Evaluating the Impact of Ex Vivo Lung Perfusion on Organ Transplantation
Bibliographic record
Abstract
BACKGROUND: Ex vivo lung perfusion (EVLP) sustains and allows advanced assessment of potentially useable donor lungs before transplantation, potentially relieving resource constraints. OBJECTIVE: We sought to characterize the effect of EVLP on organ utilization and patient outcomes. METHODS: We performed a retrospective, before-after cohort study using linked institutional data sources of adults wait-listed for lung transplant and donor organs transplanted in Ontario, Canada between 2005 and 2019. We regressed the annual number of transplants against year, EVLP use, and organ characteristics. Time-to-transplant, waitlist mortality, primary graft dysfunction, tracheostomy insertion, in-hospital mortality, and chronic lung allograft dysfunction were evaluated using propensity score-weighted regression. RESULTS: EVLP availability ( P =0.01 for interaction) and EVLP use ( P <0.001 for interaction) were both associated with steeper increases in transplantation than expected by historical trends. EVLP was associated with more donation after circulatory death and extended-criteria donors transplanted, while the numbers of standard-criteria donors remained relatively stable. Significantly faster time-to-transplant was observed after EVLP was available (hazard ratio=1.64 [1.41-1.92]; P <0.001). Fewer patients died on the waitlist after EVLP was available, but no difference in the hazard of waitlist mortality was observed (HR=1.19 [0.81-1.74]; P =0.176). We observed no difference in the likelihood of chronic lung allograft dysfunction before versus after EVLP was available. CONCLUSIONS: We observed a significant increase in organ transplantation since EVLP was introduced into practice, predominantly from increased acceptance of donation after circulatory death and extended-criteria lungs. Our findings suggest that EVLP-associated increases in organ availability meaningfully alleviated some barriers to transplant.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".