Heart Transplantation From Brain Dead Donors: A Systematic Review of Animal Models
Bibliographic record
Abstract
Despite advances in mechanical circulatory devices and pharmacologic therapies, heart transplantation (HTx) is the definitive and most effective therapy for an important proportion of qualifying patients with end-stage heart failure. However, the demand for donor hearts significantly outweighs the supply. Hearts are sourced from donors following brain death, which exposes donor hearts to substantial pathophysiological perturbations that can influence heart transplant success and recipient survival. Although significant advances in recipient selection, donor and HTx recipient management, immunosuppression, and pretransplant mechanical circulatory support have been achieved, primary graft dysfunction after cardiac transplantation continues to be an important cause of morbidity and mortality. Animal models, when appropriate, can guide/inform medical practice, and fill gaps in knowledge that are unattainable in clinical settings. Consequently, we performed a systematic review of existing animal models that incorporate donor brain death and subsequent HTx and assessed studies for scientific rigor and clinical relevance. Following literature screening via the U.S National Library of Medicine bibliographic database (MEDLINE) and Embase, 29 studies were assessed. Analysis of included studies identified marked heterogeneity in animal models of donor brain death coupled to HTx, with few research groups worldwide identified as utilizing these models. General reporting of important determinants of heart transplant success was mixed, and assessment of posttransplant cardiac function was limited to an invasive technique (pressure-volume analysis), which is limitedly applied in clinical settings. This review highlights translational challenges between available animal models and clinical heart transplant settings that are potentially hindering advancement of this field of investigation.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".