BACKGROUND: The use of expanded criteria for donors to expand the donor pool has increased the number of discarded liver grafts in situ. The aim of our study was to elaborate a prediction model to reduce the percentage of liver grafts discarded before the procuring team is sent out. METHODS: We analyzed the donor factors of 244 evaluated candidates for liver donation. We performed a multiple logistic regression to evaluate the probability of liver grafts discarded (PD). RESULTS: The PD was determined by use of 3 variables: age, pathological ultrasonography, and body mass index >30. The area under curve was 82.7%, and, for a PD of 70%, the false-positive probability was 1.2%. CONCLUSIONS: We have created a useful clinical prediction model that could avoid up to 20% of discarded liver grafts.
BACKGROUND: The use of expanded criteria for donors to expand the donor pool has increased the number of discarded liver grafts in situ. The aim of our study was to elaborate a prediction model to reduce the percentage of liver grafts discarded before the procuring team is sent out. METHODS: We analyzed the donor factors of 244 evaluated candidates for liver donation. We performed a multiple logistic regression to evaluate the probability of liver grafts discarded (PD). RESULTS: The PD was determined by use of 3 variables: age, pathological ultrasonography, and body mass index >30. The area under curve was 82.7%, and, for a PD of 70%, the false-positive probability was 1.2%. CONCLUSIONS: We have created a useful clinical prediction model that could avoid up to 20% of discarded liver grafts.
Authors: Andrew M Bishara; Dmytro S Lituiev; Dieter Adelmann; Rishi P Kothari; Darren J Malinoski; Jacob D Nudel; Mitchell B Sally; Ryutaro Hirose; Dexter D Hadley; Claus U Niemann Journal: Transplant Direct Date: 2021-09-27