Liver transplantation is considered lifesaving for selected patients with end-stage primary biliary cirrhosis (PBC). A mathematical model to predict survival in the patient with PBC who has not undergone transplantation would be valuable for improving selection of patients for and timing of transplantation and for providing control information for assessment of the efficacy of transplantation. The Cox regression method and data from 312 Mayo Clinic patients with PBC were used to develop a model based on age, total serum bilirubin, serum albumin, prothrombin time, and severity of edema. When cross-validated on an independent set of 106 Mayo patients, the model accurately predicted their survival. It was similar to two other published survival models in terms of risk measurement but had the advantage of not necessitating liver biopsy. The model was used to assess the efficacy of liver transplantation by comparing the Kaplan-Meier survival of 32 Mayo patients after transplantation with the average model prediction of survival without transplantation. Beyond 3 months after transplantation, Kaplan-Meier survival probabilities were significantly greater than control survival predicted by the model (P<0.001). Examples of using the model for aiding in selection of patients for and timing of transplantation are provided.
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