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

Senanayake, Sameera; Graves, Nicholas; Healy, Helen; Baboolal, Keshwar; Barnett, Adrian; Sypek, Matthew P.; Kularatna, Sanjeewa

doi:10.1186/s12913-020-05736-y

Cited by 6 publications

(6 citation statements)

References 36 publications

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“…to affect perioperative complications and posttransplant survival and have been strongly associated with higher cost. 16,17 The addition of registry and claims-based data provide important additional explanatory power as demonstrated by the increase in the R 2 and lower BIC and AIC.…”

Section: Discussionmentioning

confidence: 99%

“…These data do not include objective measures, such as laboratory values and severity of illness scores such as MELD score for LTx and expected posttransplant survival score for KTx. These scores have been validated to affect perioperative complications and posttransplant survival and have been strongly associated with higher cost 16,17 . The addition of registry and claims‐based data provide important additional explanatory power as demonstrated by the increase in the R 2 and lower BIC and AIC.…”

Section: Discussionmentioning

confidence: 99%

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

Axelrod

Schwantes

Harris

et al. 2022

Clinical Transplantation

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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 R 2 = .069, clinical only R 2 = .047, combined R 2 = .14, p < .0001) and liver (administrative only R 2 = .28, clinical only R 2 = .25, combined R 2 = .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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Axelrod

Schwantes

Harris

et al. 2022

Clinical Transplantation

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“…However, we could only fit a uniform distribution due to limitations in the data source. Uniform distributions for transplant costs have been used before [ 41 ], and evidence indicates that the transplant and dialysis costs are not expected to be skewed [ 27 ]. Therefore, the authors believe that this limitation would not affect the results.…”

Section: Discussionmentioning

confidence: 99%

“…1, P7) Both time-dependent and fixed transition probabilities were used as transition probabilities. When time-dependent probabilities were used, the probabilities varied in each cycle and were dependent on how long the cohort had been modelled, while the same probability is used in every cycle when fixed-transition probabilities are used [27]. Four parametric distributions were employed to estimate the time-dependent transition probabilities: Weibull, Exponential, Log-logistic, and Log-normal.…”

Section: Data Sourcesmentioning

confidence: 99%

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

Senanayake

Healy

McPhail

et al. 2022

Appl Health Econ Health Policy

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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.

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“…Secondly, quality of the donor kidney was defined using KDPI, which has only reasonable discriminatory power of predicting graft failure. However, KDPI is the best available index to describe donor kidney quality and has previously been used to define marginal quality kidneys in economic evaluations [ 5 , 6 , 43 ]. Finally, we adopted a healthcare-payer perspective which does not capture costs incurred by the patient.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 6 publications

References 36 publications

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

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

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

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

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