Influence of kidney offer acceptance behavior on metrics of allocation efficiency

Cytomegalovirus Matching in Deceased Donor Kidney Allocation: Results From a U.S. National Simulation Model

Background

Cytomegalovirus (CMV) infects >60% of adults and can pose an independent risk factor for allograft loss and mortality in solid organ transplant recipients. The purpose of this study is to evaluate the impact of a nationwide implementation of CMV seromatching (donor/recipient: D-/R- and D+/R+) in the U.S. deceased donor kidney allocation system (KAS).

Methods

Adult candidates on the U.S. kidney-only transplant waiting list and deceased donor kidneys offered to the U.S. transplant centers were considered. A discrete-event simulation model, simulating the pre-COVID-19 period from January 1, 2015, to January 1, 2018, was used to compare the performances of currently employed KAS-250 policy (without CMV matching) to various simulated CMV matching policies parameterized by calculated panel reactive antibody exception threshold. Outcomes included CMV serodistribution, waiting time, access to transplantation among various groups, transplant rate, graft survival, kidney discard rate, and antigen-mismatch distribution, stratified by CMV serostatus.

Results

CMV matching policy with a calculated panel reactive antibody exception threshold of 50% (namely, the CMV">50%" policy) strikes a better balance between benefits and drawbacks of CMV matching. Compared with KAS-250, CMV">50%" reduced CMV high-risk (D+/R-) transplants (6.1% versus 18.1%) and increased CMV low-risk (D-/R-) transplants (27.2% versus 13.1%); increased transplant rate for CMV R- patients (11.54 versus 12.57) but decreased for R+ patients (10.68 versus 10.48), yielding an increase in aggregate (11.09 versus 10.94); and reduced mean time to transplantation (by 6 wk); and reduced kidney discard rate (25.7% versus 26.2%).

Conclusions

Our findings underscore the feasibility and potential advantages of a nationwide CMV seromatching policy in kidney transplantation.

Cold Ischemia Time, Kidney Donor Profile Index, and Kidney Transplant Outcomes: A Cohort Study

Rationale & Objective

An average of 3,280 recovered deceased donor kidneys are discarded annually in the United States. Increased cold ischemia time is associated with an increased rate of organ decline and subsequent discard. Here we examined the effect of prolonged cold ischemia time on kidney transplant outcomes.

Study Design

Retrospective observational study.

Setting & Participants

Recipients of deceased donor kidney transplants in the United States from 2000 to 2018.

Exposure

Recipients of deceased donor kidneys were divided based on documented cold ischemia time: ≤16, 16-24, 24-32, 32-40, and >40 hours.

Outcomes

The incidence of delayed graft function, primary nonfunction, and 10-year death-censored graft survival.

Analytical Approach

The Kaplan-Meier method was used to generate survival curves, and the log rank test was used to compare graft survival.

Results

The rate of observed delayed graft function increased with cold ischemia time (20.9%, 28.1%, 32.4%, 37.5%, and 35.8%). Primary nonfunction also showed a similar increase with cold ischemia time (0.6%, 0.9%, 1.3%, 2.1%, and 2.3%), During a median follow-up time of 4.6 years, 37,301 recipients experienced death-censored graft failure. Analysis based on kidney donor profile index (KDPI) demonstrated significant differences in 10-year death-censored graft survival, with a death-censored graft survival in recipients of a kidney with a KDPI <85% of 71.0% (95% CI, 70.5%-71.5%), 70.5% (95% CI, 69.9%-71.0%), 69.6% (95% CI, 68.7%-70.4%), 65.5% (95% CI, 63.7%-67.3%), and 67.2% (95% CI, 64.6%-69.6%), compared to 53.5% (95% CI, 51.1%-55.8%), 50.7% (95% CI, 48.3%-53.1%), 50.3% (95% CI, 46.6%-53.8%), 50.7% (95% CI, 45.1%-56.1%), and 48.3% (95% CI, 40.0%-56.1%), for recipients of a kidney with a KDPI >85%.

