Advancements in Donor Heart Preservation Methods: A Review of Approaches
Bibliographic record
Abstract
Heart transplantation remains the gold standard for end-stage heart failure, yet access is limited by geography, organ shortages, and preservation technology. In Canada, long wait times and organ underutilization persist due to the use of static cold storage (SCS), the current standard. Although SCS is cost-effective and technically simple, its short ischemic window and the inability to monitor heart function restrict donor heart viability. This review examines the evolution of preservation methods, with a focus on SCS and novel machine perfusion systems that aim to address the limitations of SCS. Hypothermic machine perfusion (HMP) and normothermic machine perfusion (NMP) both provide extended preservation times and metabolic support. HMP delivers cold, oxygenated or non-oxygenated perfusate to minimize ischemia, while NMP maintains the donor heart at body temperature in a semi-physiological state, allowing for functional monitoring and therapeutic reconditioning. Various technologies have already demonstrated success in large animal models and select clinical trials, with promising results for extended preservation intervals and DCC heart use. Normothermic regional perfusion (NRP) is a method for initial resuscitation of the DCC heart, by restoring circulation in situ before procurement and reducing ischemic injury. Its adoption in Europe and the United States (U.S.) has accelerated DCC heart donation while maintaining excellent outcomes. With new ethical guidelines supporting Canada's use, pilot programs are now possible. To improve transplant equity and maximize heart utilization, Canada can invest in machine perfusion research, infrastructure, and national implementation strategies. These technologies offer not only extended viability but also decrease waitlist mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".