Goals

To achieve this goal, nationwide cohort studies have been conducted in the UK, Australia, and Spain. The Gender-Equity Model for Liver Allocation (GEMA) has been shown not only to correct disparities in access to transplantation in these countries, but also to significantly reduce waiting list mortality.

The GEMA model, obtained using a generalised additive multivariate Cox model, has been compared with the classical MELD and MELD-Na systems, as well as with the new MELD 3.0 in terms of discrimination, calibration and reclassification benefit. The superiority of GEMA over the MELD family models has been demonstrated in the UK, Australia and Spain, where the national cohort of the Spanish National Transplant Organisation (ONT) and the Spanish Liver Transplant Registry (RETH) has been used. GEMA results are supported by the experience of approximately 16,000 patients on the liver transplant waiting list. GEMA can be calculated automatically from the following link.  

The GEMA project includes additional actions aimed at optimising the performance of the model, as well as adapting it to the current challenges posed by the reality of liver transplantation. The usefulness of artificial intelligence, and specifically of evolutionary artificial neural networks, will be explored to increase the accuracy of the model’s predictions, especially in the most severe patients, such as those with acute on chronic liver failure (ACLF) or severe acute alcoholic hepatitis. In addition, the creation of a consensus document will be promoted within the Spanish Liver Transplant Society (SETH) to homogenise the criteria for prioritisation of the transplant waiting list in this country among the different hospitals. A web tool and smartphone app will be developed for the integrated management of the waiting list to facilitate the coordination of donation and transplant teams. Finally, the GEMA project includes training activities aimed at professionals and patients to raise awareness of the waiting list management model in Spain.