2)SRTR (4/1/96-8/1/08): n = 1113 for DCD and n = 42,254 for DBD85.0Abt et al.4 (2004)
 3 years77.4 (77.0-77.8)77.4Abt et al.4 (2004)
Retransplant patient survival (%)
 1 year67.6 (66.1-68.9)SRTR (4/1/96-8/1/08): n = 515366.9UNOS/OPTN57 (2008)
 3 years59.0 (57.5-60.5)55.5UNOS/OPTN57 (2008)
Acute biliary complications
 DBD (%)11.7 ± 0.8Meta-analysis by Jay et al.18: n = 275 for DCD and n = 1627 for DBD  
 DCD (OR)1.03 (0.52-2.04)  
 DBD (%)3.5 ± 0.3Meta-analysis by Jay et al.18: n = 288 for DCD and n = 1725 for DBD  
 DCD (OR)12.52 (4.50-34.84)  
Retransplantation (%)
 DBD6.8 ± 0.1SRTR (4/1/96-8/1/08): n = 1113 for DCD and n = 42,254 for DBD5.4Selck et al.9 (2008)
 DCD14.7 ± 1.113.6Selck et al.9 (2008)

All costs in this analysis were based on direct medical care costs. Inpatient and outpatient costs that accrued during the analysis period were included, but costs related to lost wages or other societal costs were not included. Costs for hospitalizations, procedures, medications, and follow-up were drawn from the available literature and are presented in Table 3. Organ acquisition costs were not included. All costs were adjusted to 2008 dollars with the consumer price index for medical care/services from the US Bureau of Labor Statistics.

Table 3. Costs and Utilities Used in the Model
CostsBase ($)Range ($)Reference
  • NOTE: All costs are reported in 2008 adjusted US dollars.

  • *

    Incremental costs are applied in addition to base transplant costs.

  • Incremental costs of DCD transplants are based on costs identified for livers with a DRI > 1.8.17

  • Except for postoperative disutility, utilities are reported on a scale of 0 to 1, with 0 representing no health or death and 1 representing perfect health.

  • §

    Applied to the initial 30-day postoperative period.

Base transplant costs (includes 30-day posttransplant period)90,03566,515-113,556Salvalaggio et al.,17 Northup et al.,21 Axelrod et al.,22 Bennett et al.,23 Huang et al.,24 and Lin et al.25
Incremental transplant costs*  Salvalaggio et al.17 and Axelrod et al.22
 MELD score   
  <15 (reference)   
Monthly wait-list costs (visits and labs)18091-360Northup et al.,21 Bennett et al.,23 and Lin et al.25
Annual incremental pretransplant costs according to MELD score  Buchanan et al.26
 <15 (reference)   
Annual health care costs for HCC patients31,74015,870-63,480Lang et al.27
Monthly posttransplant outpatient follow-up costs (visits and labs)306153-612Bennett et al.23 and Lin et al.25
Annual costs of transplant medications18,8484294-37,574Bennett et al.23 and Shenoy et al.28
90-day costs of biliary complications15,8104737-34,606Northup et al.21 and Ammori et al.29
Annual IC costs (readmissions, ERCP/PTC, and medications)78,68933,520-123,859Jay et al.11 and Ammori et al.29
Costs of cirrhotic death42,29721,148-84,656Lin et al.25
Costs of noncirrhotic death54291086-10,858Huang et al.24
Wait list/ESLD according to MELD score  Northup et al.,21 Bennett et al.,23 Bryce et al.,30 McLernon et al.,31 Sagmeister et al.,32 Shechter et al.,33 and Younossi et al.34
After transplantation0.830.62-0.87Northup et al.,21 Bennett et al.,23 McLernon et al.,31 Sagmeister et al.,32 and Shechter et al.33
Postoperative disutility (%)§50 Northup et al.21
Biliary complications/IC0.710.39-0.81Younossi et al.,34 Bondini et al.,35 Kim et al.,36 Longworth et al.,37 Olsson et al.,38 and Parikh et al.39

Table 3 also lists the utility values and ranges for the various health states represented in the model. A 1-month utility toll was applied when patients underwent transplantation. Currently, no utility examining patients with biliary complications is available. The utility used in the model for this health state was based on a recent analysis of quality-of-life data from a small group of patients with IC and from studies evaluating patients with cholestatic liver disease who experienced symptoms of jaundice, pruritus, and cholangitis. All utilities represented in the model were summary estimates based on the available literature.

