O004 Impact of normothermic regional perfusion on recipient outcomes after simultaneous pancreas and kidney transplantation – a UK analysis from the NHSBT Pancreas Advisory Group
Bibliographic record
Abstract
Abstract Introduction A quarter of UK SPK transplants are from Deceased Cardiac Donors(DCD), with an increasing number recovered following in-situ normothermic regional perfusion(NRP). The aim of this study was to review the UK experience of SPK transplantation following NRP in DCD. Methods Data were collected on all first DCD SPKTs(n=360) performed during 2013–2021 from the UK Transplant Registry. Non-NRP DCD SPK were compared to NRP DCD SPKs. Kaplan Meier plots and cox regression analyses were performed. Results Some 198 pancreas were offered from NRP donors with 83 being retrieved. The majority of SPK grafts were from nonNRP donors n=324(90.0%) with n=36(10.0%) from NRP donors. The median cold ischaemic time (CIT) from NRP donors (9.7 hours) was significantly less than nonNRP donors (10.2 hours) (p=0.013). For all other parameters, donors were well matched. Recipients who received a graft from an NRP donor were also well matched with the recipients who received a graft from a nonNRP donor. Univariate analysis showed no statistically significant difference in one-year pancreas graft (NRP 97.2%, non-NRP 89.2%, p=0.145), kidney graft (NRP 100%, nonNRP 95.9%, p=0.221) or patient survival (NRP 100%, nonNRP 98.3%, p=0.442) despite an increasing trend in favour of NRP SPK. Conclusions This is the largest reported analysis of NRP for SPK transplants to date. NRP has previously been shown to be beneficial for liver transplants. Despite some concerns that NRP may preferentially benefit the liver at the expense of other organs our study has shown no adverse effects. Larger studies are needed to evaluate whether NRP improves graft utilisation rates for SPK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".