Evidence map›Paper›PMID 40061353›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Nonstandard coding of deceased-donor kidney out-of-sequence allocation leads to underrecognition of allocation deviations.

Emma G Tucker, Miko E Yu, Joel T Adler, David C Cron, Prateek V Sahni, Jesse D Schold, Sumit Mohan, Syed Ali Husain

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Funding

5/14 APOL1 Long-term Kidney Transplantation Outcomes Network (APOLLO) Clinical CenterU01DK116066 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Sumit Mohan, Deirdre Sawinski · 2017 to 2026
$2.5M
Using Decision Making Science to Optimize Deceased Donor Kidney AllocationK23DK133729 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI HUSAIN, SYED ALI · 2022 to 2025
$678k
NIDDK NIH HHS K23 DK133729NIDDK NIH HHS U01 DK116066
6 · The paper itself

Abstract

Out-of-sequence (OOS) allocation, the process by which organ procurement organizations (OPOs) can deviate from standard rank lists of potential recipients to expeditiously allocate deceased-donor kidneys, is rising in the U.S. We aimed to determine whether current OPO reporting practices obscure the extent of OOS allocation. Using match-run data for all U.S. deceased-donor kidney transplants from 2021-2023, we defined "miscoded" OOS (mOOS) allocation transplants as those with use of the 799 or 898 OPO- initiated refusal codes ("other, specify") with free text responses clearly indicating OOS allocation, and compared these to "explicit" OOS (eOOS) allocation, wherein OOS transplants are appropriately coded using refusal codes 861-863. We found that the prevalence of mOOS allocation increased from 2021 (122 transplants) to 2023 (430 transplants) and accounted for 12% of all OOS transplants by 2023. Organs allocated via mOOS had a lower median KDPI than those allocated via eOOS (51% vs 55%, p <0.01). While an increasing number of OPOs used mOOS throughout the study period, the practice remained concentrated overall, with 5 "outlier" OPOs performing 66% of mOOS allocations in 2023. These findings highlight the need for stricter oversight of organ allocation and underscore the responsibility of the OPTN to ensure proper data reporting.

Identifiers

PMID40061353
PMCPMC11888527

What OpenQuestion holds

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Registered trials

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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.