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A Clinically Based Discrete-Event Simulation of End-Stage Liver Disease and the Organ Allocation Process

Department of Industrial Engineering, University of Pittsburgh, Pennsylvania

Cindy L. Bryce, PhD

Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania

Oguzhan Alagoz, MS

Department of Industrial Engineering, University of Pittsburgh, Pennsylvania

Jennifer E. Kreke, MS

Department of Industrial Engineering, University of Pittsburgh, Pennsylvania

James E. Stahl, MD, CM, MPH

MGH-Institute for Technology Assessment, Massachusetts General Hospital, Boston

Andrew J. Schaefer, PhD

Department of Industrial Engineering, Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania

Derek C. Angus, MD, MPH

Center for Research on Health Care, University of Pittsburgh, Pennsylvania, CRISMA Laboratory, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pennsylvania

Mark S. Roberts, MD, MPP

Department of Industrial Engineering, Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania

Background. The optimal allocation of scarce donor livers is a contentious health care issue requiring careful analysis. The objective of this article was to design a biologically based discrete-event simulation to test proposed changes in allocation policies. Methods. The authors used data from multiple sources to simulate end-stage liver disease and the complex allocation system. To validate the model, they compared simulation output with historical data. Results. Simulation outcomes were within 1% to 2% of actual results for measures such as new candidates, donated livers, and transplants by year. The model overestimated the yearly size of the waiting list by 5% in the last year of the simulation and the total number of pretransplant deaths by 10%. Conclusion. The authors created a discrete-event simulation model that represents the biology of end-stage liver disease and the health care organization of transplantation in the United States.

Key Words: liver transplantation • discrete-event simulation • simulation modeling • Monte Carlo simulation • organ allocation • patient survival • graft survival • policy analysis

Medical Decision Making, Vol. 25, No. 2, 199-209 (2005)
DOI: 10.1177/0272989X04268956


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