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Predicting Function Delay with a Machine Learning Model: Improve the Long-term Survival of Pancreatic Grafts

Emanuel Vigia*, Luís Ramalhete, Rita Ribeiro, Inês Barros, Beatriz Chumbinho, Edite Filipe, Ana Pena, Luís Bicho, Ana Nobre, Sofia Carrelha, Sofia Corado, Mafalda Sobral, Jorge Lamelas, João Santos Coelho, Hugo Pinto Marques, Paula Pico, Susana Costa, Fernando Rodrigues, Miguel Bigotte Vieira, Rita Magriço, Patrícia Cotovio, Fernando Caeiro, Inês Aires6, Cecília Silva, Francisco Remédio, Américo Martin, Aníbal Ferreira, JorgePaulino, Fernando Nolasco

The impact of delayed graft function on outcomes following various solid organ transplants is well documented and addressed in the literature. Delayed graft function following various solid organ transplants is associated with both short- and long-term graft survival issues. In a retrospective cohort study including 106 patients we evaluated whether pancreas graft survival differs according to moment of insulin therapy following simultaneous pancreaskidney transplant. As a result, we aimed to identify possible risk factors and build a machine-learning-based model that predicts the likelihood of dysfunction following SPK transplant patients based on day zero data after transplant, allowing to enhance pancreatic graft survival. Feature selection by Relief algorithm yielded donor features, age, cause of death, hemoglobin, gender, ventilation days, days in ICU, length of cardiac respiratory arrest and recipient features, gender, long-term insulin, dialysis type, time of diabetes mellitus, vPRA pre-Tx, number of HLA-A mismatches and PRDI, all contributed to the models' strength.