Accounting for Liver Transplant in Acute Liver Failure Research

Acute liver failure (ALF) is a rare but serious disease with challenging clinical decisions, including the possibility of liver transplantation. Although there is interest in predicting who will need a transplant, that outcome is difficult to define as the decision to transplant includes many extraneous factors. The majority of research in this setting focuses on identifying factors that can provide guidance on a patient's likelihood of survival without a liver transplant. The question that arises is whether death and transplant should be combined as a poor outcome or should alternative approaches be used to account for transplant in this setting. Furthermore, does the approach to incorporating transplant information impact the accuracy of predicting survival. We aim to compare alternative analytic methods for the ALF setting to provide guidance to the clinical research community on how to handle transplant when the outcome of interest is survival without a transplant.

Five analysis approaches are compared based on model performance using existing registry data from 2100 ALF patients: logistic regression with transplant as part of the outcome, logistic regression with transplant as a covariate, inverse probability weighting, survival analysis, and multiple imputation.

The various models exhibit comparable model fit with each providing advantages and challenges in implementation.

There are alternative modeling approaches in the ALF setting, leaving researchers with multiple valid options for how to include transplant when examining factors that may influence transplant-free survival.