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Bagheri Lankarani K, Honarvar B, Shafi Pour F, Bagherpour M, Erjaee A, Rouhezamin MR, Khorrami M, Amiri Zadeh Fard S, Seifi V, Geramizadeh B, Salahi H, Nikeghbalian S, Shamsaeefar A, Malek-Hosseini SA, Shirzadi S. Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study. J Biomed Phys Eng 2022; 12:591-598. [PMID: 36569570 PMCID: PMC9759643 DOI: 10.31661/jbpe.v0i0.2010-1212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 12/13/2020] [Indexed: 06/17/2023]
Abstract
BACKGROUND Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion. OBJECTIVE This current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization. MATERIAL AND METHODS In this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization. RESULTS Between all MELD types, MELD-Na was a stronger determinant of LT candidates' survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors. CONCLUSION Therefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.
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Affiliation(s)
- Kamran Bagheri Lankarani
- MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Behnam Honarvar
- MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farshad Shafi Pour
- PhD, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Morteza Bagherpour
- PhD, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Asma Erjaee
- MD, Department of Pediatrics, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Reza Rouhezamin
- MD, Trauma Research Center, Rajaei Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mojdeh Khorrami
- MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saeid Amiri Zadeh Fard
- MD, Department of Internal Medicine, Gastroenterology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Vahid Seifi
- MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bita Geramizadeh
- MD, Department of Pathology, Transplant Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Heshmatollah Salahi
- MD, Shiraz Organ Transplant Center, Shiraz University of Medical Sciences Shiraz, Iran
| | - Saman Nikeghbalian
- MD, Shiraz Organ Transplant Center, Shiraz University of Medical Sciences Shiraz, Iran
| | - Alireza Shamsaeefar
- MD, Shiraz Organ Transplant Center, Shiraz University of Medical Sciences Shiraz, Iran
| | | | - Saeedreza Shirzadi
- MD, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
- MD, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
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