Faculty Bibliography

Redistricting, which means sharing organs in novel districts developed through mathematical optimization, has been proposed to reduce pervasive geographic disparities in access to liver transplantation. The economic impact of redistricting was evaluated with two distinct data sources, Medicare claims and the University HealthSystem Consortium (UHC). We estimated total Medicare payments under (i) the current allocation system (Share 35), (ii) full regional sharing, (iii) an eight-district plan, and (iv) a four-district plan for a simulated population of patients listed for liver transplant over 5 years, using the liver simulated allocation model. The model predicted 5-year transplant volumes (Share 35, 29,267; regional sharing, 29,005; eight districts, 29,034; four districts, 28,265) and a reduction in overall mortality, including listed and posttransplant patients, of up to 676 lives. Compared with current allocation, the eight-district plan was estimated to reduce payments for pretransplant care ($1638 million to $1506 million, p < 0.001), transplant episode ($5607 million to $5569 million, p < 0.03) and posttransplant care ($479 million to $488 million, p < 0.001). The eight-district plan was estimated to increase per-patient transportation costs for organs ($8988 to $11,874 per patient, p < 0.001) and UHC estimated hospital costs ($4699 per case). In summary, redistricting appears to be potentially cost saving for the health care system but will increase the cost of performing liver transplants for some transplant centers.

In June 2013, a change to the liver waitlist priority algorithm was implemented. Under Share 35, regional candidates with MELD ≥ 35 receive higher priority than local candidates with MELD < 35. We compared liver distribution and mortality in the first 12 months of Share 35 to an equivalent time period before. Under Share 35, new listings with MELD ≥ 35 increased slightly from 752 (9.2% of listings) to 820 (9.7%, p = 0.3), but the proportion of deceased-donor liver transplants (DDLTs) allocated to recipients with MELD ≥ 35 increased from 23.1% to 30.1% (p < 0.001). The proportion of regional shares increased from 18.9% to 30.4% (p < 0.001). Sharing of exports was less clustered among a handful of centers (Gini coefficient decreased from 0.49 to 0.34), but there was no evidence of change in CIT (p = 0.8). Total adult DDLT volume increased from 4133 to 4369, and adjusted odds of discard decreased by 14% (p = 0.03). Waitlist mortality decreased by 30% among patients with baseline MELD > 30 (SHR = 0.70, p < 0.001) with no change for patients with lower baseline MELD (p = 0.9). Posttransplant length-of-stay (p = 0.2) and posttransplant mortality (p = 0.9) remained unchanged. In the first 12 months, Share 35 was associated with more transplants, fewer discards, and lower waitlist mortality, but not at the expense of CIT or early posttransplant outcomes.

Whether the liver allocation system shifts organs from better performing organ procurement organizations (OPOs) to poorer performing OPOs has been debated for many years. Models of OPO performance from the Scientific Registry of Transplant Recipients make it possible to study this question in a data-driven manner. We investigated whether each OPO's net liver import was correlated with 2 performance metrics [observed to expected (O:E) liver yield and liver donor conversion ratio] as well as 2 alternative explanations [eligible deaths and incident listings above a Model for End-Stage Liver Disease (MELD) score of 15]. We found no evidence to support the hypothesis that the allocation system transfers livers from better performing OPOs to centers with poorer performing OPOs. Also, having fewer eligible deaths was not associated with a net import. However, having more incident listings was strongly correlated with the net import, both before and after Share 35. Most importantly, the magnitude of the variation in OPO performance was much lower than the variation in demand: although the poorest performing OPOs differed from the best ones by less than 2-fold in the O:E liver yield, incident listings above a MELD score of 15 varied nearly 14-fold. Although it is imperative that all OPOs achieve the best possible results, the flow of livers is not explained by OPO performance metrics, and instead, it appears to be strongly related to differences in demand.

Concerns have been raised that optimized redistricting of liver allocation areas might have the unintended result of shifting livers from better-performing to poorer-performing organ procurement organizations (OPOs). We used liver simulated allocation modeling to simulate a 5-year period of liver sharing within either 4 or 8 optimized districts. We investigated whether each OPO's net liver import under redistricting would be correlated with 2 OPO performance metrics (observed to expected liver yield and liver donor conversion ratio), along with 2 other potential correlates (eligible deaths and incident listings above a Model for End-Stage Liver Disease score of 15). We found no evidence that livers would flow from better-performing OPOs to poorer-performing OPOs in either redistricting scenario. Instead, under these optimized redistricting plans, our simulations suggest that livers would flow from OPOs with more-than-expected eligible deaths toward those with fewer-than-expected eligible deaths and that livers would flow from OPOs with fewer-than-expected incident listings to those with more-than-expected incident listings; the latter is a pattern that is already established in the current allocation system. Redistricting liver distribution to reduce geographic inequity is expected to align liver allocation across the country with the distribution of supply and demand rather than transferring livers from better-performing OPOs to poorer-performing OPOs.

U.S. organ allocation policy sequesters livers from deceased donors within arbitrary geographic boundaries, frustrating the intent of those who wish to offer the livers to transplant candidates based on medical urgency. We used a zero-one integer program to partition 58 donor service areas into between four and eight sharing districts that minimize the disparity in liver availability among districts. Because the integer program necessarily suppressed clinically significant differences among patients and organs, we tested the optimized district maps with a discrete-event simulation tool that represents liver allocation at a per-person, per-organ level of detail. In April 2014, the liver committee of the Organ Procurement and Transplantation Network (OPTN) decided in a unanimous vote of 22-0-0 to write a policy proposal based on our eight-district and four-district maps. The OPTN board of directors could implement the policy after the proposal and public-comment period.Redistricting liver allocation would save hundreds of lives over the next five years and would attenuate the serious geographic inequity in liver transplant offers.

Recent allocation policy changes have increased the sharing of deceased donor livers across local boundaries, and sharing even broader than this has been proposed as a remedy for persistent geographic disparities in liver transplantation. It is possible that broader sharing may increase cold ischemia times (CITs) and thus harm recipients. We constructed a detailed model of transport modes (car, helicopter, and fixed-wing aircraft) and transport times between all hospitals, and we investigated the relationship between the transport time and the CIT for deceased donor liver transplants. The median estimated transport time was 2.0 hours for regionally shared livers and 1.0 hour for locally allocated livers. The model-predicted transport mode was flying for 90% of regionally shared livers but for only 22% of locally allocated livers. The median CIT was 7.0 hours for regionally shared livers and 6.0 hours for locally allocated livers. Variation in the transport time accounted for only 14.7% of the variation in the CIT, and the transport time on average composed only 21% of the CIT. In conclusion, nontransport factors play a substantially larger role in the CIT than the transport time. Broader sharing will have only a marginal impact on the CIT but will significantly increase the fraction of transplants that are transported by flying rather than driving.