Evidence map›Paper›PMID 38757051›Full record

ArticleTransplantation direct2024

Pancreas Transplantation Outcome Predictions-PTOP: A Risk Prediction Tool for Pancreas and Pancreas-Kidney Transplants Based on a European Cohort.

Gregor Miller, Donna P Ankerst, Michael W Kattan, Norbert Hüser, Felix Stocker, Serge Vogelaar, Milou van Bruchem, Volker Assfalg

Abstract read
In one paragraph

Article in Transplantation direct, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. One-Year HbA1c Predicts Long-Term Pancreas Graft Survival Following SPK Transplantation: A US Population Cohort Study.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    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

8 authors.

Gregor MillerDepartment of Surgery, Technical University of Munich (TUM), TUM School of Medicine and Health, TUM - Munich Transplant Center, Klinikum rechts der Isar, Munich, Germany.
Donna P AnkerstTechnical University of Munich (TUM), TUM School of Computation, Information and Technology, Garching, Germany.
Michael W KattanDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.
Norbert HüserDepartment of Surgery, Technical University of Munich (TUM), TUM School of Medicine and Health, TUM - Munich Transplant Center, Klinikum rechts der Isar, Munich, Germany.
Felix StockerDepartment of Surgery, Technical University of Munich (TUM), TUM School of Medicine and Health, TUM - Munich Transplant Center, Klinikum rechts der Isar, Munich, Germany.
Serge VogelaarEurotransplant International Foundation, Leiden, The Netherlands.
Milou van BruchemEurotransplant International Foundation, Leiden, The Netherlands.
Volker AssfalgDepartment of Surgery, Technical University of Munich (TUM), TUM School of Medicine and Health, TUM - Munich Transplant Center, Klinikum rechts der Isar, Munich, Germany.ORCID https://orcid.org/0000-0002-6907-5524

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: For patients with complicated type 1 diabetes having, for example, hypoglycemia unawareness and end-stage renal disease because of diabetic nephropathy, combined pancreas and kidney transplantation (PKT) is the therapy of choice. However, the shortage of available grafts and complex impact of risk factors call for individualized, impartial predictions of PKT and pancreas transplantation (PT) outcomes to support physicians in graft acceptance decisions. Methods: Based on a large European cohort with 3060 PKT and PT performed between 2006 and 2021, the 3 primary patient outcomes time to patient mortality, pancreas graft loss, and kidney graft loss were visualized using Kaplan-Meier survival curves. Multivariable Cox proportional hazards models were developed for 5- and 10-y prediction of outcomes based on 26 risk factors. Results: Risk factors associated with increased mortality included previous kidney transplants, rescue allocations, longer waiting times, and simultaneous transplants of other organs. Increased pancreas graft loss was positively associated with higher recipient body mass index and donor age and negatively associated with simultaneous transplants of kidneys and other organs. Donor age was also associated with increased kidney graft losses. The multivariable Cox models reported median C-index values were 63% for patient mortality, 62% for pancreas loss, and 55% for kidney loss. Conclusions: This study provides an online risk tool at https://riskcalc.org/ptop for individual 5- and 10-y post-PKT and PT patient outcomes based on parameters available at the time of graft offer to support critical organ acceptance decisions and encourage external validation in independent populations.

Identifiers

PMID38757051
PMCPMC11098189

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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.