Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method

Taherkhani, Nasrin; Sepehri, Mohammad Mehdi; Shafaghi, Shadi; Khatibi, Toktam

doi:10.1186/s12911-019-0892-y

Cited by 15 publications

(18 citation statements)

References 54 publications

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“…The committee is exploring the use of analytic hierarchy process (AHP), a structured approach to eliciting value judgments and preferences from stakeholders, to establish this idealized policy [23][24][25][26][27]. The AHP results will be compared with the revealed preference analysis presented herein to stimulate discussion on the appropriate level of importance to be placed on each attribute, in accordance with federal regulation governing organ allocation policies [4].…”

Section: Discussionmentioning

confidence: 99%

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

Stewart¹,

Wood

Alcorn³

et al. 2020

Preprint

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Background: The patient ranking process for donor lung allocation in the United States is carried out by a classification-based, computerized algorithm, known as the match system. Experts have wondered whether a continuous, points-based allocation framework would better serve waiting list candidates by removing hard boundaries and increasing transparency into the relative importance of factors used to order candidates. We applied discrete choice modeling to match run data to determine the feasibility of approximating current lung allocation policy by one or more composite scores. Our study aimed to demystify the points-based approach to allocation; quantify the relative importance of factors used in allocation under current policy; and provide a viable policy option that adapts the current, classification-based system to the continuous allocation framework.Methods: Rank ordered logistic regression models were estimated using 6,466 match runs for 5,913 adult donors and 534 match runs for 488 pediatric donors from 2018. Four primary attributes are used to rank candidates and were included in the models: (1) medical priority, (2) candidate age, (3) candidate’s transplant center proximity to the donor hospital, and (4) blood type compatibility with the donor.Results: Two composite scores were developed, one for adult and one for pediatric donor allocation. Candidate rankings based on the composite scores were highly correlated with current policy rankings (Kendall’s Tau ~0.80, Spearman correlation >90%), indicating both scores strongly reflect current policy. In both models, candidates are ranked higher if they have higher medical priority, are registered at a transplant center closer to the donor hospital, or have an identical blood type to the donor. Proximity was the most important attribute. Under a points-based scoring system, candidates in further away zones are sometimes ranked higher than more proximal candidates compared to current policy. Conclusions: Revealed preference analysis of match runs produced composite scores that capture the essence of current policy while removing hard boundaries of the current classification-based system. A carefully crafted, continuous version of lung allocation policy has the potential to make better use of the limited supply of donor lungs in a manner consistent with the priorities of the transplant community.

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Section: Discussionmentioning

confidence: 99%

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

Stewart¹,

Wood

Alcorn³

et al. 2020

Preprint

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“…Allograft allocation is a complex process. Currently, there are numerous factors such as the urgency of transplant, the lack of donor information, and the type of organ being allocated; because of this, it is often difficult to take HLA matching into account [3].…”

Section: The Effects Of Poor Hla Matchingmentioning

confidence: 99%

Epitope-Level Matching—A Review of the Novel Concept of Eplets in Transplant Histocompatibility

Renaldo¹,

Roa-Bautista²,

González-López³

et al. 2021

Transplantology

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The development of de novo donor-specific antibodies is related to the poor matching of the human leukocyte antigen (HLA) between donor and recipient, which leads to dismal clinical outcomes and graft loss. However, new approaches that stratify the risks of long-term graft failure in solid organ transplantation have emerged, changing the paradigm of HLA compatibility. In addition, advances in software development have given rise to a new structurally based algorithm known as HLA Matchmaker, which determines compatibility at the epitope rather than the antigen level. Although this technique still has limitations, plenty of research maintains that this assessment represents a more complete and detailed definition of HLA compatibility. This review summarizes recent aspects of eplet mismatches, highlighting the most recent advances and future research directions.

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“…In the past decade, the number of active patients on the waiting list in the United States has increased from 52,503 in 2009 to 95,052 in 2019 [ 2 ]. One of the most important challenges in kidney transplantation process is selecting the most appropriate recipient [ 3 ]. There are two general methods for kidney allocation: filtering and scoring.…”

Section: Introductionmentioning

confidence: 99%

“…In filtering, the waiting list is filtered step-by-step based on the important factors of kidney allocation. Finally, the expert selects the most appropriate recipient from the filtered list [ 3 ]. This method, despite its simplicity, is not a fair and equitable way for allocation, because as the waiting list is gradually reduced, the overall effect of all factors is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…This method, despite its simplicity, is not a fair and equitable way for allocation, because as the waiting list is gradually reduced, the overall effect of all factors is not considered. On the other hand, since the expert selects the final recipient, the likelihood of human emotions and errors can affect the selection of the recipient [ 3 ]. Most of the developing countries use this method, including Iran.…”

Section: Introductionmentioning

confidence: 99%

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Ranking patients on the kidney transplant waiting list based on fuzzy inference system

et al. 2022

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Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

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Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method

Cited by 15 publications

References 54 publications

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

A Revealed Preference Analysis to Develop Composite Scores Approximating Lung Allocation Policy in the U.S.

Epitope-Level Matching—A Review of the Novel Concept of Eplets in Transplant Histocompatibility

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

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