Limitations

Heterogeneity of acceptance patterns among transplant centers, presence of confounding variables leading to acceptance of kidneys with prolonged cold ischemia times.

Conclusions

Cold ischemia time was associated with an increased risk of delayed graft function and primary nonfunction. However, the effect of increased cold ischemia time is modest and has less impact than the KDPI. Transplant programs should not consider prolonged cold ischemia time alone as a predominant reason to decline an organ, especially with a KDPI <85%.

Implications of Accumulated Cold Time for US Kidney Transplantation Offer Acceptance

BACKGROUND AND OBJECTIVES

Reducing discard is important for the US transplantation system because nearly 20% of the deceased donor kidneys are discarded. One cause for the discards is the avoidance of protracted cold ischemia times. Extended cold ischemia times at transplant are associated with additional risk of graft failure and patient mortality. A preference for local (within the same donor service area) or low-Kidney Donor Risk Index organs, the endogeneity of cold ischemia time during organ allocation, and the use of provisional offers all complicate the analysis of cold ischemia times' influence on kidney acceptance decision making.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Using January 2018 to June 2019 Organ Procurement and Transplantation Network data, we modeled the probability of accepting an offer for a kidney after provisional acceptance. We use logistic regression that includes cold ischemia time, Kidney Donor Risk Index, and other covariates selected from literature. Endogeneity of cold ischemia time was treated by a two-stage instrumental variables approach.

RESULTS

Logistic regression results for 3.33 million provisional acceptances from 12,369 donors and 108,313 candidates quantify trade-offs between cold ischemia time at the time of offer acceptance and donor-recipient characteristics. Overall, each additional 2 hours of cold ischemia time affected acceptance for nonlocal and local recipients (odds ratio, 0.75; 95% confidence interval, 0.73 to 0.77, odds ratio, 0.88; 95% confidence interval, 0.86 to 0.91; P<0.001). For Kidney Donor Risk Index >1.75 (Kidney Donor Profile Index >85) kidneys, an additional 2 hours of cold ischemia time for nonlocal and local recipients was associated with acceptance with odds ratio, 0.58; 95% confidence interval, 0.54 to 0.63 (nonlocal) and odds ratio, 0.65; 95% confidence interval, 0.6 to 0.7 (local); P<0.001. The effect of an additional 2 hours of cold ischemia time on acceptance of kidneys with Kidney Donor Risk Index ≤1.75 (Kidney Donor Profile Index ≤85) was less pronounced for nonlocal offers (odds ratio, 0.82; 95% confidence interval, 0.80 to 0.85; P<0.001) and not significant for local offers.

CONCLUSIONS

The acceptability of marginal organs was higher when placements were nearer to the donor and when cold ischemia time was shorter.

The Suitability of Potential Organ Donors Using Real Case-Scenarios; Do we Need to Create a "Donor Board" Process for Donors Perceived as Unlikely Suitable?

Introduction: Despite availability of selection criteria, different interpretations can lead to variability in the appreciation of donor eligibility with possible viable organs missed. Our primary objective was to test the perception of feasibility of potential organ donors through the survey of a small sample of external evaluators.

Methods: Clinical scenarios summarizing 66 potential donors managed in the first year of our Organ Recovery Center were sent to four critical care physicians to evaluate the feasibility of the potential donors and the probability of organ procurement.

Results: Potential donors procuring at least one organ were identified in 55 of the 66 cases (83%). Unanimity was reached in 38 cases, encompassing 35 out of the 55 converted and 3 of the non-converted donors. The overall agreement was moderate (kappa = 0.60, 95% CI: 0.37–0.82). For the organs finally procured for transplantation, organ donation was predicted for the majority of the cases, but high discrepancy was present with the final outcome of organs not procured (particularly liver and kidney).

Conclusion: The assessment of a potential donor is a complex dynamic process. In order to increase organ availability, standardized electronically clinical data, as well a “donor board” structure of decision might inform future systems.

