New Strategies to Expand and Optimize Heart Donor Pool: Ex Vivo Heart Perfusion and Donation After Circulatory Death: A Review of Current Research and Future Trends
Bibliographic record
Abstract
Heart transplantation remains the definitive management for end-stage heart failure refractory to medical therapy. While heart transplantation cases are increasing annually worldwide, there remains a deficiency in organ availability with significant patient mortality while on the waiting list. Attempts have therefore been made to expand the donor pool and improve access to available organs by recruiting donors who may not satisfy the standard criteria for organ donation because of donor pathology, anticipated organ ischemic time, or donation after circulatory death. "Ex vivo" heart perfusion (EVHP) is an emerging technique for the procurement of heart allografts. This technique provides mechanically supported warm circulation to a beating heart once removed from the donor and before implantation into the recipient. EVHP can be sustained for several hours, facilitate extended travel time, and enable administration of pharmacological agents to optimize cardiac recovery and function, as well as allow assessment of allograft function before implantation. In this article, we review recent advances in expanding the donor pool for cardiac transplantation. Current limitations of conventional donor criteria are outlined, including the determinants of organ suitability and assessment, involving transplantation of donation after circulatory death hearts, extended criteria donors, and EVHP-associated assessment, optimization, and transportation. Finally, ongoing research relating to organ optimization and functional ex vivo allograft assessment are reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".