Introduction to the Liver Simulated Allocation Model Presenter: - - PowerPoint PPT Presentation

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Introduction to the Liver Simulated Allocation Model

Presenter: John R. Lake, MD1 Authors: John R. Lake, MD1 Josh Pyke, PhD2 David Schladt, MS2

• 1. University of Minnesota; SRTR Senior Staff
• 2. Scientific Registry of Transplant Recipients

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Topics

• Structure of the Liver Simulated Allocation Model (LSAM)
• How LSAM is Used in Evaluating Proposed Policies
• Strengths and Limitations of LSAM
• Share35: LSAM projections and observed results
• Current redistricting results: where LSAM is used (and where

it is not)

• LSAM implications for interpreting the redistricting

projections

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Liver Simulated Allocation Model

• We used the Liver Simulated Allocation Model (LSAM) program to

simulate the performance of different scenarios.

• LSAM uses historical real-world data to estimate the interactions

between donors and candidates with new sets of allocation/distribution rules.

• LSAM is a discrete-event simulation program
• Draws from real donor and candidate data
• Models organ offers, organ acceptance, MELD changes over

time, waitlist survival, and post-transplant survival

• Simulates the uncertainty associated with these events
• Used extensively by SRTR/OPTN to predict the impact of many

proposed policy changes

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LSAM Conceptual Flow Chart

Pool of Donors

Includes actual donors with arrival varied on each run

Offer Acceptance Goes down ranked list until exhausted or accepted. Modeled given candidate & donor characteristics Not Transplanted

Post-Transplant Survival

Modeled given candidate & donor characteristics

Pool of Candidates

Includes candidates

• n the waitlist at start

& new candidates added throughout period.

Rule File Filters and Sorts candidates for specific donor organs at that time Transplanted

Candidates removed from pool upon removal from waitlist

• r death

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LSAM Strengths and Limitations

Strengths

• Draws on real transplant

data

• Simulates up to 5 years
• Multivariable acceptance

and survival models

• Can compare multiple

allocation and distribution systems

Limitations

• Predicts direction of change

between alternatives, not necessarily the magnitude

• f change
• Cannot account for changes

in listing or acceptance behavior

• Cannot predict outcomes on

a center-by-center basis

• Most recent input data files

use data through 2011

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How LSAM is Used to E valuate Proposed Allocation and Distribution Policies

• The committee generates a data request specifying the

proposed system and metrics of interest.

• LSAM rule files are generated which implement the proposed

policy.

• LSAM is used to simulate both current and proposed policies.
• Each simulation uses multiple iterations to characterize the

degree of variability in the results.

• The proposed policy results are compared to the current

policy results for the committee’s metrics of interest.

LSAM Predictions and Share 35

Methods

• LSAM was used to simulate 1 year of liver transplants under two

sets of allocation rules: pre- and post-Share35.

• LSAM simulations were repeated 10 times for each scenario.
• Patient inputs were based on candidates and donors from 2010.
• Both simulations used the same set of donors and candidates in
• rder to focus on the effects of the allocation rules; this is standard

practice with LSAM simulations.

• Observed data was extracted from the SRTR SAF for 2 periods, each

covering 1 year pre- and post-Share35 implementation on June 18, 2013.

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Share35 Comparison: Transplant and Sharing Rates

LSAM Pre-Share35 LSAM Share35 Actual Pre- Share35 Actual Share35 Total Livers 6698 6698 6706 7026 Transplanted 6074 (5997-6123) 90.7% 6102 (6079-6126) 91.1% 6028 (89.9% ) 6361 (90.5% ) Not Transplanted (Discarded) 624 (575-701) 9.3% 596 (572-619) 8.9% 678 (10.1% ) 665 (9.5% ) LSAM Pre-Share35 LSAM Share35 Actual Pre- Share35 Actual Share35 Local 4079 (4049-4131) 67.1% 3632 (3585-3660) 59.5% 4505 (74.7% ) 4014 (63.1% ) Regional 1735 (1687-1783) 28.6% 2047 (2010-2079) 33.5% 1229 (20.4% ) 2023 (31.8% ) National 261 (249-281) 4.3% 423 (408-446) 6.9% 295 (4.9% ) 322 (5.1% )

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Share35 Comparison: Waitlist mortality rates

LSAM Pre-Share35 LSAM Share35 Actual Pre-Share35 Actual Share35 S tatus 1A 457.7 (359.1-528.7) 502.1 (378.6-653.4) 541.5 395.6 S tatus 1B 47.1 (30.5-65.1) 44.8 (22-61.7) 44.6 43.5 >= MELD/ PELD 35 105.1 (94.3-112.6) 100.1 (88.6-109.5) 156.8 142.8 MELD/ PELD 30-34 28.6 (25.6-34.4) 29.4 (26.6-32.4) 15.5 18.2 MELD/ PELD 25-29 8 (7.2-8.4) 8 (7.5-8.5) 6.4 7.1 MELD/ PELD 15-24 3.4 (3.2-3.5) 3.3 (3.2-3.4) 5.5 6.1 < MELD/ PELD 15 0.5 (0.4-0.5) 0.5 (0.5-0.5) 2.6 2.6 Inactive 6.7 (6.6-6.9) 6.6 (6.4-6.7) 25.4 25.2

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LSAM and Share35 summary

• LSAM correctly predicted the direction of change for Share35

results in most categories of interest.

• The projected magnitude of change was smaller than
• bserved in some cases—LSAM projections were conservative.
• LSAM overestimated the proportion of regional transplants

pre-Share35 but still predicted an increase in regional sharing as seen in Share35.

