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

• Main Authors: Darren E. Stewart, Dallas W. Wood, James B. Alcorn, Erika D. Lease, Michael Hayes, Brett Hauber, Rebecca E. Goff
• Format: Article
• Language: English
• Published: BMC 2021-01-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: Lung allocation; Organ transplantation; Rank ordered logistic regression; Organ Procurement and Transplantation Network (OPTN); Lung allocation score (LAS); Continuous allocation
• Online Access: https://doi.org/10.1186/s12911-020-01377-7

Abstract Background The patient ranking process for donor lung allocation in the United States is carried out by a classification-based, computerized algorithm, known as the match system. Experts have suggested that a continuous, points-based allocation framework would better serve waiting list candidates by removing hard boundaries and increasing transparency into the relative importance of factors used to prioritize candidates. We applied discrete choice modeling to match run data to determine the feasibility of approximating current lung allocation policy by one or more composite scores. Our study aimed to demystify the points-based approach to organ allocation policy; quantify the relative importance of factors used in current policy; and provide a viable policy option that adapts the current, classification-based system to the continuous allocation framework. Methods Rank ordered logistic regression models were estimated using 6466 match runs for 5913 adult donors and 534 match runs for 488 pediatric donors from 2018. Four primary attributes are used to rank candidates and were included in the models: (1) medical priority, (2) candidate age, (3) candidate’s transplant center proximity to the donor hospital, and (4) blood type compatibility with the donor. Results Two composite scores were developed, one for adult and one for pediatric donor allocation. Candidate rankings based on the composite scores were highly correlated with current policy rankings (Kendall’s Tau ~ 0.80, Spearman correlation > 90%), indicating both scores strongly reflect current policy. In both models, candidates are ranked higher if they have higher medical priority, are registered at a transplant center closer to the donor hospital, or have an identical blood type to the donor. Proximity was the most important attribute. Under a points-based scoring system, candidates in further away zones are sometimes ranked higher than more proximal candidates compared to current policy. Conclusions Revealed preference analysis of lung allocation match runs produced composite scores that capture the essence of current policy while removing rigid boundaries of the current classification-based system. A carefully crafted, continuous version of lung allocation policy has the potential to make better use of the limited supply of donor lungs in a manner consistent with the priorities of the transplant community.