Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Research article

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

Authors: Nasrin Taherkhani, Mohammad Mehdi Sepehri, Shadi Shafaghi, Toktam Khatibi

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

Login to get access

Abstract

Background

Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence, the purpose of this study is to identify and weight the kidney allocation criteria considering the balance between utility and equity.

Methods

To do this, a new fuzzy hybrid approach is proposed, which consists of two steps: In the first step, Fuzzy Delphi Method (FDM) is used to identify the effective criteria in the kidney allocation algorithm. In the second step, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is employed to determine the weight of the criteria.

Results

The results showed that the highest weight belongs to “Medical emergency” criterion and the lowest weight to “5 HLA mismatches”, which is similar to Euro-transplant kidney allocation system (ETKAS). The developed method is evaluated in two steps. First, the proposed model is implemented using a real case study from the Iranian Kidney Allocation System. It was shown that the proposed model has the potential to improve allocation outcome. Second, the proposed model’s superiority to the current model is approved by the experts using the results display in the profile matrix. Finally, sensitivity analysis is performed to check the robustness of the proposed model.

Conclusions

This paper contributes to the kidney allocation literature by doing the following: (a) developing a comprehensive framework for identification and weightings of criteria for kidney allocation, (b) using, for the first time, the IF-AHP technique to consider hesitancy of decision makers and uncertainty in organ allocation, and (c) proposing an appropriate framework for the countries that intend to improve or modify their organ allocation system.
Appendix
Available only for authorised users
Literature
1.
2.
3.
Stegall MD, et al. Why do we have the kidney allocation system we have today? A history of the 2014 kidney allocation system. Hum Immunol. 2017;78(1):4–8.PubMedCrossRef
4.
Freeman RB, et al. Moving kidney allocation forward: the ASTS perspective. Am J Transplant. 2009;9(7):1501–6.PubMedCrossRef
5.
6.
Stegall MD. Developing a new kidney allocation policy: the rationale for including life years from transplant. Am J Transplant. 2009;9(7):1528–32.PubMedCrossRef
7.
Mirhosseini SM, Najafizadeh K, Shafaghi S. Establishing standard heart allocation protocol in Iran. J Heart Lung Transplant. 2015;34(4):S157–8.CrossRef
8.
Baskin-Bey ES, Nyberg SL. Matching graft to recipient by predicted survival: can this be an acceptable strategy to improve utilization of deceased donor kidneys? Transplant Rev. 2008;22(3):167–70.CrossRef
9.
Stegall MD. The Right Kidney for the Right Recipient: The Status of Deceased Donor Kidney Allocation Reform. Semin Dialysis. 2010;23:248–52.PubMedCrossRef
10.
David I, Yechiali U. Sequential assignment match processes with arrivals of candidates and offers. Probability in the Engineering and Informational Sciences. 1990;4(4):413–30.CrossRef
11.
Yuan Y, et al. An internet-based fuzzy logic expert system for organ transplantation assignment. Int J Healthc Technol Manag. 2001;3(5-6):386–405.CrossRef
12.
Gundogar E, et al. Fuzzy Organ Allocation System for Cadaveric Kidney Transplantation. Transplantation. 2005;80:1648–53.PubMedCrossRef
13.
Cruz-Ramírez M, et al. Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif Intell Med. 2013;58(1):37–49.PubMedCrossRef
14.
Bertsimas D, Farias VF, Trichakis N. Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper Res. 2013;61(1):73–87.CrossRef
15.
Tong A, et al. Patient preferences for the allocation of deceased donor kidneys for transplantation: a mixed methods study. BMC Nephrology. 2012;13(1):18.
16.