Sensitivity Analysis

A probabilistic sensitivity analysis was conducted for each initial MELD state with a Monte Carlo second-order simulation based on the overall distributions for all parameter estimates (probabilities, costs, and utilities). Differences in graft quality between DCD and DBD grafts were evaluated in the Monte Carlo simulations. One thousand Monte Carlo iterations were performed, and new random values were selected for each parameter according to the distributions of these parameters.

A 1-way sensitivity analysis (varying single parameters individually) was used to identify threshold values for key variables, including the probability of a wait-listed patient receiving a DBD transplant. We examined regional variations in the probability of DBD transplantation according to the MELD score. UNOS data on transplant event counts and candidate registrations from January 1, 2003 to December 31, 2009 were used to determine average 30-day probabilities. Finally, an expected value of perfect information (EVPI) analysis was used to further examine uncertainty in the model. TreeAge Pro 2009 decision analysis software (version, TreeAge Software, Williamstown, MA) was used for model development and analysis. This study was approved for exemption by the institutional review board of the Feinberg School of Medicine at Northwestern University.


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  2. Abstract

Cost-effectiveness results for the index case analyses according to the MELD scores are summarized in Table 4. For patients with a MELD score <15, DCD transplantation was associated with both reduced effectiveness and greater costs in comparison with remaining on the wait list. Patients with a MELD score of 15 to 20 had a small improvement in effectiveness with DCD liver transplantation (4.43 QALYs for DCD versus 4.36 QALYs for DBD). However, this increase (0.07 QALYs) was associated with an increased cost of $150,000 ($357,000 for DCD versus $207,000 for DBD), which resulted in an ICER >$2,000,000/QALY. Patients with a MELD score of 21 to 30 or a MELD score >30 also derived benefits from DCD transplantation (0.25 and 0.83 QALYs, respectively) with ICERs of $478,222/QALY and $120,144/QALY, respectively. For HCC patients, DCD transplantation led to increased costs and decreased survival (see Table 4). However, when HCC patients were stratified into those receiving MELD exception points and those not receiving them, patients without exception points derived a survival benefit (0.26 QALYs) from DCD transplantation with an ICER of $392,067/QALY.

Table 4. Incremental Cost-Effectiveness Results for the Base Case Analysis
StrategyCost ($)Incremental Cost ($)Effectiveness (QALY)Incremental Effectiveness (QALY)Incremental Net Monetary Benefit ($)ICER ($/QALY)
MELD score < 11      
 Staying on the wait list166,000 4.86   
 DCD transplantation357,000191,0004.43−0.43−233,000Dominated
MELD score = 11-14      
 Staying on the wait list180,000 4.66   
 DCD transplantation357,000177,0004.43−0.23−201,000Dominated
MELD score = 15-20      
 Staying on the wait list207,000 4.36   
 DCD transplantation357,000150,0004.430.07−144,0002,587,046
MELD score = 21-30      
 Staying on the wait list239,000 4.18   
 DCD transplantation357,000118,0004.430.25−93,000478,222
MELD score > 30      
 Staying on the wait list257,000 3.59   
 DCD transplantation357,000100,0004.420.83−17,000120,144
All HCC patients      
 Staying on the wait list223,000 3.84   
 DCD transplantation334,000111,0003.79−0.05−116,000Dominated
HCC patients with exceptions      
 Staying on the wait list262,000 3.93   
 DCD transplantation334,00072,0003.79−0.14−86,000Dominated
HCC patients without exceptions      
 Staying on the wait list234,000 3.53   
 DCD transplantation334,000100,0003.790.26−74,000392,067

