tackling geographic disparity – redistribution of livers ryutaro hirose, md professor, surgery...

Tackling geographic disparity – Redistribution of livers

Ryutaro Hirose, MDProfessor, Surgery

Vice Chair, UNOS Liver Intestine committee

Region 5 CollaborativeLas Vegas, NV March 2015

History - UNOS

• 1984 – the US Congress passed NOTA (National Organ Transplant Act)– Established the OPTN (Organ Procurement and

Transplantation Network)– To maintain a national list of candidates– To establish a national systems to match organs

with candidates– To assist OPOs in the nationwide distribution of

organs EQUITABLY among transplant patients

Recent history – liver allocation v 2.0

• 2000 – US Department of Health and Human Services publishes Final Rule for the operation of the OPTN

• 2002 – Changes made to use the MELD score to allocate liver allografts

US Department of HHS Final Rule

• Allocation of cadaveric organs – the allocation polices:– Shall seek to achieve the best use of donated

organs;– Shall be designed to avoid wasting organs, to

avoid futile transplants, to promote patient access to transplantation, and to promote the efficient management of organ placement;

– Shall not be based on the candidate's place of residence or place of listing

Sickest first

• Setting priority rankings…that shall be ordered from most to least medically urgent. There shall be a sufficient number of categories (if categories are used) to avoid grouping together patients with substantially different medical urgency;

Allocation of scarce resources

• Demand vs. supply• Transplantation is one field that rations

medical care• Selection committees – Dialysis– Transplant candidacy

Organ allocation – balancing conflicting principles

• UTILITY– Maximizing usefulness of scarce resource

• EFFICIENCY– Cost– Value

• JUSTICE– Fairness– Equal access

Deceased donor organs

• Allocation scheme– How patients are ordered– LIVER: sickest first– LUNG: ratio of yrs added with transplant/mortality

pretxp without txp– Kidney: has largely been based on waiting time

MELD – sickest first

• MELD =(0.957 x LN(creatinine) + 0.378 x LN(bilirubin) +1.12 x LN(INR) +0.643) x 10

• Capped at 40• MELD score is relatively accurate in predicting

3 month waitlist mortality in liver transplant recipients

Liver – sickest first

• Status 1 – acute liver failure• MELD SCORE– Bilirubin– INR– Cr

• MELD EXCEPTIONS– Metabolic diseases– HCC

Map of OPO boundaries/DSA

UNOS regions

Organ distribution – SHARE 35

• Status 1 – UNOS REGION• MELD 35-40 – UNOS REGION• MELD 15-34 – local DSA• MELD >15 – UNOS REGION• Status 1 – National• MELD >15 National• MELD <15 Local, regional then National

Share 35

• An extension of regional sharing for status 1 patients

• Expanding access to livers for the sickest patients

• Pts with MELD scores have >33% risk of dying in a year

Share 35

• Has accomplished intended goals• More pts with MELD > 35 are getting

transplanted• So far, no decrease in outcomes• No increase in discards• Increase in cold ischemia time modest

Share 35

• BUT some other • Increased costs – Transplant centers (sicker pts)– OPOs

• Increased flyouts• Transportation logistics• Outside teams, team work (lack thereof)

• Gaming the system – Accepting multiple offers– Late declines

Share 35 – needs exposed

• Need more cooperation, communication• Need more transparency• Ability of OPOs to see how many offers a center

has accepted for a candidate (put limits)• Need more agile allocation system (DonorNET)– More offers than 3 centers at a time

• Change scheme so that livers don’t cross in the air for a single MELD point ?

HHS: Final Rule

• Neither place of residence or place of listing shall be a major determinant of access to a transplant

Metrics of disparity

• Supply/Demand ratio– Supply – All liver donors? eligible deaths? Potential

deaths? All deaths?– Demand – wait listed pts? Pts with ESLD?

• Variance in transplant rates across DSA/Regions• Variance in organ offers across DSA/Regions• Variance in drop off rates• Variance in waitlist mortality• Variance in MELD scores at transplant• Variance compared to single national list

Measuring Disparity

• Summative metrics– Over the whole country– On average, 40% of the pts with a MELD score of 38-

39 die within 90 days• Disparity metric– Range across the country by OPO/DSA– In some DSA’s 18% of pts with MELD 38-39 die within

90 days– In others 82% die– Variance in death across the country by DSAs

Transplant rates by MELD

Waitlist deaths by MELD

Alternatives

• Different Districts (super-regions), maintaining OPO boundaries as a guide to redistricting

• Concentric circles around a donor hospital• Compare to single national list – full national

sharing (lowest disparity)

Comparing designs/maps

• Alternative maps generated by contraints and algorithm• LSAM simulation models of allocation policy changes

• Limitations of LSAM– Changes in behavior impossible to model– Listing criteria/Organ acceptance patterns– OPO aggressiveness– Hard to predict, not included in models

Redistricting objectives

• Reduce/Minimize total disparity– Difference between the # of organs an area should

have (if organ went to highest MELD pt) – organs a region has

– Minimize sum of these disparities across all regions

• Subject to constraints– The minimal disparity is achieved by complete

national sharing

Redistricting constraints

• At least 4 and no more than 8 districts• Minimum # of centers per district: 6• Maximal travel time between DSAs in same

Region: 4-5 hours• Summative metric: the total # of waitlist

deaths under redistrcting must not be higher than current system

Outputs from LSAM - Metrics

• SUMMATIVE– Total deaths– Waitlist deaths– Avg transport times– % transportable by car– Avg transport distance– % organs to pts with

MELD>25

• DISPARITY– Variance of MELD score

at transplant across DSAs

7 regions, 3 hour transport

4 region

Comparison to concentric circles

• Concentric circle – first allocation within circle of 500 miles

• Advantage – simple, transparent• Disadvantage – not superior to optimized

maps for decreasing disparity

Existing geographic disparity

4 district map reduces disparity

NEXT LSAM request

• Add 3 or 5 MELD points for local candidates• Local defined by a circle around a donor

hospital of 150 vs. 250 miles

• To avoid livers flying for differences of 1-2 MELD points (some local priority)

• Will examine all metrics of disparity

Liver forum – ad hoc subcommittees

• Metrics of diparity• Logistics• Finances

• Increased donation and utilization

Potential proposals from subcommittees

• Increase transparency – allow OPOs to see all offers a specific candidate

• Limit acceptances to 2 livers per candidate• Increase # of offers (from 3) to many more on

donorNET• Expedited placement – More broad offers

immediately for suboptimal donors• Track acceptance patterns vs. listing criteria for

centers

The US - EXTREMES

• Currently 28% of The US population lives in a DSA where median MELD at transplant is >29 or <20

• After redistricting only 6% of the population will live in DSAs where median MELD scores are that extreme

Summary

• Implementation of MELD largely fulfilled ‘sickest first’

• Significant geographic disparity exists – policy change is hard to implement– Political forces– Self-interest: fears of transplant centers– Concerns about disparate OPO performance– Concerns about increasing distances, costs