Transplant Center Variability in Organ Offer Acceptance and Mortality Among US Patients on the Heart Transplant Waitlist

Importance

Under the current Centers for Medicare & Medicaid Services guidelines, there is incentivization to optimize posttransplant outcomes regardless of mortality among patients on the waitlist and transplant rates; few data exist with regard to transplant center acceptance practices and survival to heart transplant.

Objectives

To evaluate the extent of variability in organ acceptance practices in the US and whether this center-level behavior is associated with heart transplant candidate survival.

Design, Setting, and Participants

In this retrospective cohort study, the US National Transplant Registry was queried for all match runs of adult candidates listed for isolated heart transplant between May 1, 2007, and March 31, 2017. Data analysis was conducted from October 30, 2018, to May 1, 2019. The final cohort included 93 transplant centers, 19 703 donors, and 9628 candidates.

Main Outcomes and Measures

Center acceptance rates for heart allografts offered to the highest-priority candidates, association between center acceptance rate and mortality among patients on the waitlist, and posttransplant outcomes between candidates who accepted their first-rank offers vs those who accepted previously declined offers.

Results

Among 19 703 unique organ offers, 6302 hearts (32.0%) were accepted for first-rank candidates. After adjustment for donor, candidate, and geographic covariates, transplant centers varied in acceptance rates (12.3%-61.5%) of offers made to first-rank candidates. Higher acceptance rates were associated with lower cumulative incidence of 1-year mortality among patients on the waitlist. For every 10% increase in adjusted center acceptance rate, the risk of mortality decreased by 27% (subdistribution hazard ratio, 0.73; 95% CI, 0.67-0.80). No statistically significant difference was observed in 5-year adjusted posttransplant patient survival (adjusted hazard ratio, 1.02; 95% CI, 0.94-1.11) and graft failure (subdistribution hazard ratio; 0.95; 95% CI, 0.83-1.09) between hearts accepted at the first-rank compared with lower-rank positions.

Conclusions and Relevance

Variability in heart allograft acceptance rates appears to exist among transplant centers, with candidates listed at lower acceptance rate centers being more likely to experience mortality while on the waitlist. Comparable posttransplant survival suggests that allografts that were declined as a first offer perform as well as those that were accepted at their first offer. These findings suggest that organ acceptance rate or time to transplant from being added to the waitlist may be an additional measure of heart transplant program performance.

A Simulation-Based Optimization Model to Study the Impact of Multiple-Region Listing and Information Sharing on Kidney Transplant Outcomes

More than 8000 patients on the waiting list for kidney transplantation die or become ineligible to receive transplants due to health deterioration. At the same time, more than 4000 recovered kidneys from deceased donors are discarded each year in the United States. This paper develops a simulation-based optimization model that considers several crucial factors for a kidney transplantation to improve kidney utilization. Unlike most proposed models, the presented optimization model incorporates details of the offering process, the deterioration of patient health and kidney quality over time, the correlation between patients’ health and acceptance decisions, and the probability of kidney acceptance. We estimate model parameters using data obtained from the United Network of Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients (SRTR). Using these parameters, we illustrate the power of the simulation-based optimization model using two related applications. The former explores the effects of encouraging patients to pursue multiple-region waitlisting on post-transplant outcomes. Here, a simulation-based optimization model lets the patient select the best regions to be waitlisted in, given their demand-to-supply ratios. The second application focuses on a system-level aspect of transplantation, namely the contribution of information sharing on improving kidney discard rates and social welfare. We investigate the effects of using modern information technology to accelerate finding a matching patient to an available donor organ on waitlist mortality, kidney discard, and transplant rates. We show that modern information technology support currently developed by the United Network for Organ Sharing (UNOS) is essential and can significantly improve kidney utilization.

Donor Kidney Quality and Transplant Outcome: An Economic Evaluation of Contemporary Practice

OBJECTIVES

The study had two main aims. First, we assessed the cost-effectiveness of transplanting deceased donor kidneys of differing quality levels based on the Kidney Donor Profile Index (KDPI). Second, we assessed the cost-effectiveness of remaining on the waiting list until a high-quality kidney becomes available compared to transplanting a lower-quality kidney.