Probabilistic Sensitivity Analysis

A probabilistic sensitivity analysis was performed for each MELD quintile. For patients with a MELD score <11 or a MELD score of 11 to 14, DCD transplantation was dominated (increased costs and reduced QALYs) in 85% and 76% of the 1000 iterations, respectively. For patients with a MELD score of 15 to 20, DCD liver transplantation was associated with increased QALYs in only 56% of the iterations, but again the ICER value was <$100,000/QALY in only 0.6% of iterations. Increased effectiveness was observed in 82% of the iterations with DCD transplantation for patients with a MELD score of 21 to 30, and an ICER <$100,000/QALY was present for 12% of the iterations. Finally, in patients with a MELD score >30, increased QALYs were identified in 98% of the cases, and an ICER <$100,000/QALY was identified in 55%. An ICER <$50,000/QALY was identified in 23% of the iterations.

One-Way Sensitivity Analyses

The impact on effectiveness imparted by the monthly probability of receiving a DBD liver and the monthly probability of dying on the wait list according to the MELD score was examined with 1-way sensitivity analyses. In the cohort of patients with a MELD score >30, DCD transplantation was associated with more QALYs unless the monthly probability of receiving a DBD transplant exceeded 76% (mean national probability = 48%1). For the cohort of patients with a MELD score of 21 to 30, staying on the wait list became the more effective strategy when the monthly probability of DBD transplantation exceeded 32% (mean national probability = 19%1). The regional probabilities varied from 7% (region 1) to 26% (region 9; Fig. 2). Finally, a monthly probability of DBD transplantation greater than 8% in patients with a MELD score of 15 to 20 was necessary for DCD transplantation to be the preferred strategy (mean national probability = 5%1).

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Figure 2. One-way sensitivity analyses of the probability of receiving a DBD transplant while a patient is on the waiting list: (A) a MELD score of 21 to 30, (B) a MELD score > 30, and (C) HCC without exception points. This figure presents the average effectiveness (measured in QALYs) of undergoing DCD transplantation versus remaining on the wait list and possibly subsequently undergoing DBD transplantation. The dashed lines represent the threshold probability at which staying on the wait list (rather than undergoing DCD transplantation) was associated with increased QALYs. From left to right, the solid, vertical lines represent the lowest probability region, the average national probability, and the highest probability region for the MELD groups. These graphs demonstrate that all these probabilities remained below the threshold, but they also show the proximity of the high-probability DBD transplant regions to the threshold value at which waiting for a DBD transplant was associated with improved survival. Regional probabilities were not available for HCC patients. The average national probability for DBD transplantation is shown.

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Figure 3 displays UNOS data–based regional 30-day probabilities of patients receiving a DBD liver transplant with a MELD score >20. There are substantial regional variations. Region 1 had the lowest probability of DBD liver transplantation (9.6% ± 3.5%), whereas region 3 exhibited the highest probability (43.9% ± 7.0%).

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Figure 3. Thirty-day probabilities of receiving a DBD transplant for patients with a MELD score >20 across the 11 UNOS regions. The means and standard deviations are reported for the individual regions.

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Additionally, we compared the treatment strategies after we excluded quality adjustments by removing the contributions of utilities. Excluding the utilities from our analysis produced results similar to those of our quality-adjusted analysis. The increased effectiveness and ICER for patients with a MELD score >30 was 1.56 life years and $64,000/life year, respectively. The groups with MELD scores of 15 to 20 and 21 to 30 were associated with ICERs of $385,000/life year and $177,000/life year, respectively, and DCD transplantation was dominated in the remaining MELD quintiles. Finally, we looked at the differences in the ICERs over shorter and longer time horizons and found minimal variability. For instance, in the group with MELD scores >30, we observed an ICER of $106,000/QALY at 5 years and an ICER of $130,000/QALY at 20 years.


