Deceased donor kidney allocation: an economic evaluation of contemporary longevity matching practices

Longevity Matching for Living Donor Renal Transplantation

INTRODUCTION

This study identifies the effect of individual donor and recipient characteristics on graft survival in living-donor kidney transplantation (LDKT) using a recently described novel measure, kidney life years (KLYs).

MATERIALS AND METHODS

The Organ Procurement and Transplantation Network/United Network for Organ Sharing database was used to identify first-time kidney-only LDKT recipients between 1987 and 2020 who did not experience death with a functioning graft (DWFG) and were not missing relevant information (n = 87,290). Patient characteristics were evaluated using Cox and multiple regression analyses, with the dependent variable being KLYs. An equation for expected KLYs based on patient characteristics was created using regression coefficients. The equation was validated using bootstrapped Pearson correlations and then applied to the DWFG group for comparison.

RESULTS

Based on statistical significance from Cox and multiple linear regression analyses, 9 of the original 18 variables were selected for inclusion in the equation. Variables with notable impact included HLA match points (0.021 KLYs; 95% CI: [0.019,0.024]; P ≤ .001), Donor Age (-0.030 KLYs; 95% CI: [-0.035,-0.025]; P ≤ .001), and Donor African American Ethnicity (-2.356 KLYs; 95% CI: [-2.552,-2.159]; P ≤ .001). Equation validation was supported, given a negative correlation (r = -0.071; P ≤ .001) between expected KLY change and observed graft failure. Expected KLY change was found to be greater in those who eventually DWFG when compared with all other LDKTs (t = -5.735, P ≤ .001).

CONCLUSIONS

Increasing HLA match points may be more beneficial for graft longevity than minimizing donor age in comparisons using realistic between-donor differences. Additionally, greater average expected KLYs in those who ultimately DWFG may illustrate an opportunity for improved donor-recipient matching.

Equity matrix for kidney transplant allocation

There is a general agreement that the distribution of kidneys for transplantation should balance utility criteria with justice. Moreover, a kidney allocation system must be based on transparent policies and seen as an ongoing process. This study aims to present an allocation system grounded on an equity matrix that balances the criteria of utility and justice. Synthetic data for a waiting list with 2000 transplant candidates and a pool of 280 donors were generated. A color priority system, the Eurotransplant (ET) kidney allocation system, and the proposed Equity Matrix (EQM) allocation system were compared after 1000 iterations of kidney allocations. Distributions of variables like the age difference, Human Leukocyte Antigens (HLA) mismatches (mmHLA), recipients' time on dialysis, cPRA, and a transplant score obtained by different allocation models were compared graphically and with Cohen's d effect size. For the analyzed variables, when we compare only the selected recipients from ET with the selected recipients from the EQM neutral model, we can conclude that the former model selects more hypersensitized recipients, a higher number of 65+ years' old recipients with 65+ years' old donors and higher number of recipients with 0 mmHLA. While recipients from EQM neutral are slightly older, have a lower age difference with their donors, have a lower number of mmHLA, are less likely to have 6 mmHLA with their donors, and have more time on dialysis. The proposed EQM model attempts to provide a simple, transparent, and equitable response to a complex question with results that outperform established practices.

The need for integrated clinical and administrative data models for risk adjustment in assessment of the cost transplant care

INTRODUCTION

Value-based purchasing requires accurate techniques to appropriately measure both outcomes and cost with robust adjustment for differences in severity of illness. Traditional methods to adjust cost estimates have exclusively used administrative data derived from billing claims to identify comorbidity and complications. Transplantation uniquely has accurate national clinical registry data that can be used to supplement administrative data.

METHODS

Administrative claims from the Vizient, Inc, Clinical Data Base (CDB) were linked with clinical records from the Scientific Registry for Transplant Recipients for 76 liver and 109 kidney transplant programs. Using either or both datasets, we fitted a regression model to the total direct cost of care for 16,649 kidney and 6058 liver transplants.

RESULTS

The proportion of variation explained by these risk-adjustment models increased significantly when combined administrative and clinical data were used for kidney (administrative only R² = .069, clinical only R² = .047, combined R² = .14, p < .0001) and liver (administrative only R² = .28, clinical only R² = .25, combined R² = .33, p < .0001).

CONCLUSION

Incorporating accurate clinical data into risk-adjustment methodologies can improve risk adjustment methodologies; however, as majority of variation in cost remains unexplained by these risk-adjustment models further work is needed to accuracy assess transplant value.

Cost-Effectiveness and Budget Impact Analysis of Implementing a 'Soft Opt-Out' System for Kidney Donation in Australia

INTRODUCTION

There is a severe shortage of donor organs globally. There is growing interest in understanding how a 'soft opt-out' organ donation system could help bridge the supply and demand gap for donor organs. This research aims to estimate the cost-effectiveness and budget impact of implementing a 'soft opt-out' organ donation system for kidney donation.

METHODS

A decision-analytic model was developed to estimate the incremental costs from a health system's perspective, quality-adjusted life-years (QALYs), and death averted of people who have kidney failure, comparing a 'soft opt-out' organ donation system to an 'opt-in' system. This study analysed three scenarios where the 'soft opt-out' system generated a 20%, 30%, and 40% increase in deceased organ donation rates over 20 years. A 5-year time horizon was adopted for the budget impact analysis.

RESULTS

A 20% increase in organ donation rates could have a cost saving of 650 million Australian dollars (A$) and a 10,400-QALY gain. A 20% increase would avert more than 1500 deaths, while a 40% increase would avert 3200 deaths over a time horizon of 20 years. Over the first 5 years, a 20% increase would have a net saving of A$53 million, increasing to A$106 million if the donation rate increases by 40%.

CONCLUSION

A 'soft opt-out' organ donation system would return a cost saving for the healthcare system, a net gain in QALYs, and prevention of a significant number of deaths. Advantageous budgetary impact is important, but understanding the aversion for a 'soft opt-out' system in Australia is also important and remains a priority for further research.

Time-to-event analysis in economic evaluations: a comparison of modelling methods to assess the cost-effectiveness of transplanting a marginal quality kidney

BACKGROUND

Economic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney.

METHODS

Cost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon.

RESULTS

DES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased.

CONCLUSION

Different modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.