Post on 27-Mar-2015
An Agent-based Simulation Model to Analyze the US Liver Allocation Policy
Yu Teng, Nan KongWeldon School of Biomedical Engineering
Purdue UniversityWest Lafayette, IN
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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
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Organ Transplantation
• Living donor vs. Deceased donor
ESLD Patient
Transplant Waiting List
Living Donor
Deceased Donor
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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
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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 RatioEquity:
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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
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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)
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Current Allocation PolicyVery sick
Healthy
High
LowLocal (OPO)
Regional
National
Status 1
MELD 6-14
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1
3
5
4 6 HealthLevel
MELD
MELD 15-40Local
Regional
National
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8
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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.
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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
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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
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Conceptual Model
Patient Generator
Pre-transplant Medical History
Organ Generator
Matching Algorithm
Post-transplant Medical History
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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
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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.
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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
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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.
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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
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Agent Behavior in an “Assignment Cycle”
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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
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System Outcome Performances
Division Current CombinationLocal
National National S1 Extension
Death Number 972.2 979 1016.7 1010.2 1723.8 1092
Ave CIT (hr) 10.04 10.07 10.16 10.44 13.13 10.12Ave Patient Survival (%) 87.27 87.22 87.12 86.32 81.37 87.20Ave Graft
Survival (%) 80.85 80.75 80.55 79.32 70.74 80.67
Death/Tx Ratio 0.144 0.146 0.151 0.151 0.258 0.162
Ave Distance 47.53 59.48 87.41 182.96 1077.5 75.31
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Strategy Comparison: Paired-t Tests
P valueDivision vs.
CurrentCurrent vs.
CombinationCombination vs. Local National
Local National vs. National
Current vs. S1 Extension
Death Number 0.349 0.026 0.345 0.000 0.000
Ave CIT 0.000 0.000 0.000 0.000 0.000Ave Patient
Survival 0.000 0.000 0.000 0.000 0.000
Ave Graft Survival 0.000 0.000 0.000 0.000 0.000
Death/Tx Ratio 0.249 0.028 0.467 0.000 0.000
Ave Distance 0.000 0.000 0.000 0.000 0.000
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Death vs. Tx Ratio
Current
Division Combination
[0,0.1) [.11,.12) [.12,.13) [.13,.14) [.14,.15) [.15,.16) [.16,.17) [.17,.18) [.18,.19) >=.19
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Organ Transport Distance
Current
Division Combination
[0,10) [10,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80) [80,90) >=90 miles
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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
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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
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Equity – Death/Tx Ratio• Regional level
• OPO level
Division Current Combination S1 Extension
Maximum 0.208 0.159 0.206 0.223
Minimum 0.107 0.140 0.105 0.111
Difference 0.101 0.019 0.101 0.112
Division Current Combination S1 Extension
Maximum 0.220 0.217 0.233 0.256
Minimum 0.065 0.081 0.074 0.065
Difference 0.155 0.137 0.159 0.191
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Equity – Ave Transport Distance• Regional level
• OPO level
Division Current Combination
Maximum 182.4 156.7 173.2
Minimum 1.593 47.96 1.978
Difference 180.8 108.8 171.2
Division Current Combination
Maximum 280.0 378.9 267.2
Minimum 0.649 14.78 1.559
Difference 279.3 364.1 265.6
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Future Research• Pre-transplant patient natural history• Post-transplant survival prediction
• A decentralized system: organ allocator’s autonomy
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Questions?
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