Evidence map›Paper›PMID 40126479›Full record

ArticleJAMA network open2025

Kidney Transplant Wait Times Under Waiting List Expansion Scenarios.

Jillian S Caldwell, Xingxing S Cheng, Glenn M Chertow, Jeremy D Goldhaber-Fiebert

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Jillian S CaldwellSchool of Medicine, Division of Nephrology, Stanford University, Stanford, California.
Xingxing S ChengSchool of Medicine, Division of Nephrology, Stanford University, Stanford, California.
Glenn M ChertowSchool of Medicine, Division of Nephrology, Stanford University, Stanford, California.
Jeremy D Goldhaber-FiebertDepartment of Health Policy, Stanford University, Stanford, California.

Funding

Professional Development CoreU2CDK133488 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Marsha J Treadwell · 2023 to 2026
$4.1M
Refining repeat screening for coronary artery disease in kidney transplant candidatesK23DK123410 · NIDDK · STANFORD UNIVERSITY · PI CHENG, XINGXING SHELLEY · 2020 to 2024
$826k
The Role of Pretransplant Services on Outcomes and Costs in Kidney TransplantationR03DK134795 · NIDDK · STANFORD UNIVERSITY · PI CHENG, XINGXING SHELLEY · 2023 to 2024
$244k
Incentive Payments and Practice Patterns in Dialysis: The Case of CalcimimeticsF32DK141532 · NIDDK · STANFORD UNIVERSITY · PI CALDWELL, JILLIAN SCOTT · 2024 to 2025
$172k
NIDDK NIH HHS F32 DK141532NIDDK NIH HHS K23 DK123410NIDDK NIH HHS L30 DK137293NIDDK NIH HHS R03 DK134795NIDDK NIH HHS U2C DK133488
6 · The paper itself

Abstract

Importance: Kidney transplantation offers survival benefits and superior quality of life compared with maintenance dialysis for patients with end-stage kidney disease (ESKD), but it is limited to approximately 25 000 patients annually in the United States. Expanding access to transplant could be accomplished by allowing more patients to join the waiting list or by increasing organ supply. Objective: To estimate how these interventions could affect transplant wait times. Design, Setting, and Participants: This decision-analytic study used a Markov model with a simulated cohort of transplant-eligible US patients over 10 years (2022-2032). Exposures: Three expansion strategies: waiting list expansion alone, waiting list expansion with deceased-donor transplant (DDT) expansion, and waiting list expansion with living-donor transplant (LDT) expansion. Relative 10%, 25%, 50%, and 100% expansions of the current deceased-donor organ supply and 25%, 50%, 100%, and 200% expansions of current living donation rates were simulated, modeling 2 degrees of waiting list expansion (10% and 50%) for each strategy. Main Outcomes and Measures: Median wait time to kidney transplant using Kaplan-Meier survival analysis. Results: There were a total of 662 190 transplant-eligible patients in the simulated cohort, with a mean (SD) age of 58.7 (14.7) years; 327 126 (49%) female individuals; and 269 082 (41%) Black, 163 028 (25%) Hispanic, 233 739 (35%) non-Hispanic White, and 78 496 (12%) Asian individuals and individuals with another race or ethnicity. Under the baseline strategy, median (IQR) wait time was 32.8 (13.1-66.4) months and increased to 36.8 (14.7-74.7) months and 52.6 (21.0-107.9) months for 10% and 50% waiting list expansion alone. DDT and LDT expansion strategies added 1911 to 20 035 organs. For 10% waiting list expansion, median (IQR) wait times ranged from 23.7 (9.3-47.8) months to 34.5 (13.9-74.7) months. For 50% waiting list expansion, median (IQR) wait times ranged from 34.2 (13.6-69.4) months to 49.4 (19.7-101.0) months. Conclusions and Relevance: In this decision-analytic model, expansion strategies without additional organ supply increased the median time to kidney transplant by nearly 2 years; 10% waiting list expansion required at least 2850 additional organs to shorten wait times. However, 50% waiting list expansion required at least 11 000 additional organs to approximate current wait times. Reduction in the deceased-donor organ nonuse rate alone is unlikely to meet the shortfall. Systems-level efforts to significantly increase deceased and living donation should be prioritized alongside increased access to the transplant waiting list.

Indexed as

Kidney Failure, ChronicKidney TransplantationTissue and Organ ProcurementWaiting ListsAdultAgedFemaleHumansLiving DonorsMaleMarkov ChainsMiddle AgedTime FactorsUnited States

Identifiers

PMID40126479
PMCPMC11933994

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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.