Evidence map›Paper›PMID 37925233›Full record

ArticleTransplantation proceedings2023

Evaluating Cost-Effectiveness in Using High-Kidney Donor Profile Index Organs.

Trevor A Ellison, Sunjae Bae, Eric K H Chow, Allan B Massie, Lauren M Kucirka, Kyle J Van Arendonk, Dorry L Segev

Open access · greenAbstract read
In one paragraph

Article in Transplantation proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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 at 4 institutions in 1 country.

Trevor A EllisonDepartment of Cardiothoracic Surgery, Genesis Healthcare, Zanesville, OH. Electronic address: trevorellison@gmail.com.
Sunjae BaeDepartment of Surgery, NYU Grossman School of Medicine, NY; Department of Population Health, NYU Grossman School of Medicine, NY.
Eric K H ChowRosalind Franklin University of Medicine and Science, North Chicago, IL.
Allan B MassieDepartment of Surgery, NYU Grossman School of Medicine, NY; Department of Population Health, NYU Grossman School of Medicine, NY.
Lauren M KucirkaDepartment of Obstetrics and Gynecology, University of North Carolina, NC.
Kyle J Van ArendonkDepartment of Surgery, Division of Pediatric Surgery, Medical College of Wisconsin, WI.
Dorry L SegevDepartment of Surgery, NYU Grossman School of Medicine, NY; Department of Population Health, NYU Grossman School of Medicine, NY.
New York University · USGenesis HealthCare · USMedical College of Wisconsin · USRosalind Franklin University of Medicine and Science · US

Funding

Patient Oriented Research in Solid Organ TransplantationK24AI144954 · NIAID · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SEGEV, DORRY L. · 2020 to 2024
$956k
Patient Oriented Research in Kidney Disease and Transplant SurgeryK24DK101828 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI SEGEV, DORRY L. · 2014 to 2018
$922k
Determinants of chronic kidney disease in African-American live kidney donorsK01DK101677 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI MASSIE, ALLAN B · 2015 to 2019
$789k
NIAID NIH HHS K24 AI144954NIDDK NIH HHS K01 DK101677NIDDK NIH HHS K24 DK101828
6 · The paper itself

Abstract

A more granular donor kidney grading scale, the kidney donor profile index (KDPI), has recently emerged in contradistinction to the standard criteria donor/expanded criteria donor framework. In this paper, we built a Markov decision process model to evaluate the survival, quality-adjusted life years (QALY), and cost advantages of using high-KDPI kidneys based on multiple KDPI strata over a 60-month time horizon as opposed to remaining on the waiting list waiting for a lower-KDPI kidney. Data for the model were gathered from the Scientific Registry of Transplant Recipients and the United States Renal Data System Medicare parts A, B, and D databases. Of the 129,024 phenotypes delineated in this model, 65% of them would experience a survival benefit, 81% would experience an increase in QALYs, 87% would see cost-savings, and 76% would experience cost-savings per QALY from accepting a high-KDPI kidney rather than remaining on the waiting list waiting for a kidney of lower-KDPI. Classification and regression tree analysis (CART) revealed the main drivers of increased survival in accepting high-KDPI kidneys were wait time ≥30 months, panel reactive antibody (PRA) <90, age ≥45 to 65, diagnosis leading to renal failure, and prior transplantation. The CART analysis showed the main drivers of increased QALYs in accepting high-kidneys were wait time ≥30 months, PRA <90, and age ≥55 to 65.

Indexed as

Kidney TransplantationAgedCost-Benefit AnalysisGraft SurvivalHumansKidneyMedicareRetrospective StudiesTissue DonorsUnited States

Identifiers

PMID37925233
PMCPMC10841655
OpenAlexW4388296535

What OpenQuestion holds

Textmetadata
LicenceTDM
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.