AIMC Topic: Graft Survival

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Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in EspaƱa [MADR-E]). The aim is to ...

Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Transplantation
BACKGROUND: The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritiz...

Minimally invasive kidney transplantation: perioperative considerations and key 6-month outcomes.

Transplantation
BACKGROUND: Minimally invasive approaches to kidney transplantation (KT) have been described recently. However, information concerning perioperative management in these patients is lacking. Accordingly, in the current study, we describe our periopera...