Evidence map›Paper›PMID 41915102›Full record

ArticleClinical transplantation2026

Development of a Donor-Based Predictive Model for Pancreas Non-Utilization Among Deceased Donor Transplants in the US.

Christie Rampersad, Jason Bau, Jeffrey Schiff, Trevor Reichman, S Joseph Kim

Abstract read
In one paragraph

Article in Clinical transplantation, 2026. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Christie RampersadAjmera Transplant Centre, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0001-8040-7908
Jason BauSchool of Medicine, Toronto Metropolitan University, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-1148-113X
Jeffrey SchiffAjmera Transplant Centre, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
Trevor ReichmanAjmera Transplant Centre, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-2303-1606
S Joseph KimAjmera Transplant Centre, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-5021-9831

Funding

Hennepin Healthcare Research Institute
6 · The paper itself

Abstract

backgroundPancreas non-utilization affects 25%-30% of deceased donor offers in the US. The European-derived Pre-Procurement Pancreas Suitability Score (P-PASS) has performed inconsistently, is not validated in North America, and requires less accessible inputs, highlighting need for a robust prediction tool for non-utilization.

methodsWe developed a predictive model for pancreas non-utilization using procured offers from the US SRTR (2003-2023). A multivariable logistic model used variables selected by causal diagram, LASSO, and univariable analyses. Model performance was evaluated using discrimination (AUC), calibration, and net reclassification improvement (NRI) versus a modified P-PASS. Internal validation used 1000 bootstrap resamples. Sensitivity analyses included recent-era offers, an all-comer cohort including non-procured offers, and excluding non-donor-related non-utilizations.

resultsAmong 30 757 pancreas offers, 26% were non-utilized. The model incorporated ten factors: older age, male sex, higher BMI, hypertension, gastrointestinal disease, smoking, donation after cardiac death, stroke as cause of death, elevated terminal creatinine, and abnormal lipase. This model achieved moderate discrimination (AUC 0.699; optimism-corrected 0.698), accurate calibration, Brier score 0.090, and 9.2% NRI. Sensitivity analyses confirmed robust performance, including AUC 0.8 among all-comer offers.

conclusionsThis model provides a practical, data-driven tool that improves identification of high-risk pancreata and may support more consistent, efficient utilization decisions. By demonstrating modifiable center-level variation and outperforming P-PASS in its first North American external validation, our findings offer a strong evidence base for targeted quality-improvement and policy initiatives to enhance pancreas transplant activity.

Indexed as

Donor SelectionPancreas TransplantationTissue and Organ ProcurementTissue DonorsAdultFemaleFollow-Up StudiesGraft SurvivalHumansMaleMiddle AgedPrediction AlgorithmsPrognosisRisk FactorsUnited Statesdiscarddonationnon‐utilizationpancreas transplantprediction model

Identifiers

PMID41915102
PMCPMC13037697

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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.