Applying Simulation Optimization to Improve the Efficiency of Organ Allocation

Transcription

1 Applying Simulation Optimization to Improve the Efficiency of Organ Allocation Nan Kong, Patricio Rocha Department of Industrial and Management Systems Engineering University of South Florida

2 Outline Motivation Matching Issues Logistics Issues Allocation Policy Allocation Policy: Alternatives Simulation Simulation Optimization

3 Motivation Allocation of organs in the U.S.: contentious issue. In particular, end-stage liver disease is the twelfth leading cause of death in the U.S. The only solution: Liver Transplantation. The amount of patients waiting for livers is significantly greater than the number of donated livers.

4 Motivation Allocation policies consider two main issues: Matching Issue Logistics Issue Policies frequently modified. A need for evaluation.

5 Matching Issues Size Blood Type Clinical Illness severity, Lab value, MELD Score Disease group Waiting time Demographical Age (Adult or Pediatric) Gender Location

6 Logistics Issues Originated because of Cold Ischemia time and organ decay. Differs from heart, kidney transplants. Resource Allocation Transportation/Logistics/Arrangement Country divided in 59 OPOs.

7 Logistics Issues

8 Allocation Policy GEOGRAPHICAL TIERS Local (OPO) Status 1B 5 MELD >= 15 Status 1A MELD < Regional 6 National 11

9 Allocation Policy Alternatives Two types of questions can be formulated: Clinical Is it convenient to separate the patient in just two groups in accordance with their MELD score (>=15, <15)? Is 15 a good cut-off point regarding MELD score? Demographical Is the geographical preference valid to improve the performance with the policy? What is the best region design?

10 Allocation Policy Alternatives Complications Point order system in each of the tiers. A patient could reject an organ. Each OPO is autonomous. Variance issues. In general, policies are too complex to be modeled analytically.

11 Allocation Policy Alternatives Region Design The most efficient sets of OPOs partitioning the country Alaska is in region 6 Hawaii is in region 5 Exponentially many potential combinations

12 Simulation Model Donor OPO Variance Patient Autonomy Patient Index Type (Living, Deceased) Age Ethnicity Gender Circumstance of Death Mechanism of Death Cause of Death Location Blood Type Size (Liver) Complication Matching Index Age Ethnicity Gender Location Waiting Time Blood Type Size (Liver) Status MELD Score Donor Type Recipient Blood Type Recipient Age Recipient Ethnicity Recipient Gender Location (OPO) Donor Relation Recipient Live or Deceased Years of Survival Status MELD Score Transplant Medical Outcome

13 Simulation Optimization Too many policy alternatives. For instance, number of possible regions. How it works Simulation Model Optimization Routine

14 Simulation Optimization Exploits computational advances. Improves modeling accuracy. Solution techniques vary according with the type of state space. Discrete: ranking and selection. Continuous: response surface, stochastic approximation.

15 Simulation Optimization The set partitioning problem involved in the simulation optimization routine is: max s. t. x r r R r R a ir c r x {0,1} r x r = 1 i r I R I : set of OPOs ; R: set of Regions

16 Simulation Optimization Method: Genetic Algorithms Metaheuristic Evolutionary algorithm Generates solutions at each iteration. Solutions are selected and then combined to get new solutions. Uses inheritance, mutation, crossover. Alternatives generated are evaluated using techniques such as ranking and selection.

17 Simulation Optimization Set-partitioning is highly constrained. Separate fitness and unfitness scores. Chu and Beasley (1998).

18 Future Work Equity consideration Other solution techniques: heuristics and exact solutions

19 Questions???