Efficient and Fair Heart Allocation Policies for Transplantation

Abstract

Background: The optimal allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. We developed a simulation model of the US waiting list for heart transplantation to investigate the potential impacts of allocation policies on several outcomes such as pre- and posttransplant mortality. Methods: We used data from the United Network for Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipient (SRTR) to simulate the heart allocation system. The model is validated by comparing the outcomes of the simulation with historical data. We also adapted fairness schemes studied in welfare economics to provide a framework to assess the fairness of allocation policies for transplantation. We considered three allocation policies, each a modification to the current UNOS allocation policy, and analyzed their performance via simulation. The first policy broadens the geographical allocation zones, the second modifies the health status order for receiving hearts, and the third prioritizes patients according to their waiting time. Results: Our results showed that the allocation policy similar to the current UNOS practice except that it aggregates the three immediate geographical allocation zones, improves the health outcomes, and is “closer” to an optimal fair policy compared to all other policies considered in this study. Specifically, this policy could have saved 319 total deaths (out of 3738 deaths) during the 2006 to 2014 time horizon, in average. This policy slightly differs from the current UNOS allocation policy and allows for easy implementation. Conclusion: We developed a model to compare the outcomes of heart allocation policies. Combining the three immediate geographical zones in the current allocation algorithm could potentially reduce mortality rate and is closer to an optimal fair policy.

Ensuring the need is met: A 50-year simulation study of the National Kidney Registry’s family voucher program

“…A Monte Carlo simulation model 5 was constructed to estimate the annual number of voucher redemptions relative to the number of kidneys available for transplant over a 50‐year time horizon under several projected scenarios. Monte Carlo computer simulation models are commonly used in a wide range of fields such as finance, insurance, and transportation to perform risk analyses in complex situations where it is not possible to mathematically calculate future values for important outcome variables and have been applied in projecting availability of donor livers 6 and impact of organ allocation strategies 7‐9 . Our model tracks simulated donors and voucher holders until either a voucher is redeemed, the voucher holder dies, or the voucher “expires” (voucher holder reaches age 85 or another voucher holder from the same family redeems voucher).…”

Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

“…Broadened Regional Sharing (BRS) Policy: The geographic disparity is an important problem in organ allocation and sharing organs can address it. Hasankhani and Khademi (2017) proposed a policy similar to UNOS that broadens the regional sharing of the donor hearts by appropriately combining UNOS allocation zones. In each zone, the same health status, blood type match, and waiting time prioritization rules are considered as UNOS.…”

“…The initial population in each class at the start of the planning horizon is estimated by using the SRTR annual data reports. All the parameters are directly estimated from the UNOS and SRTR data sets or, if not available, by the validated simulation model of Hasankhani and Khademi (2017) and Hasankhani (2020). The overview of the simulation model is enclosed in EC.1.…”