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.
Committee on Organ Procurement and Transplantation Policy, Institute of Medicine. Organ Procurement and Transplantation. Washington (DC): National Academy Press; 1999.
2.
United Network for Organ Sharing. US transplantation data. Available from: URL: http://www.unos.org/data/default.asp?displayType=usData. Accessed 23 December 2003.
3.
Pritzker AAB, Martin DL, Reust JS, et al.Organ transplantation policy evaluation. In: Alexopoulos C, Kang K, Lilegdon WR, Goldsman D, eds. Proceedings of the 1995 Winter Simulation Conference, 3-6 December 1995. Arlington (VA):1314-1323.
4.
Consad Research. An analysis of alternative national policies for allocating donor livers for transplantation. Final Report, Consad Research, Pittsburgh (PA); 1995.
5.
Klein RW, Dittus RS, Roberts SD, Wilson JR. Simulation modeling and health-care decision making. Med Decis Making. 1993;13(4):347-354.
6.
Tsevat J, Cook EF, Green ML, et al.Health values of the seriously ill. SUPPORT Investigators. Ann Intern Med. 1995;122(7):514-520.
7.
Bryce CL, Angus DC, Roberts MS, Tsevat J. Health status versus utilities of patients with end-stage liver disease. Qual Life Res. 2004;13:773-782.
8.
Law AM, Kelton WD. Simulation Modeling and Analysis. 3rd ed.New York: McGraw-Hill; 2000.
9.
Kreke J, Schaefer AJ, Angus DC, Bryce CL, Roberts MS. Incorporating biology into discrete event simulation models of organ allocation. In: Proceedings of the 2002 Winter Simulation Conference. Piscataway (NJ): Institute of Electrical and Electronics Engineers; 2002.
10.
Alagoz O, Roberts MS, Bryce CL, Schaefer AJ, Chang J, Angus DC. Incorporating clinical natural history in simulation models: empiric estimates of the progression of end-stage liver disease. Med Decis Making. Under review.
11.
United Network for Organ Sharing and the Division of Organ Transplantation, Bureau of Health Resources Development, Health Resources and Services Administration, US Department of Health and Human Services. Annual Report of the U.S. Scientific Registry for Transplant Recipients and the Organ Procurement and Transplantation Network—Transplant Data: 1988-1993. Bethesda (MD): US Department of Health and Human Services; 1994.
12.
United Network for Organ Sharing. Annual Report of the United States Scientific Registry for Transplant Recipients and the Organ Procurement and Transplant Network: Transplant Data 1989-1998. Richmond (VA): United Network for Organ Sharing; 1999.
13.
Cox DR. Regression models and life tables. J R Stat Soc [Ser B]. 1972;34:187-220.
14.
Roberts MS, Angus DC, Bryce CL, Valenta Z, Weissfeld L. Survival after liver transplantation in the United States: a disease-specific analysis of the UNOS database. Liver Transpl. 2004;10(7):886-897.
15.
Collett D.Modelling Survival Data in Medical Research. New York: Chapman & Hall; 1994.
16.
Roberts MS, Bryce CL, Schaefer A, Shechter S, Alagoz O, Angus D. Optimal organ allocation policy: a biologically-based simulation model [abstract]. Med Decis Making. 2003;23:575.
