North American expert consensus on the clinical role of ex vivo lung perfusion (EVLP) with acellular perfusate
Bibliographic record
Abstract
Background: Ex vivo lung perfusion (EVLP) of donor lungs not otherwise acceptable for transplantation can provide outcomes similar to standard-criteria lung transplantation and has been reported to increase transplant volume by approximately 20% in some transplant centers. Evidence to support decisions about use of EVLP is limited, so expert opinion can be a useful decision aid. This study developed expert consensus recommendations for EVLP with acellular perfusate using a modified Delphi method. Methods: A panel of 18 physicians with expertise in lung transplantation and EVLP who practice in North America completed three surveys on EVLP: Survey 1 used open-ended questions; Survey 2 used primarily Likert-scale questions; and Survey 3 repeated Survey 2 while providing panelists with the Survey 2 results. A follow-up meeting after Survey 3 probed open questions. Results: The primary goal for EVLP is expanding the number of donor lungs available for transplant. Lungs that are acceptable after EVLP are equivalent to lungs that met standard criteria initially. Lungs with unclear or marginal quality should be placed on EVLP for evaluation, including lungs received from third party organizations with incomplete or concerning information. Decisions on whether to put lungs on EVLP require nuanced clinical judgement and should consider compliance and deflation, the ratio of PaO2 to fraction of inspired oxygen (P/F ratio), peak inspiratory pressure (PIP), edema on imaging, and bronchoscopy, with additional parameters considered as appropriate if lung quality is unclear. EVLP lungs are appropriate for transplant if all relevant parameters are acceptable and may be appropriate if some parameters are borderline depending on clinical judgment. Decisions about transplanting EVLP lungs should consider radiography, delta PO2, overall movement, STEEN Solution™ loss, bronchoscopy, peak airway pressure, and palpation, along with other parameters as appropriate. Key open areas for research include evidence-based criteria for lung selection and assessment, the role of biomarkers, and enhanced techniques and perfusion solutions. In addition, the role of EVLP is unclear in lungs with pulmonary emboli and lungs procured with normothermic regional perfusion (NRP), as is the maximal duration of cold ischemia time (CIT). Conclusions: Decisions about EVLP require nuanced consideration of numerous parameters. Expert opinion from this study may help optimize use of EVLP.
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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 | 0.191 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".