METHODS

A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Separate models were developed for 4 separate KDPI bands, with higher values indicating lower quality. Models were simulated in 1-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient from the healthcare payer's perspective. Weibull regression was used to calculate the time-dependent transition probabilities in the base analysis. The impact uncertainty arising in model parameters was included by probabilistic sensitivity analysis using the Monte Carlo simulation method. Willingness to pay was considered as Australian $28 000.

RESULTS

Transplanting a kidney of any quality is cost-effective compared to remaining on a waitlist. Transplanting a lower KDPI kidney is cost-effective compared to a higher KDPI kidney. Transplanting lower KDPI kidneys to younger patients and higher KDPI kidneys to older patients is also cost-effective. Depending on dialysis in hopes of receiving a lower KDPI kidney is not a cost-effective strategy for any age group.

CONCLUSION

Efforts should be made by the health systems to reduce the discard rates of low-quality kidneys with the view of increasing the transplant rates.

Association between changes in Membership and Professional Standards Committee review criteria and use of higher-risk kidneys for transplant

The Organ Procurement and Transplantation Network's Membership and Professional Standards Committee implemented an operational rule on March 1, 2017, intended to increase the number of kidneys transplanted from donors with kidney donor profile index (KDPI) ≥ 85% into recipients with poor estimated posttransplant survival (≥ 80%). Using data from the Scientific Registry of Transplant Recipients, ordinal and logistic regressions estimated, respectively, differences in kidney yield (number of transplanted kidneys per recovered donor) and offer acceptance practices before and after implementation. We included donors recovered January 1, 2016-February 28, 2018. The odds of higher kidney yield for donors with KDPI ≥ 85% were 27% higher after implementation (odds ratio, _1.06 1.27_1.53 ), but odds were also 20% higher for donors with KDPI < 85% (_1.04 1.20_1.38 ). Thus, kidney yield was higher for all donors, with a slightly larger difference for donors with KDPI ≥ 85%. Additionally, the difference in offer acceptance before and after implementation was similar regardless of KDPI (KDPI < 85%, _0.97 1.02_1.07 ; KDPI ≥ 85%, _0.95 1.04_1.14 ). In the first year after implementation, kidney yield increased for donors with KDPI < and ≥ 85%. Thus, kidney yield from higher KDPI donors may have increased without the operational rule.

The Collaborative Innovation and Improvement Network (COIIN): Effect on donor yield, waitlist mortality, transplant rates, and offer acceptance

The Organ Procurement and Transplantation Network implemented the Collaborative Improvement and Innovation Network (COIIN) to improve the use of donors with kidney donor profile index >50%. COIIN recruited 2 separate cohorts of kidney transplant programs. Cohort A included 19 programs of 44 applicants (January 1, 2017, to September 30, 2017), and cohort B included 39 programs of 47 applicants (October 1, 2017, to June 30, 2018). We investigated the effect of COIIN on kidney yield (number of kidneys transplanted from donors from whom any organ was recovered), offer acceptance, deceased donor transplant rates, and waitlist mortality rates for January 1, 2016, to March 31, 2019. COIIN did not notably affect kidney yield or waitlist mortality rates. Cohort A, but not cohort B, had significantly higher deceased donor transplant and offer acceptance rates during its intervention period than programs not in COIIN (adjusted transplant rate ratio: cohort A, _1.08 1.17_1.27 , cohort B, _0.94 1.01_1.08 ; adjusted offer acceptance ratio: cohort A, _1.08 1.18_1.29 , cohort B, _0.93 1.00_1.08 ). Thus, COIIN improved the use of kidneys at programs in cohort A but not at those in cohort B. Further research is necessary to understand the different effects for cohorts A and B, and further monitoring of posttransplant outcomes is required.