Al-Ebbini L, Oztekin A, Chen Y. FLAS: fuzzy lung allocation system for US-based transplantations. Eur J Oper Res. 2016;248(3):1051–65.CrossRef
17.
Ahmadvand S, Pishvaee MS. An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach. Health Care Manag Sci. 2018;21(4):587–603.PubMedCrossRef
18.
La Scalia G, et al. Multi-criteria decision making support system for pancreatic islet transplantation. Expert Syst Appl. 2011;38(4):3091–7.CrossRef
19.
Dongping G, et al. Long-term factors influencing Survial after kidney transplantation. Transplant Proc. 2013;45(1):129–33.PubMedCrossRef
20.
Nosotti M, et al. Selection of candidates for lung transplantation: the first Italian consensus statement. Transplant Proc. 2017;49(4):702–6.PubMedCrossRef
21.
Ryan Cook D, Staschak S, Green WT. Equitable allocation of livers for orthotopic transplantation: An application of the Analytic Hierarchy process. Eur J Operation Res. 1990;48(1):49–56.CrossRef
22.
Koch TJTM. Normative and prescriptive criteria: The efficacy of organ transplantation allocation protocols. Theor Med. 1996;17(1):75–93.PubMedCrossRef
23.
Koch T, Rowell M. A pilot study on transplant eligibility criteria: valuing the stories in numbers. Pediatr Nurs. 1997;23(2):160–6.PubMed
24.
Koch T, Rowell MJTM. And bioethics, the dream of consensus: finding common ground in a bioethical context. Theor Med Bioethics. 1999;20(3):261–73.CrossRef
25.
Saha, C., et al., Selection and matching of kidney donor and recipient using fuzzy techniques and analytic hierarchy process. 2012.
26.
Lin CS, Harris SL. A unified framework for the prioritization of organ transplant patients: analytic hierarchy process, sensitivity and multifactor robustness study. J Multicrit Decis Anal. 2013;20(3–4):157–72.CrossRef
27.
Tavana M, et al. An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Appl Soft Comput. 2016;40:544–57.CrossRef
28.
Büyüközkan G, Çifçi G. A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl. 2012;39(3):2341–54.CrossRef
29.
Lupo T. A fuzzy framework to evaluate service quality in the healthcare industry: an empirical case of public hospital service evaluation in Sicily. Appl Soft Comput. 2016;40:468–78.CrossRef
30.
31.
Huang Y-P, et al. Health symptom checking system for elderly people using fuzzy analytic hierarchy process. Appl Syst Innov. 2018;1:10.CrossRef
32.
Otay İ, et al. Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowl-Based Syst. 2017;133:90–106.CrossRef
33.
Büyüközkan G, Feyzioğlu O, Gocer F. Evaluation of hospital web services using intuitionistic fuzzy AHP and intuitionistic fuzzy VIKOR. In: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); 2016.
34.
Abdullah L, Najib L. A new preference scale of intuitionistic fuzzy analytic hierarchy process in multi-criteria decision making problems. J Intell Fuzzy Syst. 2014;26:1039–49.
35.
Sadiq, R., S.J.S.E.R. Tesfamariam, and R. Assessment, Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). 2009. 23(1): p. 75-91.
36.
Kardaras DK, Karakostas B, Mamakou XJ. Content presentation personalisation and media adaptation in tourism web sites using fuzzy Delphi method and fuzzy cognitive maps. Expert Syst Appl. 2013;40(6):2331–42.CrossRef
37.
38.
Hsu C-C, Sandford BA. The Delphi Technique: Making Sense Of Consensus. Pract Assess Res Eval. 2007;12.
39.
Kannan D, de Sousa Jabbour AB, Jabbour CJ. Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. Eur J Oper Res. 2014;233(2):432–47.CrossRef
40.
41.
Ishikawa A, et al. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets Syst. 1993;55(3):241–53.CrossRef
42.
Bouzon M, et al. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resour Conserv Recycl. 2016;108:182–97.CrossRef
43.
Hsu P-F, Chen BY. Developing and Implementing a Selection Model for Bedding Chain Retail Store Franchisee Using Delphi and Fuzzy AHP. Quality Quantity. 2007;41(2):275–90.CrossRef
44.
Cheng J-H, Lee C-M, Tang C-H. An application of fuzzy Delphi and fuzzy AHP on evaluating wafer supplier in semiconductor industry. WSEAS Trans Info Sci and App. 2009;6(5):756–67.
