TY - JOUR
T1 - Kidney allocation rules simulator
AU - Lima, Bruno A.
AU - Henriques, Teresa S.
AU - Alves, Helena
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/6
Y1 - 2022/6
N2 - The greatest challenge of any kidney transplant program lies in finding enough organ donors (in number and quality) for all waitlisted transplant candidates. Unfortunately, we must resign ourselves to a manifestly insufficient supply of organs for the current demand. Furthermore we must be able to predict kidney transplant success at organ allocation if we want to minimize the number of patients who return to an already overcrowded waiting list for transplantation. Therefore, the definition of deceased donors' kidney allocation rules on transplantation must be supported by simulations that allow foreseeing, as much as possible, the consequences of these rules. Here we present the Kidney Allocation Rules Simulator (KARS) application that enables testing different kidney transplant allocation’ systems with different donors and transplant candidates' datasets. In this application, it is possible to simulate allocation rules implemented in Portugal, in the United Kingdom, in countries within Eurtotransplant, and a previously suggested color priority system. As inputs, this application needs three data files: a file with transplant candidates' information, a file with candidates' anti-HLA antibodies, and a file with donors' information. As output, we will have a file with donor-recipient pairs selected according to the kidney allocation system simulated. When seeking waste reduction while ensuring a fair distribution of organs from deceased donors, the definition of rules selecting donor-recipient pairs in renal transplantation must be based on evidence supported by data. On the continuously changing process for a better distribution of an increasingly scarce resource must, we must be able to predict transplant outcomes when defining the best allocation rules.
AB - The greatest challenge of any kidney transplant program lies in finding enough organ donors (in number and quality) for all waitlisted transplant candidates. Unfortunately, we must resign ourselves to a manifestly insufficient supply of organs for the current demand. Furthermore we must be able to predict kidney transplant success at organ allocation if we want to minimize the number of patients who return to an already overcrowded waiting list for transplantation. Therefore, the definition of deceased donors' kidney allocation rules on transplantation must be supported by simulations that allow foreseeing, as much as possible, the consequences of these rules. Here we present the Kidney Allocation Rules Simulator (KARS) application that enables testing different kidney transplant allocation’ systems with different donors and transplant candidates' datasets. In this application, it is possible to simulate allocation rules implemented in Portugal, in the United Kingdom, in countries within Eurtotransplant, and a previously suggested color priority system. As inputs, this application needs three data files: a file with transplant candidates' information, a file with candidates' anti-HLA antibodies, and a file with donors' information. As output, we will have a file with donor-recipient pairs selected according to the kidney allocation system simulated. When seeking waste reduction while ensuring a fair distribution of organs from deceased donors, the definition of rules selecting donor-recipient pairs in renal transplantation must be based on evidence supported by data. On the continuously changing process for a better distribution of an increasingly scarce resource must, we must be able to predict transplant outcomes when defining the best allocation rules.
KW - Allocation system
KW - Clinical decision-making
KW - Kidney transplantation
UR - http://www.scopus.com/inward/record.url?scp=85126070805&partnerID=8YFLogxK
U2 - 10.1016/j.trim.2022.101578
DO - 10.1016/j.trim.2022.101578
M3 - Article
C2 - 35278649
AN - SCOPUS:85126070805
SN - 0966-3274
VL - 72
JO - Transplant Immunology
JF - Transplant Immunology
M1 - 101578
ER -