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An Agent-based Simulation Model to Analyze the US Liver Allocation Policy Yu Teng, Nan Kong Weldon School of Biomedical Engineering Purdue University West.

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Presentation on theme: "An Agent-based Simulation Model to Analyze the US Liver Allocation Policy Yu Teng, Nan Kong Weldon School of Biomedical Engineering Purdue University West."— Presentation transcript:

1 An Agent-based Simulation Model to Analyze the US Liver Allocation Policy Yu Teng, Nan Kong Weldon School of Biomedical Engineering Purdue University West Lafayette, IN 1

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 2

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

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 4

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: 5

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 6

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) 7

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

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. 9

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 10

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 11

12 Conceptual Model 12

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 13

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. 14

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 15

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. 16

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 17

18 Agent Behavior in an Assignment Cycle 18

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 19

20 System Outcome Performances DivisionCurrentCombination Local NationalNationalS1 Extension Death Number972.29791016.71010.21723.81092 Ave CIT (hr)10.0410.0710.1610.4413.1310.12 Ave Patient Survival (%)87.2787.2287.1286.3281.3787.20 Ave Graft Survival (%)80.8580.7580.5579.3270.7480.67 Death/Tx Ratio0.1440.1460.151 0.2580.162 Ave Distance47.5359.4887.41182.961077.575.31 20

21 Strategy Comparison: Paired-t Tests P value Division vs. Current Current vs. Combination Combination vs. Local National Local National vs. National Current vs. S1 Extension Death Number0.3490.0260.3450.000 Ave CIT0.000 Ave Patient Survival0.000 Ave Graft Survival0.000 Death/Tx Ratio0.2490.0280.4670.000 Ave Distance0.000 21

22 Death vs. Tx Ratio Current DivisionCombination [0,0.1)[.11,.12)[.12,.13)[.13,.14)[.14,.15)[.15,.16)[.16,.17) [.17,.18)[.18,.19)>=.19 22

23 Organ Transport Distance Current DivisionCombination [0,10)[10,20)[20,30)[30,40)[40,50)[50,60)[60,70)[70,80)[80,90)>=90 miles 23

24 Urgency Group Reclassification (Death vs. Tx Ratio) Current [0,0.1)[.11,.12)[.12,.13)[.13,.14)[.14,.15)[.15,.16)[.16,.17) [.17,.18)[.18,.19)>=.19 S1 Extension 24

25 OPO Level (Death vs. Tx Ratio) [0,0.1)[.11,.12)[.12,.13)[.13,.14)[.14,.15)[.15,.16)[.16,.17) [.17,.18)[.18,.19)>=.19 25

26 Equity – Death/Tx Ratio Regional level OPO level DivisionCurrentCombinationS1 Extension Maximum0.2080.1590.2060.223 Minimum0.1070.1400.1050.111 Difference0.1010.0190.1010.112 DivisionCurrentCombinationS1 Extension Maximum0.2200.2170.2330.256 Minimum0.0650.0810.0740.065 Difference0.1550.1370.1590.191 26

27 Equity – Ave Transport Distance Regional level OPO level DivisionCurrentCombination Maximum182.4156.7173.2 Minimum1.59347.961.978 Difference180.8108.8171.2 DivisionCurrentCombination Maximum280.0378.9267.2 Minimum0.64914.781.559 Difference279.3364.1265.6 27

28 Future Research Pre-transplant patient natural history Post-transplant survival prediction A decentralized system: organ allocators autonomy 28

29 Questions? 29

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