Factors Associated With the Use of Hypothermic Machine Perfusion in Kidney Transplant Recipients: A Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
Background: Delayed graft function (DGF) is associated with an increased risk of graft loss. The use of cold hypothermic machine perfusion (HMP) has been shown to reduce the incidence of DGF in kidney transplant recipients (KTRs), especially when extended-criteria donors (ECDs) are used. HMP can also improve graft survival. However, there is a paucity of data on the determinants of HMP use in clinical practice. Objective: We aimed to determine the factors associated with the use of HMP in a cohort of donors and KTRs. Design: Multicenter retrospective cohort study. Setting: 5 transplant centers in Quebec. Patients: 159 neurologically deceased donors (NDD) and 281 KTR. Measurements: Use of HMP. Methods: We collected data on consecutive NDD admitted to a dedicated donor unit in a single university-affiliated center and their KTRs between June 2013 and December 2018 in 5 adult transplant centers across the province of Quebec, Canada. All organs were recovered in a single hospital center where a HMP device was available for every organ recovered and the decision to use HMP was left at the discretion of the procurement surgeon. Generalized estimating equations were used to predict the use of HMP. Results: = .77). In univariate analysis, the use of HMP was associated with KTR race (non-Caucasian), longer CIT, use of basiliximab/alemtuzumab, year of transplant, and transplant center. The use of HMP varied largely across transplant centers, ranging from 15% to 82%. In multivariate analysis, use of HMP was associated with longer CIT (odds ratio [OR] 1.15, 95% confidence interval [CI] = 1.07-1.25), transplant center as well as transplantations performed after 2013. Limitations: One dedicated donor unit including NDD only, absence of specific data on surgeons' experience and personal or logistic reasons for using or not HMP. Conclusions: We found that use of HMP remains low and varies largely across transplant centers. The use of HMP was strongly associated with the transplant center where the surgeons practiced, suggesting that surgeon preference/training plays an important role in determining the use of HMP. Availability of HMP at the time of organ procurement might also be limited by logistic issues such as difficulty in returning the device. Further studies aimed at determining the reasons underlying the barriers precluding the use of HMP could help increasing its use and improve transplant outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".