Variability in donor organ offer acceptance and lung transplantation survival

BACKGROUND

Lung transplantation offers a survival benefit for patients with end-stage lung disease. When suitable donors are identified, centers must accept or decline the offer for a matched candidate on their waitlist. The degree to which variability in per-center offer acceptance practices impacts candidate survival is not established. The purpose of this study was to determine the degree of variability in per-center rates of lung transplantation offer acceptance and to ascertain the associated contribution to observed differences in per-center waitlist mortality.

METHODS

We performed a retrospective cohort study of candidates waitlisted for lung transplantation in the US using registry data. Logistic regression was fit to assess the relationship of offer acceptance with donor, candidate, and geographic factors. Listing center was evaluated as a fixed effect to determine the adjusted per-center acceptance rate. Competing risks analysis employing the Fine-Gray model was undertaken to establish the relationship between adjusted per-center acceptance and waitlist mortality.

RESULTS

Of 15,847 unique organ offers, 4,735 (29.9%) were accepted for first-ranked candidates. After adjustment for important covariates, transplant centers varied markedly in acceptance rate (9%-67%). Higher cumulative incidence of 1-year waitlist mortality was associated with lower acceptance rate. For every 10% increase in adjusted center acceptance rate, the risk of waitlist mortality decreased by 36.3% (sub-distribution hazard ratio 0.637; 95% confidence interval 0.592-0.685).

CONCLUSIONS

Variability in center-level behavior represents a modifiable risk factor for waitlist mortality in lung transplantation. Further intervention is needed to standardize center-level offer acceptance practices and minimize waitlist mortality.

Evaluation of Accepting Kidneys of Varying Quality for Transplantation or Expedited Placement With Decision Trees

BACKGROUND

Underutilization of marginal-quality kidneys for transplantation produced ideas of expediting kidney placement for populations with decreased opportunities of receiving transplants. Such policies can be less efficacious for specific individuals and should be scrutinized until the decision-making for accepting marginal-quality organs, which has relied on experiential judgment, is better understood at the individual level. There exist rigorous tools promoting personalized decisions with useful and objective information.

METHODS

This article introduces a decision-tree methodology that analyzes a patient's dilemma: to accept a kidney offer now or reject it. The methodology calculates the survival benefit of accepting a kidney given a certain quality now and the survival benefit of rejecting it. Survival benefit calculation accounts for patients' and donors' characteristics and transplant centers' and organ procurement organizations' performances and incorporates patients' perceived transplant and dialysis utilities. Valuations of rejecting an offer are contingent on future opportunities and subject to uncertainty in the timing of successive kidney offers and their quality and donor characteristics.

RESULTS

The decision tree was applied to a realistic patient profile as a demonstration. The tool was tested on 1000 deceased-donor kidney offers in 2016. Evaluating up to 1 year of future offers, the tool attains 61% accuracy, with transplant utility of 1.0 and dialysis utility of 0.5. The accuracy reveals potential bias in kidney offer acceptance/rejection at transplant centers.

CONCLUSIONS

The decision-tree tool presented could aid personalized transplant decision-making in the future by providing patients with calculated, individualized survival benefits between accepting and rejecting a kidney offer.

Prospective Validation of Prediction Model for Kidney Discard

BACKGROUND

Many kidneys are discarded every year, with 3631 kidneys discarded in 2016 alone. Identifying kidneys at high risk of discard could facilitate "rescue" allocation to centers more likely to transplant them. The Probability of Delay or Discard (PODD) model was developed to identify marginal kidneys at risk of discard or delayed allocation beyond 36 hours of cold ischemia time. However, PODD has not been prospectively validated, and patterns of discard may have changed after policy changes such as the introduction of Kidney Donor Profile Index and implementation of the Kidney Allocation System (KAS).

METHODS

We prospectively validated the PODD model using Scientific Registry of Transplant Recipients data in the KAS era (January 1, 2015, to March 1, 2018). C statistic was calculated to assess accuracy in predicting kidney discard. We assessed clustering in centers' utilization of kidneys with PODD >0.6 ("high-PODD") using Gini coefficients. Using match run data from January 1, 2015, to December 31, 2016, we examined distribution of these high-PODD kidneys offered to centers that never accepted a high-PODD kidney.

