Developing Prediction Models for Allograft Failure After Liver Transplantation

Prompt identification of allograft failure (AF) is highly desirable to address patients to liver retransplantation, in order to maximize results and preserve patients safety. Recently, sophisticated kinetic models became available, offering the possibility to predict 90-day AF with unprecedented accuracy, by computing data from the first 10 days after liver transplant (LT). The growing utilization of extended criteria and cardiac death donors stimulates the transplant community to further refine such predictive models and validate them on a larger scale population of patients across the nations. This study aims to develop new algorithms for the timely prediction of AF at 90 and 365 days using a prospective international cohort from high-volume centers, to validate them on a large retrospective cohort, to identify the best time for retransplantation, to stratify the risk of AF according to the graft type (i.e. DBD, ECD, DCD, LD), to weigh the effect of risk-mitigation strategies, and to assess the correlation with post-LT morbidity and mortality.