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Updated: Apr 21, 2026

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Predicting Liver Transplant Capacity Using Discrete Event Simulation.

Hector Toro-Díaz1, Maria E Mayorga2, A Sidney Barritt3

  • 1Department of Industrial Engineering, Clemson University, SC (HTD)

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|November 14, 2014
PubMed
Summary
This summary is machine-generated.

Liver transplants (LTs) are declining due to poorer donor liver quality. A simulation predicts fewer LTs by 2030, highlighting the need for policy interventions to sustain this therapy.

Keywords:
forecastliver transplantationorgan donorssimulation

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Area of Science:

  • Transplantation Medicine
  • Organ Donation Research
  • Health Services Research

Background:

  • Liver transplant (LT) volume in the US has declined since 2006, contrary to increasing demand.
  • Decreased donor liver quality and increased organ discard are potential contributors to this decline.
  • Donor characteristics like older age, obesity, diabetes, and donation after cardiac death are increasingly prevalent.

Purpose of the Study:

  • To predict future liver transplant (LT) trends through 2030 using a discrete event simulation (DES) model.
  • To estimate the number of future donors required to maintain current LT volumes.
  • To assess the impact of improved reperfusion technology and forecast waiting list numbers.

Main Methods:

  • Construction of a discrete event simulation (DES) model.
  • Informed the model with current donor characteristics and data from the United Network for Organ Sharing (UNOS) database.
  • Projected LT trends, donor needs, and waiting list impacts through 2030.

Main Results:

  • The DES model quantifies a projected reduction in the number of LTs performed through 2030.
  • Identified the increasing prevalence of factors associated with higher liver discard rates.
  • Forecasted the number of patients on the waiting list and compared it to estimated LT volumes.

Conclusions:

  • The declining trend in liver transplants is projected to continue through 2030 without intervention.
  • The developed DES model can inform policy interventions to prevent a further decrease in LT volume.
  • This predictive model, based on epidemiological trends, is unique in forecasting future LT use.