Abstract
BackgroundCurrent allocation mechanisms for liver transplantation (LT) overemphasize emergency, leading to poorer longtime outcomes. The utility was introduced to recognized outcomes in allocation. Recently, Molinari proposed a predictive outcome model based on recipient data.AimsThe aims of this study were to validate this model and to combine it with the utility to emphasize outcome in allocation.MethodsWe retrospectively analyzed 734 patients who were transplanted between January 2010 and December 2019. Points were assigned as in Molinari's model and the score sum was correlated with observed 90-day mortality. The utility was calculated as the product of 1-year survival times 3-month mortality on the waiting list. The weighting of different compounds was introduced, and utility curves were calculated. Model for End-Stage Liver Disease (MELD) scores according to maximal utility were determined.ResultsIn total, 120 patients (16.3%) had died within 90 days after LT. Higher MELD score, obesity, and hemodialysis prior to LT were confirmed risk factors. Overall survival was 83.8 and 77.4% after 90 days and 12 months, respectively. General utility culminated at MELD scores >35 in the overall population. Emphasizing the outcome shifted the maximal utility to lower MELD scores depending on Molinari scores.ConclusionsEmphasizing outcome, at least in certain recipient risk categories, might improve the longtime outcomes and might be integrated into allocation models.
Highlights
The ongoing scarcity of deceased donor organs for liver transplantation (LT) has led to the development of various allocation systems in the past decades [1–5]
Emphasizing on outcome weighing of 4/1 or 9/1 vs. emergency altered the shape of the utility curves and reduced the maximum value of utility to a Model for End-Stage Liver Disease (MELD) scores of 30 and 24, respectively
In the present study we aimed to 1. validate the recently published model of Molinari et al for the prediction of 90-day mortality after LT based solely on recipient parameters, 2. utilize this model to optimize the weighting balance between emergency and outcome considerations based on objective parameters
Summary
The ongoing scarcity of deceased donor organs for liver transplantation (LT) has led to the development of various allocation systems in the past decades [1–5]. Based on the principles of emergency, outcome, and fairness, the current system advocates Model for End-Stage Liver. Disease (MELD) scores to prioritize the sickest patients in need of immediate transplantation. Such emergency-driven allocation, based on the “sickest first” principle, shifts waiting list mortality [6] to the post-transplant period, resulting in poorer outcomes [7, 8]. Emphasizing Outcome in Liver Transplant recipient parameters in our study population were reassessed by logistic regression analysis, with 90-day mortality as our end point since the original artificial neural network was not available. Current allocation mechanisms for liver transplantation (LT) overemphasize emergency, leading to poorer longtime outcomes. Molinari proposed a predictive outcome model based on recipient data
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