Unlocking the potential of uncontrolled DCD in lung transplantation: A review of 2 decades of experience
Bibliographic record
Abstract
Uncontrolled donation after circulatory death (uDCD) represents a promising yet underutilized approach to expanding the lung donor pool amid persistent organ shortages. Since the first successful lung transplantation from a uDCD donor in 2001, increasing clinical experience and advancements in organ preservation have demonstrated its feasibility. This review critically explores historical evolution, physiological basis, preservation techniques, ethical and legal considerations, and clinical outcomes of uDCD lung transplantation. The lung's unique ability to maintain viability through passive oxygen diffusion in the absence of perfusion supports its potential in the uDCD context. Compared to donors after brain death (DBD), uDCD donors may avoid systemic inflammatory response, potentially preserving graft quality. However, concerns persist regarding ischemia-reperfusion injury and mitochondrial dysfunction, highlighting the need for mitigation strategies such as ex vivo lung perfusion and normothermic ventilation. Ethical and legal challenges-particularly those related to the determination of death and consent-remain key obstacles. Organizational demands, including rapid coordination between prehospital, hospital teams and transplant teams, further limit broader implementation. Despite these barriers, reported outcomes are encouraging: to date, over 70 transplants from uDCD donors have been documented, with 1-year survival rates ranging from 71% to 87.5% and long-term outcomes comparable to DBD transplants. Integration of uDCD into routine clinical practice will require standardized protocols, robust public engagement, and institutional commitment. When appropriately implemented, uDCD lung transplantation offers a viable opportunity to increase donor availability and improve access to life-saving treatment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".