RESULTS

The PODD model predicted discard accurately under KAS (C-statistic, 0.87). Compared with utilization of low-PODD kidneys (Gini coefficient = 0.41), utilization of high-PODD kidneys was clustered more tightly among a few centers (Gini coefficient, 0.84 with >60% of centers never transplanted a high-PODD kidneys). In total, 11684 offers (35.0% of all high-PODD offers) were made to centers that never accepted a high-PODD kidney.

CONCLUSIONS

Prioritizing allocation of high-PODD kidneys to centers that are more likely to transplant them might help reduce kidney discard.

Seeking new answers to old questions about public reporting of transplant program performance in the United States

The Scientific Registry of Transplant Recipients (SRTR) is mandated by the National Organ Transplant Act, the Final Rule, and the SRTR contract with the Health Resources and Services Administration to report program-specific information on the performance of transplant programs. Following a consensus conference in 2012, SRTR developed a new version of the public website to improve public reporting of often complex metrics, including changing from a 3-tier to a 5-tier summary metric for first-year posttransplant survival. After its release in December 2016, the new presentation was moved to a "beta" website to allow collection of additional feedback. SRTR made further improvements and released a new beta website in May 2018. In response to feedback, SRTR added 5-tier summaries for standardized waitlist mortality and deceased donor transplant rate ratios, along with an indicator of which metric most affects survival after listing. Presentation of results was made more understandable with input from patients and families from surveys and focus groups. Room for improvement remains, including continuing to make the data more useful to patients, deciding what additional data elements should be collected to improve risk adjustment, and developing new metrics that better reflect outcomes most relevant to patients.

Association of Dialysis Duration With Outcomes After Kidney Transplantation in the Setting of Long Cold Ischemia Time

Background

There is no mechanism that matches hard-to-place kidneys with the most appropriate candidate. Thus, unwanted kidney offers are typically to recipients with long renal replacement time (vintage) which is a strong risk factor for mortality and graft failure, and in combination with prolonged cold ischemia time (CIT), may promote interactive effects on outcomes.

Methods

Consecutive adult isolated kidney transplants between October 2015 and December 2017 were stratified by vintage younger than 1 year and CIT longer than 30 hours.

Results

Long (n = 169) relative to short (n = 93) vintage recipients were significantly more likely to be younger (32.2 years vs 56.9 years, P = 0.02), black race (40.8% vs 18.3%, P = 0.02), have higher estimated posttransplant survival (52.6 vs 42.0, P = 0.04), and have a comorbid condition (45.6% vs 30.1%, P = 0.02); they were less likely to receive a donation after circulatory death kidney (27.8% vs 39.8%, P = 0.05). Long vintage was significantly associated with length of stay longer than 4 days (45.5% vs 30.1%, P = 0.02), and 30-day readmission (37.3% vs 22.6%, P = 0.02) but not additional operations (17.8% vs 15.1%, P = 0.58), short-term patient mortality (3.0% vs 2.2%, P = 0.70), or overall graft survival (P = 0.23). On multivariate logistic regression, long vintage remained an independent risk factor for 30-day readmission (adjusted odds ratio, 1.92; 95% confidence interval, 1.06-3.47); however, there was no interaction of vintage and CIT for this outcome (P = 0.84).

Conclusions

Readmission is significantly associated with pretransplant dialysis duration; however, CIT is not a modifying factor for this outcome.

Scientific Registry of Transplant Recipients program-specific reports: where we have been and where we are going

PURPOSE OF REVIEW

Reporting provider data on quality to patients and the general public is increasingly common in healthcare. Reporting outcomes in solid organ transplantation has always been controversial and deserves careful consideration to ensure optimal results.

