Risk Factors for No Valid Liver Graft. Multivariate Study Based on the Variables Included in the Donation Protocol of the National Trasplant Organisation

Tuñón, Juan Manuel Castillo; Gómez, Luis Miguel Marín; Artacho, G. Suárez; Franco, Carmen Cepeda; Bellido, C. Bernal; Martínez, J. M. Álamo; Ruiz, Francisco Javier Padillo; Bravo, Miguel Ángel Gómez

doi:10.1016/j.cireng.2020.06.011

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, the process for liver graft assessment can be improved; the rate of NTLG without pathological findings that confirm it is between 20.4%–35.5% ( 31 , 32 ). It refers to liver grafts rejected for LT that could have been used for that.…”

Section: Discussionmentioning

confidence: 99%

Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons

Pontes Balanza,

Castillo Tuñón,

Mateos García

et al. 2023

Front. Surg.

Self Cite

View full text Add to dashboard Cite

BackgroundThe complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.Material and methodLiver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.ResultsA total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.ConclusionThe tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.

show abstract

Section: Discussionmentioning

confidence: 99%