A revealed preference analysis to develop composite scores approximating lung allocation policy in the U.S.

Area of Science:

• Transplantation Science
• Health Policy Analysis
• Biostatistics

Background:

• The current donor lung allocation system in the U.S. relies on a classification-based algorithm called the match system.
• Experts propose a continuous, points-based framework to enhance transparency and fairness in organ allocation.
• This study investigates the feasibility of approximating the existing lung allocation policy with a continuous, points-based system.

Purpose of the Study:

• To demystify the points-based approach to organ allocation policy.
• To quantify the relative importance of factors in current lung allocation policy.
• To propose a viable policy option adapting the current system to a continuous allocation framework.

Main Methods:

• Discrete choice modeling was applied to match run data.
• Ranked logistic regression models were estimated using adult and pediatric donor match runs from 2018.
• Key attributes included medical priority, candidate age, transplant center proximity, and blood type compatibility.

Main Results:

• Two composite scores (adult and pediatric) were developed, highly correlating with current policy rankings (Kendall's Tau ≈ 0.80, Spearman > 90%).
• Higher rankings were associated with greater medical priority, closer transplant center proximity, and identical blood type.
• Transplant center proximity emerged as the most significant attribute in the allocation models.

Conclusions:

• Revealed preference analysis yielded composite scores that capture current lung allocation policy while eliminating rigid classification boundaries.
• A continuous points-based system offers a potential improvement for utilizing donor lungs effectively.
• This approach aligns with transplant community priorities and enhances the organ allocation process.