Evidence map›Paper›PMID 33407427›Full record

ArticleBMC medical informatics and decision making2021

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

Darren E Stewart, Dallas W Wood, James B Alcorn, Erika D Lease, Michael Hayes, Brett Hauber, Rebecca E Goff

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Influence of U.S. lung composite allocation score components on transplant candidate prioritization.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2026
    Article
  2. Article
  3. Article
  4. Lung transplant waitlist outcomes among ABO blood groups vary based on disease severity.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2023
    Article
  5. Article
  6. France's New Lung Transplant Allocation System: Combining Equity With Proximity by Optimizing Geographic Boundaries Through the Supply/Demand Ratio.Transplant international : official journal of the European Society for Organ Transplantation · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Darren E StewartUnited Network for Organ Sharing, Richmond, VA, USA. Darren.Stewart@unos.org.ORCID 0000-0002-6764-4842
Dallas W WoodResearch Triangle Institute International, Research Triangle Park, NC, USA.
James B AlcornUnited Network for Organ Sharing, Richmond, VA, USA.
Erika D LeaseDivision of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Washington, USA.
Michael HayesResearch Triangle Institute International, Research Triangle Park, NC, USA.
Brett HauberRTI Health Solutions, Research Triangle Park, NC, USA.
Rebecca E GoffUnited Network for Organ Sharing, Richmond, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsRank 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.

resultsTwo 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.

conclusionsRevealed 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.

Indexed as

Tissue and Organ ProcurementAdultChildHumansLungPolicyTissue DonorsUnited StatesWaiting ListsContinuous allocationLung allocationLung allocation score (LAS)Organ Procurement and Transplantation Network (OPTN)Organ transplantationRank ordered logistic regression

Identifiers

PMID33407427
PMCPMC7789710

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.