• LSAM underestimated transplant rates and overestimated

death rates for candidates in the MELD 25-34 range.

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Where is LSAM used?

LSAM used? District map design No Supply/demand analysis No Proximity circles analysis Yes (disparity and summative metrics) Cost analysis Yes (simulated transplants and outcomes)

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LSAM Strengths and Limitations

Strengths

• Draws on real transplant

data

• Simulates up to 5 years
• Multivariable acceptance

and survival models

• Can compare multiple

allocation and distribution systems

Limitations

• Predicts direction of change

between alternatives, not necessarily the magnitude

• f change
• Cannot account for changes

in listing or acceptance behavior

• Cannot predict outcomes on

a center-by-center basis

• Most recent input data files

use data through 2011

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LSAM Offer Acceptance Model

• Higher travel time is correlated with lower acceptance for all

patients

• Nonlocal transplant is correlated with lower acceptance for all

but Status 1A patients

• This results from current organ allocation policy: most organs

traveling beyond the local DSA have been turned down by all candidates in the DSA

• All of the modeled scenarios use full regional or district-wide

sharing as the first level of allocation, so this effect is not likely to persist

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Transplant counts and organ sharing

Transplant counts correlate highly with local transplant percentage in the LSAM projections

Overall Transplant Counts Local Transplant Percentage

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Implications for redistricting

• The simulated scenarios share high-quality organs more

broadly than current policy because the first level of distribution is regional sharing.

• The LSAM acceptance model assumes that organ acceptance

probability declines with distance, but this effect is likely to decrease as acceptance behavior responds to wider sharing.

• LSAM projects a slight decrease in transplants due to

acceptance model effects.

• If transplant rates remain at historical levels, slightly more

transplants will be performed than LSAM predicts, and some

• utcome metrics will improve.

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Overall Summary

• LSAM simulates the liver allocation process. Uncertainty in
• ffer acceptance and patient outcomes is modeled using

historical data.

• LSAM correctly predicted the direction of change in most

categories of interest for Share35.

• The offer acceptance model is limited by its reliance on

historical behavior, especially the relationship between organ quality and transplant distance. This may cause LSAM to underestimate the number of transplants under redistricting and so to underestimate the benefits.

• Despite its limitations LSAM has been used extensively to

evaluate proposed liver allocation policies.

Supplemental Slides

Share35 Comparison: Recipient Age Groups

LSAM Pre-Share35 LSAM Share35 Actual Pre-Share35 Actual Share35 <1 105 (99-110) 1.7% 101 (94-113) 1.7% 112 (1.9%) 128 (2%) 1-5 Years 151 (139-163) 2.5% 157 (145-168) 2.6% 199 (3.3%) 179 (2.8%) 6-11 Years 70 (65-75) 1.2% 72 (67-74) 1.2% 71 (1.2%) 86 (1.4%) 12-17 Years 91 (83-98) 1.5% 92 (82-98) 1.5% 90 (1.5%) 88 (1.4%) 18-34 Years 361 (336-384) 5.9% 369 (351-381) 6% 295 (4.9%) 316 (5%) 35-49 Years 1010 (982-1021) 16.6% 1030 (1002-1061) 16.9% 910 (15.1%) 926 (14.6%) 50-64 Years 3603 (3547-3652) 59.3% 3600 (3567-3645) 59% 3514 (58.3%) 3624 (57%) 65+ Years 684 (657-703) 11.3% 681 (670-692) 11.2% 838 (13.9%) 1012 (15.9%)

Share35 Comparison: Recipient Race/ E thnicity

LSAM Pre-Share35 LSAM Share35 Actual Pre- Share35 Actual Share35 White 4183 (4141-4220) 68.9% 4182 (4134-4205) 68.5% 4204 (69.7%) 4347 (68.4%) African American 723 (698-741) 11.9% 733 (718-761) 12% 634 (10.5%) 689 (10.8%) Hispanic/Latino 816 (788-838) 13.4% 832 (818-852) 13.6% 848 (14.1%) 939 (14.8%) Asian 277 (263-295) 4.6% 279 (263-288) 4.6% 262 (4.3%) 301 (4.7%) Other 76 (70-82) 1.2% 76 (72-80) 1.2% 81 (1.3%) 83 (1.3%)

Share35 Comparison: Recipient Region

LSAM Pre-Share35 LSAM Share35 Actual Pre-Share35 Actual Share35 Region 1 211 (201-220) 3.5% 220 (213-226) 3.6% 223 (3.7%) 222 (3.5%) Region 2 786 (772-799) 12.9% 797 (784-810) 13.1% 693 (11.5%) 757 (11.9%) Region 3 1000 (984-1023) 16.5% 988 (974-1004) 16.2% 1078 (17.9%) 1156 (18.2%) Region 4 572 (562-584) 9.4% 584 (569-592) 9.6% 520 (8.6%) 580 (9.1%) Region 5 906 (889-916) 14.9% 932 (910-949) 15.3% 901 (14.9%) 957 (15%) Region 6 179 (169-190) 2.9% 172 (165-181) 2.8% 159 (2.6%) 188 (3%) Region 7 469 (459-476) 7.7% 475 (466-491) 7.8% 525 (8.7%) 492 (7.7%) Region 8 445 (436-457) 7.3% 440 (428-453) 7.2% 467 (7.7%) 480 (7.5%) Region 9 312 (304-327) 5.1% 322 (312-330) 5.3% 296 (4.9%) 316 (5%) Region 10 541 (524-553) 8.9% 537 (521-548) 8.8% 512 (8.5%) 539 (8.5%) Region 11 655 (642-671) 10.8% 635 (618-652) 10.4% 655 (10.9%) 672 (10.6%)