Finally, we determined the value of more precise information on the probabilities, costs, and utility estimates employed in the model. The EVPI represents the expected cost of uncertainty or expected opportunity lost because of a lack of precise estimates of probabilities, costs, and utilities. Unlike a conventional sensitivity analysis, it incorporates both the probability that a decision may be wrong and the consequences of a wrong decision. Consequences are measured in terms of incremental net benefits, which are equal to incremental life years at $100,000/QALY minus incremental costs. Incremental net benefits are shown in Table 4. The EVPIs for all probabilities, costs, and utilities in our model ranged from a high of $21,890 for the group with MELD scores >30 to values of $2670 and $930 for the groups with MELD scores of 21 to 30 and 15 to 20, respectively. These values are insignificant in comparison with the tens to hundreds of thousands of dollars of incremental net benefits shown in Table 4, and this indicates that even the acquisition of perfect information on probabilities, costs, and utilities would be unlikely to alter the choice between immediately undergoing DCD transplantation and remaining on the wait list. In short, there is a clear choice in terms of incremental net benefits.


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  2. Abstract

Because of the dramatic growth in health care costs and the current national debate on value-based health care, a comparative effectiveness focus has become increasingly prominent. The 2009 Institute of Medicine report identified comparative effectiveness research as a top priority.40 Comparative effectiveness research “provides an opportunity to improve the quality and outcomes of healthcare by providing more and better information to support decisions by the public, patients, caregivers, clinicians, purchasers, and policy makers.”40 Moreover, cost-effectiveness analysis enables decision makers to allocate scarce resources in a manner that can maximize the overall health benefit for society.41 Historically, an ICER less than $50,000/QALY has been considered cost-effective according to a benchmark article examining the costs of chronic hemodialysis.42, 43 This study, if it were adjusted according to the recent consumer price index, would yield a willingness-to-pay threshold greater than $75,000 to $100,000/QALY.

This analysis is the first attempt to evaluate the comparative effectiveness of DCD liver transplantation. Nowhere is the issue of resource scarcity more evident than in the field of organ transplantation. The increasing use of DCD livers is intended to mitigate organ scarcity, but evidence of its effectiveness has not been rigorously assessed. Concerns about the increasing use of DCD liver transplantation have risen with the publication of multiple studies demonstrating that these grafts have been associated with higher complication rates, inferior graft survival, and higher costs in comparison with DBD liver transplantation.6-11, 17 Most notable is the markedly increased incidence of IC, a chronic diffuse biliary disorder, which has been identified in 14% to 50% of DCD recipients (versus 1%-2% of DBD recipients).6, 7, 10, 44, 45 Patients with IC are afflicted by jaundice, disabling pruritus, and cholangitis; this results in frequent readmissions and invasive biliary tract procedures with considerable quality-of-life implications.10 Moreover, DCD recipients require retransplantation more than twice as often, and this leads to greatly increased costs. Finally, recent declines in DBD donation in the setting of overall gains in DCD donation in the past decade have raised additional concerns about the potential cannibalization of DBD grafts.1, 3, 46 As such, the appropriate utilization of these grafts is of the utmost concern.

This study demonstrates that for patients with a MELD score < 15, DCD transplantation is associated with both higher costs and reduced effectiveness in comparison with remaining on the wait list. For patients with a MELD score >20, there was an increase in effectiveness after DCD transplantation in essentially all iterations performed in the Monte Carlo sensitivity analysis. The QALY benefit for patients with a MELD score of 15 to 20 remains much less clear, with 56% of iterations demonstrating a benefit from DCD transplantation. These findings confirm previous research by Merion et al.,47 who demonstrated a survival benefit from undergoing liver transplantation versus remaining on the wait list for patients with a MELD score >15. Subsequently, Schaubel at al.14 established that only patients with a MELD score >20 derived a benefit from transplantation with high-DRI organs when donor quality was added to the equation.