45.
Wu C-H, Fang WC. Combining the Fuzzy Analytic Hierarchy Process and the fuzzy Delphi method for developing critical competences of electronic commerce professional managers. Quality Quantity. 2011;45(4):751–68.CrossRef
46.
Ma Z, et al. Constructing road safety performance indicators using fuzzy Delphi method and Grey Delphi method. Expert Syst Appl. 2011;38(3):1509–14.CrossRef
47.
Bendersky M, David I. The full-information best-choice problem with uniform or gamma horizons. Optimization. 2016;65(4):765–78.CrossRef
48.
Luscalov S, et al. Algorithm with heuristics for kidney allocation inTransplant information system. Cham: Springer International Publishing; 2017.
49.
50.
Gupta A, et al. Pretransplant Donor-Specific Antibodies in Cytotoxic Negative Crossmatch Kidney Transplants: Are They Relevant? Transplantation. 2008;85(8):1200–4.PubMedCrossRef
51.
Fuggle SV, Martin S. Tools for Human Leukocyte Antigen Antibody Detection and Their Application to Transplanting Sensitized Patients. Transplantation. 2008;86:384–90.PubMedCrossRef
52.
53.
54.
55.
56.
Briceno J, et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J Hepatol. 2014;61(5):1020–8.PubMedCrossRef
57.
Klair T, et al. Outcomes of adult dual kidney transplants by KDRI in the United States. Am J Transplant. 2013;13(9):2433–40.PubMedCrossRef
58.
Yoo KD, et al. A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Sci Rep. 2017;7(1):8904.PubMedPubMedCentralCrossRef
59.
Brown TS, et al. Bayesian modeling of pretransplant variables accurately predicts kidney graft survival. Am J Nephrol. 2012;36(6):561–9.PubMedCrossRef
60.
Shaikhina T, et al. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomedical Signal Processing and Control. 2019;52:456–62.CrossRef
61.
Hamidi O, et al. Identifying important risk factors for survival in kidney graft failure patients using random survival forests. Iran J Public Health. 2016;45(1):27–33.PubMedPubMedCentral
62.
Premaud A, et al. An adjustable predictive score of graft survival in kidney transplant patients and the levels of risk linked to de novo donor-specific anti-HLA antibodies. PLoS One. 2017;12(7):e0180236.PubMedPubMedCentralCrossRef
63.
64.
Foucher Y, et al. A clinical scoring system highly predictive of long-term kidney graft survival. Kidney Int. 2010;78(12):1288–94.PubMedCrossRef
65.
Saaty TL. The Analytic Hierarchy Process. New York: McGraw-Hill; 1980.
66.
Chen C-W, et al. Developing indicators for sustainable campuses in Taiwan using fuzzy Delphi method and analytic hierarchy process. J Clean Prod. 2018;193:661–71.CrossRef
67.
Courtney AE, Maxwell AP. The challenge of doing what is right in renal transplantation: balancing equity and utility. Nephron Clin Pract. 2009;111(1):c62–7 discussion c68.PubMedCrossRef
68.
Williams A. Intergenerational Equity: An Exploration of the ‘Fair Innings’ Argument. Health Econ. 1997;6(2):117–32.PubMedCrossRef
69.
Saaty TL. Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. Pittsburg: RWS Publications; 2000. ISBN-10: 0962031763. ISBN-13: 978-0962031762.
Metadata
Title
Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method
Authors
Nasrin Taherkhani
Mohammad Mehdi Sepehri
Shadi Shafaghi
Toktam Khatibi
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0892-y

Other articles of this Issue 1/2019

BMC Medical Informatics and Decision Making 1/2019 Go to the issue

Research article

Knowledge, attitude, and use of mHealth technology among students in Ghana: A university-based survey

Research article

Applying machine learning to predict future adherence to physical activity programs

Research article

Accurate and rapid screening model for potential diabetes mellitus

Research article

Chronic wound assessment and infection detection method

Research article

“Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model”

Research article

An integrated model to evaluate the impact of social support on improving self-management of type 2 diabetes mellitus