An Agent-based Simulation Model to Analyze the US Liver Allocation Policy

1. An Agent-based Simulation Model to Analyze the US Liver Allocation Policy Yu Teng, Nan KongWeldon School of Biomedical EngineeringPurdue UniversityWest Lafayette, IN

2. Background • Organ transplantation and allocation has been a contentious issue in the U.S. for decades. • End-stage liver disease (ESLD) is the 12th leading cause of death in the U.S.. • Liver transplantation is the only viable therapy at present. • Limitations of liver transplantation • Cost: $500,000 • Scarcity (in 2008): 17,000 patients in waiting list 11,000 new patients 7,000 donors • Perishable: cold ischemic time (CIT) 12-18 hours

3. Living Donor ESLD Patient Transplant Waiting List Organ Transplantation • Living donor vs. Deceased donor Deceased Donor

4. Construction of an Organ Allocation Policy • Medical urgency • Before 2002: status 1, 2A, 2B and 3 • After 2002: status 1, MELD 6-40 Model for End-Stage Liver Disease (MELD) • Geographic proximity • Transplant center, organ procurement organization (OPO),region, nation • Waiting time

5. Objectives of an Organ Allocation Policy Efficiency: • Pre-transplant: death in waiting list • Transplant: average CIT, average organ travel distance • Post-transplant: average patient survival, average graft survival • Death/Tx Ratio Equity:

6. Development of Organ Allocation Policy • “Local preference” policy • Reflect the efficiency consideration • Patients with greatest medical need within the ischemic restraints may not get a donor organ • “National sharing” policy • A notion of equity • Organ viability of livers cannot be ensured after long travels

7. Current Organ Transplantation and Allocation Policy • Geographic proximity • Local • 58 OPOs (50 recipient OPOs) • Regional • 11 regions • National • Medical urgency • Status 1 • MELD 6-40 (healthy-sick)

8. Very sick High Local Regional Low Healthy National Current Allocation Policy 7 Status 1 MELD 2 6 1 4 Health Level 3 Local (OPO) 8 MELD 6-14 5 Regional MELD 15-40 National 9

9. Algorithm for Status 1 Patients Algorithm for MELD Patients Priority: 1st: MELD 2nd: Blood Compatibility 3rd: Waiting time Priority is a function of blood compatibility and waiting time.

10. Introduction to ABMS • Agent-based modeling and simulation (ABMS) models a system as a collection of autonomous decision-making entities called agents. • Based on a set of rules, each agent individually assesses its situation, makes decisions and executes various behaviors. • Applications • Epidemiology • Marketing • Emergency response • Organizational decision making

11. Why Choose ABMS In our system, both patients and OPOs in the system can be naturally modeled as agents: • Decision for OPO • What is the optimal prioritization rule • Which region to join • Decision for patients • Where to register • Whether to accept an organ offer • Multiple Listing • ~ 3.3% patients choose Multiple-listing • Multi-listing patients gain significantly higher transplantation rates

12. Conceptual Model

13. Simulation Modeling • 58 OPO network • Initial patient waitlist • Uncorrelated: blood type, OPO, MELD • Correlated: waiting time, MELD • Organ arrival • Patient arrival • Patient disease progression • Time-independent state transition model • Patient removal • Removal rate dependent upon blood type, OPO and MELD. • CIT based on distance • Patient transplantation outcome: • function of CIT; • from the literature

14. Model Implementation Repast Symphony 1.1 • Developed in Argonne National Laboratory, Decision and Information Science Division. • Includes advanced point-and-click features for agent behavioral specification and dynamic model self-assembly. • The model components can be developed using any mixture of Java, Groovy and flowcharts.

15. Model Components • Agents: • Model Initializer • Organ-patient Generator • Organ key property: ABO (blood type), location and cold ischemia time • Patient key property: ABO, location, MELD and waiting time. • OPO • 2D continuous space • Networks: • Region Network • Transplant Network

16. Agent Behavior in Model Initialization • Model Initializer • generates 58 OPOs • OPO • generates the Region Network • Organ-patient Generator • generates patient waitlist on Jan. 1st, 2004.

17. Agent Behavior in an “Assignment Cycle” Tick 1 • Organ-patient Generator generates organs and patients Tick 2 to Tick 9 • OPO agents carry the core matching algorithm. • 8 behaviors to get different patient lists • 2 behaviors to select a patient on the list to offer the organ Tick 10 • Organ agents remove assigned organs in this cycle, and record cold ischemia time • Patient agents remove assigned agents, remove dead patients, change MELD and make records • OPO agents generate outputs

18. Agent Behavior in an “Assignment Cycle”

19. Experimental Design • 2 extreme cases: “local preference” and “national sharing” • 3 alternative region configurations: • An alternative medical urgency classification: • S1+MELD 35-40, MELD 15-34, MELD 6-14 Current Division Combination

20. System Outcome Performances

21. Strategy Comparison: Paired-t Tests

22. Death vs. Tx Ratio Current Division Combination

23. Organ Transport Distance Current Division Combination miles

24. Urgency Group Reclassification(Death vs. Tx Ratio) Current S1 Extension

25. OPO Level (Death vs. Tx Ratio)

26. Equity – Death/Tx Ratio • Regional level • OPO level

27. Equity – Ave Transport Distance • Regional level • OPO level

28. Future Research • Pre-transplant patient natural history • Post-transplant survival prediction • A decentralized system: organ allocator’s autonomy

29. Questions?