However, according to recent practices, DCD transplantation was not a cost-effective strategy for any MELD group that was studied. Patients with a MELD score >30 had the lowest ICER value at $120,144/QALY. The additional consideration of health care costs can be a highly controversial topic because it goes beyond making health care decisions solely on the basis of maximizing outcomes. Indeed, cost-effectiveness is not the focus of physicians when they are advising patients. However, because of the limited liver organ resources available, these results are relevant for policy makers when they are establishing allocation and resource guidelines.

Additionally, the impact of local and regional variations on the probability of patients receiving a transplant while they are on the wait list is an important topic. We identified a substantial amount of variation when the probabilities of receiving a transplant were examined across the 11 UNOS regions, with some regions having a 30-day DBD transplant probability for patients with a MELD score >20 that was more than 4-fold higher than the probability in other regions. Although the examination of such variations is informative, it is limited in its ability to convey accurately the true variations occurring at the donor service area (DSA) and transplant center levels. According to UNOS data, the median time to transplantation varies widely from one DSA to the next within a region. For example, in region 11, the median time to transplantation ranged from 0.5 months in one DSA to >60 months in another.48 However, there are insufficient data in the registry because many DSAs record neither transplant events nor wait-listed patients (particularly among the higher MELD strata), and this makes it impossible to derive accurate 30-day transplant probabilities according to DSAs even when many years of data are aggregated.

To address the impact of the variations in DBD availability in our model, 1-way sensitivity analyses evaluating the probability of receiving a DBD transplant were performed. For patients with a MELD score >30, a 30-day probability of DBD transplantation greater than 76% (versus the national estimate of 48%) was necessary for worse survival with DCD transplantation. Similar differences in the probabilities of receiving a DBD transplant were identified for the other MELD quintiles. The threshold probability was again larger than the national estimate for MELD scores of 21 to 30 (32% versus 19%) and MELD scores of 15 to 20 (8% versus 5%). These data highlight the impact of the contextual environment on decision making by clinicians.

Similarly, we examined the impact of the recipient's age at transplantation by varying the survival probabilities for patients who were 30 to 70 years old (data not shown). This yielded no differences in which strategy was more effective for a given MELD range or relative cost-effectiveness. Additionally, we examined separate cohorts of HCC and hepatitis C virus (HCV)–positive candidates. DCD was the inferior strategy for HCC patients overall and for those patients with exception points specifically and resulted in increased costs and decreased survival. However, HCC patients not receiving exception points did have a survival benefit with DCD transplantation at an ICER of $392,067/QALY. The HCV status had no impact on the model output, and the results were similar to those from the MELD-based analysis of ESLD patients as a whole. These findings are consistent with other recent studies examining HCV.49-52


There are several limitations to this analysis. First, the accuracy of the model output was directly dependent on the ability to estimate the true probabilities, costs, and utilities. A strength of this article is that all model probabilities were derived from national registry data and a meta-analysis, and they did not rely on the results of single institutions. To address questions related to the potential variability or inaccuracy of model parameters, probabilistic sensitivity analyses were performed with all model parameters varied simultaneously according to their distributions. These analyses demonstrated the significant stability of the findings for the upper and lower MELD quintiles. However, for patients with a MELD score of 15 to 20, there was more uncertainty in whether DCD transplantation resulted in an improvement in QALYs according to the probabilistic sensitivity analysis. Moreover, the extremely low EVPI for all probabilities, costs, and utilities indicates that resolving the uncertainty in these variables would be unlikely to change the conclusions of the study.

