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Matching donor to recipient in liver transplantation: Relevance in clinical practice.

ABSTRACT: Achieving optimum outcomes after liver transplantation requires an understanding of the interaction between donor, graft and recipient factors. Within the cohort of patients waiting for a transplant, better matching of the donor organ to the recipient will improve transplant outcomes and benefit the overall waiting list by minimizing graft failure and need for re-transplantation. A PubMed search was conducted to identify published literature investigating the effects of donor factors such as age, gender, ethnicity, viral serology; graft factors such as size and quality, recipient factors such as age, size, gender and transplant factors such as major or minor blood group incompatibility and immunological factors. We also report technical and therapeutic modifications that can be used to manage donor-recipient mismatch identified from literature and the authors' clinical experience. Multiple donor and recipient factors impact graft survival after liver transplantation. Appropriate matching based on donor-organ-recipient variables, modification of surgical technique and innovative peri-transplant strategies can increase the donor pool by utilizing grafts from marginal donors that are traditionally turned down.

SUBMITTER: Reddy MS

PROVIDER: S-EPMC3847943 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Matching donor to recipient in liver transplantation: Relevance in clinical practice.

Reddy Mettu Srinivas MS Varghese Joy J Venkataraman Jayanthi J Rela Mohamed M

World journal of hepatology 20131101 11

Achieving optimum outcomes after liver transplantation requires an understanding of the interaction between donor, graft and recipient factors. Within the cohort of patients waiting for a transplant, better matching of the donor organ to the recipient will improve transplant outcomes and benefit the overall waiting list by minimizing graft failure and need for re-transplantation. A PubMed search was conducted to identify published literature investigating the effects of donor factors such as age ...[more]

PMID: 24303088

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Project description:ObjectiveOne of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process.MethodsThis was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches.ResultsThe proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex.ConclusionsThese results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.

Dataset Information

Matching donor to recipient in liver transplantation: Relevance in clinical practice.

Publications

Matching donor to recipient in liver transplantation: Relevance in clinical practice.

Similar Datasets

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