RECENT FINDINGS

As mandated by Federal law, the Scientific Registry of Transplant Recipients publishes program-specific reports on transplant candidates, recipients, donors, and transplant outcomes every 6 months. Recent changes designed to make the results more easily understood by patients and the general public have been well received by patients and controversial among providers. In particular, outcomes are now reported using a five-tier system that distinguishes program results better than the old three-tier system, in which almost all programs were reported "as expected." Metrics that reflect access to transplant are also reported, including transplant rate and survival on the waiting list. Possible measures of longer term outcomes and program rates of accepting organs for transplant are being explored.

SUMMARY

Providing detailed information regarding transplant program practices and outcomes in ways that patients and the general public can understand remains a major focus of the Scientific Registry of Transplant Recipients. Efforts to improve data collection and metrics reported are ongoing.

Hard-to-place kidney offers: Donor- and system-level predictors of discard

Understanding risk factors for deceased-donor kidney nontransplantation is important since discard rates remain high. We analyzed DonorNet^® data of consecutive deceased-donor nonmandatory share primary kidney-only offers to adult candidates at our center and beyond between July 1, 2015 and March 31, 2016 for donor- and system-level risk factors of discard, defined as nontransplantation at our or subsequent transplant centers. Exclusions were hepatitis C virus/hepatitis B virus core antibody status, blood type AB, and donor <1 year based on low candidate waitlist size. Of 456 individual kidney offers, from 296 donors, 73% were discarded. Most were national (93%) offers from Kidney Donor Profile Index 35-85% (n = 233) or >85% (n = 208) donors late in the allocation sequence with prior refusals logged for numerous candidates. On multivariate regression, factors significantly associated with discard were donor cerebrovascular accident (adjusted odds ratio [aOR]: 3.32), cancer transmission concern (aOR: 6.5), renal artery luminal compromise (aOR: 3.97), biopsy score ≥3 (aOR: 5.09), 2-hour pump resistive index >0.4 (aOR: 3.27), absence of pump (aOR: 2.58), nonspecific kidney abnormality (aOR: 2.76), increasing offer cold ischemia time category 11-15, 16-20, and >21 hours (aOR: 2.07, 2.33, 2.82), nighttime notification (aOR: 2.19), and neither kidney placed at time of offer (aOR: 2.74). Many traditional determinants of discard lack discriminatory value when granular factors are assessed. System-level factors also influence discard and warrant further study.

Kidney offer acceptance at programs undergoing a Systems Improvement Agreement

In the United States, the Centers for Medicare and Medicaid Services (CMS) use Systems Improvement Agreements (SIAs) to require transplant programs repeatedly flagged for poor-performance to improve performance or lose CMS funding for transplants. We identified 14 kidney transplant (KT) programs with SIAs and 28 KT programs without SIAs matched on waitlist volume and characterized kidney acceptance using SRTR data from 12/2006-3/2015. We used difference-in-differences linear regression models to identify changes in acceptance associated with an SIA independent of program variation and trends prior to the SIA. SIA programs accepted 26.9% and 22.1% of offers pre- and post-SIA, while non-SIA programs accepted 33.9% and 44.4% of offers in matched time periods. After adjustment for donor characteristics, time-varying waitlist volume, and secular trends, SIAs were associated with a 5.9 percentage-point (22%) decrease in kidney acceptance (95% CI: -10.9 to -0.8, P = .03). The decrease in acceptance post-SIA was more pronounced for KDPI 0-40 kidneys (12.3 percentage-point decrease, P = .007); reductions in acceptance of higher KDPI kidneys occurred pre-SIA. Programs undergoing SIAs substantially reduced acceptance of kidney offers for waitlisted candidates. Attempts to improve posttransplant outcomes might have the unintended consequence of reducing access to transplantation as programs adopt more restrictive organ selection practices.

