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Evaluating twenty-years of follow-up after orthotopic liver transplantation, best practice for Donor-recipient matching: What can we learn from the past era?

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World Journal of Transplantation 2016年第3期6卷 599-607页

作者： Niklas Buescher Daniel Seehofer Michael Helbig Andreas Andreou Marcus Bahra Andreas Pascher Johann Pratschke Wenzel Schoening Department of General Visceral and Transplantation Surgery Charité Campus Virchow Klinikum Department of General Visceral and Transplantation Surgery University Hospital

AIM To characterize major determinants of 20-year survival after liver transplantation(LT).METHODS This longitudinal single-institution study includes 313 consecutive patients who received a LT between 1988 and 1992. Pretransplant clinical characteristics and laboratory values were assessed and compared between 20-year survivors and non-survivors. Particular attention was paid to the Model for End-Stage Liver Disease(labM ELD)-score and the Eurotransplant donor Risk Index(ET-DRI) to unravel their impact on 20-year survival after LT.RESULTS Twenty-year survivors were significantly younger(44 vs 50 years, P = 0.001), more likely to be female(49% vs 36%, P = 0.03) and less likely to be obese at the time of LT(19% vs 32%, P = 0.011). Mean labM ELD-score(P = 0.156), rate of high-urgency LT(P = 0.210), coldischemia time(P = 0.994), rate of retransplantation(P = 0.12) and average donor age(28 vs 33 years, P = 0.099) were not statistically different. The mean estimated glomerular filtration rate was higher among survivors(P = 0.007). ET-DRI > 1.4(P = 0.020) and donor age ≥ 30 years(P donor and recipient conditions. However, mainly due to the current organ shortage, these ideal circumstances are rarely given; thus algorithms for Donor-recipient matching need to be refined, in order to enable a maximum benefit for the recipients of high quality as well as marginal organs.

关键词： Liver transplantation Long-term outcome Ideal recipient recipient characteristics Donor-recipient matching

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Artificial intelligence and liver transplantation:Looking for the best donor-recipient pairing

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Hepatobiliary & Pancreatic Diseases International 2022年第4期21卷 347-353页

作者： Javier Briceno Rafael Calleja César Hervás Unit of Liver Transplantation Department of General SurgeryHospital Universitario Reina SofíaCórdobaSpain Maimónides Institute of Biomedical Research of Córdoba(IMIBIC) CórdobaSpain Department of Computer Sciences and Numerical Analysis Universidad de CórdobaCórdobaSpain

Decision-making based on artificial intelligence(AI)methodology is increasingly present in all areas of modern medicine.In recent years,models based on deep-learning have begun to be used in organ transplantation.Taking into account the huge number of factors and variables involved in donor-recipient(DR)matching,AI models may be well suited to improve organ allocation.AI-based models should provide two solutions:complement decision-making with current metrics based on logistic regression and improve their predictability.Hundreds of classifiers could be used to address this problem.However,not all of them are really useful for D-R pairing.Basically,in the decision to assign a given donor to a candidate in waiting list,a multitude of variables are handled,including donor,recipient,logistic and perioperative variables.Of these last two,some of them can be inferred indirectly from the team’s previous experience.Two groups of AI models have been used in the D-R matching:artificial neural networks(ANN)and random forest(RF).The former mimics the functional architecture of neurons,with input layers and output layers.The algorithms can be uni-or multi-objective.In general,ANNs can be used with large databases,where their generalizability is improved.However,they are models that are very sensitive to the quality of the databases and,in essence,they are black-box models in which all variables are important.Unfortunately,these models do not allow to know safely the weight of each variable.On the other hand,RF builds decision trees and works well with small cohorts.In addition,they can select top variables as with logistic regression.However,they are not useful with large databases,due to the extreme number of decision trees that they would generate,making them impractical.Both ANN and RF allow a successful donor allocation in over 80%of D-R pairing,a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease,balance of risk score,and survival outcomes following liver tra

关键词： Donor-recipient matching Artificial intelligence Deep learning Artificial neural networks Random forest Liver transplantation outcome

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