Reliable models that could predict outcome of liver transplantation (LT) may guide physicians to advise their patients of immediate and late survival chances and may help them to optimize organ use. The objective of this study was to develop user-friendly models to predict short and long-term mortality after LT in adults based on pre-LT recipient characteristics. The United Network for Organ Sharing (UNOS) transplant registry (n = 38,876) from 1987 to 2001 was used to develop and validate the model. Two thirds of patients were randomized to develop the model (the modeling group), and the remaining third was randomized to cross-validate (the cross-validation group) it. Three separate models, using multivariate logistic regression analysis, were created and validated to predict survival at 1 month, 1 year, and 5 years. Using the total severity scores of patients in the modeling group, a predictive model then was created, and the predicted probability of death as a function of total score then was compared in the cross-validation group. The independent variables that were found to be very significant for 1 month and 1 year survival were age, body mass index (BMI), UNOS status 1, etiology, serum bilirubin (for 1 month and 1 year only), creatinine, and race (only for 5 years). The actual deaths in the cross-validation group followed very closely the predicted survival graph. The chi-squared goodness-of-fit test confirmed that the model could predict mortality reliably at 1 month, 1 year, and 5 years. We have developed and validated user-friendly models that could reliably predict short-term and long-term survival after LT.
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