Impact of normothermic regional perfusion on ex vivo lung perfusion outcomes in donation after circulatory death lung donors
Bibliographic record
Abstract
OBJECTIVES: Ex vivo lung perfusion (EVLP) is commonly used to assess extended-criteria donor lungs, particularly from donation after circulatory death (DCD) donors. Normothermic regional perfusion (NRP) is increasingly used in DCD procurement, but its impact on EVLP outcomes remains unclear. METHODS: This study included 187 DCD lungs perfused between October 2021 and August 2024 at 2 centralized EVLP facilities. Donor demographics, EVLP characteristics, and radiographs obtained during EVLP were analyzed. RESULTS: In total, 160 lungs were assessed by EVLP after rapid procurement; 13 of these were procured in the setting of an abdominal NRP protocol. In total, 23 DCD donor lungs underwent EVLP after thoracoabdominal (TA) NRP procurement. The primary indication for EVLP across the whole study cohort was DCD status (41%), followed by concerns about organ quality raised by the procurement team (34%), and low oxygen tension (12%). One lung from the TA-NRP group and 3 lungs from the rapid procurement group were not placed on EVLP upon arrival at the EVLP facility because of grossly abnormal appearance. Decline rates were 52% in the TA-NRP group and 50% in the rapid procurement group (P = .777). EVLP characteristics as well as radiographs were comparable across the 2 groups. In multivariate analysis, none of the available donor demographic factors or the indications for EVLP influenced EVLP outcomes. Notably, also TA-NRP was not a significant factor for EVLP conversion rates in multivariate analysis (odds ratio, 0.889; 0.338-2.339; P = .812). CONCLUSIONS: This early experience suggests that EVLP can be safely performed after TA-NRP without increasing the risk of donor lung loss.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".