Improving Liver Allocation Using Optimized Neighborhoods.

Area of Science:

• Organ transplantation research
• Health services research
• Public health policy

Background:

• Geographic disparities in liver transplant access persist across the US.
• Current Organ Procurement and Transplant Network (OPTN) structure faces challenges with redistricting proposals.
• Existing systems struggle with variability in donor and listing rates and OPO connectivity.

Purpose of the Study:

• To introduce and evaluate a novel "neighborhood" approach for liver transplant allocation.
• To optimize OPO regions to better balance organ supply and demand.
• To compare the neighborhood model against current allocation and proposed redistricting.

Main Methods:

• Designed 58 "neighborhoods" for Donor Service Areas (DSAs) optimizing for supply-demand balance using 10 years of OPTN data.
• Developed a discrete-event simulator, LivSim, to approximate liver allocation model outcomes.
• Compared neighborhood allocation with current allocation and redistricting using metrics like mortality, MELD score, and transport distance.

Main Results:

• Neighborhoods reduced DSA-average MELD score standard deviation by 29%, saving ~65 lives annually compared to current allocation.
• Neighborhoods demonstrated smaller, more uniform average transport distances than redistricting.
• Neighborhoods saved an additional ~20 lives and reduced MELD score standard deviation by 17% compared to redistricting.

Conclusions:

• Neighborhood-based allocation offers a promising alternative to current and proposed redistricting methods.
• This approach improves equity and efficiency in liver transplantation.
• Policy considerations should include these neighborhood-based solutions for better transplant access.