The success of orthotopic liver transplantation (OLT) has created a demand which exceeds the availability of donor livers, medical capacities, and financial resources. It is estimated that the number of potential OLT candidates is 4,000-10,000 per year. However, donor availability currently meets approximately 1/3 of the estimated need. Because we cannot transplant all patients with end-stage liver disease, we must develop rational guidelines so that those who are transplanted have the greatest opportunity for a successful outcome. To meet this challenge we have developed and validated survival models for primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC), and determined the efficacy of OLT for patients with these diseases. We now plan to: 1) develop methods for patient selection and OLT timing that maximize patient and graft survival; 2) determine the effect of optimal selection and timing on cost and quality of life. To achieve these goals we will: 1) combine our established PBC/PSC natural history models with proposed models of patient and graft survival and post surgical complications; 2) measure the change in quality of life pre and post OLT and determine the variables that influence quality of life; and 3) determine the cost and economic utility of OLT. A major benefit of our research will be to enable patients to become better informed participants in their health care. Our models will predict a patient's survival, surgical complications, quality of life, and costs, thereby allowing patients and health care providers to make more informed health care decisions. Ultimately these models will optimize timing of OLT by taking into consideration the patient's current status and risk of disease progression versus operative mortility , cost, and quality of life. Finally, this proposal establishes a research framework that may be applied to other categories of liver disease and other transplanted organs. Our proposal combines the strengths of four major OLT centers: University of Pittsburgh, Baylor University Medical Center, University of Washington at Seattle, and the Mayo Clinic. Combining these databases will assure geographic diversification and adequate patient numbers for mathematical modeling.
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