Similarly, the validity of utilities has been challenged because of concerns about participants' abilities to accurately understand and assess the hypothetical situations proposed in typical gamble and time-tradeoff scenarios. No utilities were available in the literature for patients with biliary complications after transplantation. The utility used in this model was based on a quality-of-life assessment of a small cohort of DCD recipients with IC. According to the quality-of-life data collected from this small group of patients, the calculated utility was 0.55. Additionally, we included the range of utilities derived from studies of patients with native biliary tract disease. Ultimately, we chose a more conservative estimate of 0.71 for the utility associated with biliary complications; this provided a relative advantage and potential bias in favor of DCD transplantation. However, the lower utility estimate of 0.55 was included in the larger range used in our probabilistic sensitivity analyses. Additionally, we examined the impact of the biliary complication utility in a 1-way sensitivity analysis. For the group with MELD scores >30, DCD transplantation was associated with increased effectiveness, with ICERs ranging from $107,000/QALY to $191,000/QALY. For the groups with MELD scores of 15 to 20 and 21 to 30, DCD transplantation became dominated (ie, it was a more costly and less effective strategy) with biliary complication utility values less than 0.65 and 0.46, respectively. Finally, we also examined the pure effectiveness of the treatment strategies in the absence of quality adjustments; this rendered results similar to those of our base case analysis.

Next, we examined regional variations in wait-list probabilities. However, an additional limitation involves the impact on the wait list with respect to increases or decreases in the number of available DCD livers. Augmenting the number of DCD liver transplants nationwide would potentially have an impact because patients would be removed from the list at a faster rate; consequently, the probability of receiving a DBD transplant with a lower MELD score would improve. More recently, however, because of concerns about the worse outcomes of DCD livers, there has been a nearly 10% decline in DCD liver utilization in the United States53 despite an overall increase in the supply of DCD livers during the last decade.1 An additional benefit from DCD transplantation may have been observed if the impact of increasing the supply of available grafts on the wait list as a whole had been considered in our model. Allocation policy reforms to address those patients disadvantaged with a failing DCD graft because of IC may be necessary to remedy this decay in DCD liver utilization.54, 55

Another limitation of this analysis is the exclusion of organ acquisition costs, which can differ greatly between DCD and DBD graft types. The costs of an individual donation can be greatly reduced by the avoidance of the time-consuming and expensive tests and treatments needed to allow a patient to progress to brain death, and this seems to favor DCD donation. However, this fails to take into account the higher nonrecovery and discard rates that have been identified for DCD donors.1 These factors result in more frequent trips by organ procurement teams in which organs are not obtained for transplantation and in higher standard acquisition costs for livers. Further research is needed to elucidate organ acquisition cost differences. Similarly, indirect costs, including lost wages and other societal costs, have not been included. Because of the higher complication rates associated with DCD liver transplantation, the inclusion of these costs would likely result in even more unfavorable ICER values for DCD transplants.

Additionally, by choosing a 10-year time horizon, we potentially minimized gains associated with prolonged survival after transplantation. However, this base time horizon was selected because of concerns about the ability to extrapolate survival data beyond this time period, especially in light of the significant evolution in transplant practices. We did further confirm the consistency of our findings by evaluating differences in the ICER according to 5-, 10-, and 20-year time horizons.

Finally, living donor liver transplantation was not considered as a therapeutic alternative in this model. Recent research has suggested that living donation represents a cost-effective option in comparison with medical management ($36,000/QALY) and provides a survival benefit but at a cost of $107,000/QALY in comparison with standard deceased donor liver transplantation.21 DCD liver transplantation has been increasingly considered for those recipients lacking an available living donor.21, 56 By focusing our study on those individuals without a viable living donor option, we aimed to address the relevant question facing the majority of patients because living donor transplantation is still performed only for a small number of liver failure patients (currently ∼3.5% of annual liver transplants).1

In conclusion, for patients with a MELD score < 15, DCD liver transplantation was associated with reduced effectiveness and greater costs in comparison with remaining on the wait list. Patients with a MELD score >20 had higher QALYs with DCD liver transplantation, but improvements in QALYs for patients with a MELD score of 15 to 20 were not stable when the model parameters were varied. For HCC patients with exception points, DCD transplantation was dominated and resulted in increased costs and decreased survival. However, a survival advantage was seen with DCD transplantation for HCC patients not receiving exception points. Patients with a MELD score >30 had the lowest ICER value at $120,144/QALY. Because of the current focus on comparative effectiveness, the lack of cost-effectiveness of DCD liver transplantation according to recent practices needs to be considered in making decisions about the utilization and allocation of DCD livers.


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