Heart and lung organ offer acceptance practices of transplant programs are associated with waitlist mortality and organ yield

Variation in heart and lung offer acceptance practices may affect numbers of transplanted organs and create variability in waitlist mortality. To investigate these issues, offer acceptance ratios, or adjusted odds ratios, for heart and lung transplant programs individually and for all programs within donation service areas (DSAs) were estimated using offers from donors recovered July 1, 2016, and June 30, 2017. Logistic regressions estimated the association of DSA-level offer acceptance ratios with donor yield and local placement of organs recovered in the DSA. Competing risk methodology estimated the association of program-level offer acceptance ratios with incidence and rate of waitlist removals due to death or becoming too sick to undergo transplant. Higher DSA-level offer acceptance was associated with higher yield (odds ratios [ORs]: lung, _1.04 1.11_1.19 ; heart, _1.09 1.21_1.35 ) and more local placement of transplanted organs (ORs: lung, _1.01 1.12_1.24 ; heart, _1.47 1.69_1.93 ). Higher program-level offer acceptance was associated with lower incidence of waitlist removal due to death or becoming too sick to undergo transplant (hazard ratios [HRs]: heart, _0.80 0.86_0.93 ; lung, _0.67 0.75_0.83 ), but not with rate of waitlist removal (HRs: heart, _0.91 0.98_1.06 ; lung, _0.89 0.99_1.10 ). Heart and lung offer acceptance practices affected numbers of transplanted organs and contributed to program-level variability in the probability of waitlist mortality.

An economic assessment of contemporary kidney transplant practice

Kidney transplantation is the optimal therapy for end-stage renal disease, prolonging survival and reducing spending. Prior economic analyses of kidney transplantation, using Markov models, have generally assumed compatible, low-risk donors. The economic implications of transplantation with high Kidney Donor Profile Index (KDPI) deceased donors, ABO incompatible living donors, and HLA incompatible living donors have not been assessed. The costs of transplantation and dialysis were compared with the use of discrete event simulation over a 10-year period, with data from the United States Renal Data System, University HealthSystem Consortium, and literature review. Graft failure rates and expenditures were adjusted for donor characteristics. All transplantation options were associated with improved survival compared with dialysis (transplantation: 5.20-6.34 quality-adjusted life-years [QALYs] vs dialysis: 4.03 QALYs). Living donor and low-KDPI deceased donor transplantations were cost-saving compared with dialysis, while transplantations using high-KDPI deceased donor, ABO-incompatible or HLA-incompatible living donors were cost-effective (<$100 000 per QALY). Predicted costs per QALY range from $39 939 for HLA-compatible living donor transplantation to $80 486 for HLA-incompatible donors compared with $72 476 for dialysis. In conclusion, kidney transplantation is cost-effective across all donor types despite higher costs for marginal organs and innovative living donor practices.

Offer acceptance practices and geographic variability in allocation model for end-stage liver disease at transplant

Offer acceptance practices may cause geographic variability in allocation Model for End-Stage Liver Disease (aMELD) score at transplant and could magnify the effect of donor supply and demand on aMELD variability. To evaluate these issues, offer acceptance practices of liver transplant programs and donation service areas (DSAs) were estimated using offers of livers from donors recovered between January 1, 2016, and December 31, 2016. Offer acceptance practices were compared with liver yield, local placement of transplanted livers, donor supply and demand, and aMELD at transplant. Offer acceptance was associated with liver yield (odds ratio, 1.32; P < 0.001), local placement of transplanted livers (odds ratio, 1.34; P < 0.001), and aMELD at transplant (average aMELD difference, -1.62; P < 0.001). However, the ratio of donated livers to listed candidates in a DSA (ie, donor-to-candidate ratio) was associated with median aMELD at transplant (r = -0.45; P < 0.001), but not with offer acceptance (r = 0.09; P = 0.50). Additionally, the association between DSA-level donor-to-candidate ratios and aMELD at transplant did not change after adjustment for offer acceptance. The average squared difference in median aMELD at transplant across DSAs was 24.6; removing the effect of donor-to-candidate ratios reduced the average squared differences more than removing the effect of program-level offer acceptance (33% and 15% reduction, respectively). Offer acceptance practices and donor-to-candidate ratios independently contributed to geographic variability in aMELD at transplant. Thus, neither offer acceptance nor donor-to-candidate ratios can explain all of the geographic variability in aMELD at transplant. Liver Transplantation 24 478-487 2018 AASLD.