Avoidance of Profound Hypothermia During Initial Reperfusion Improves the Functional Recovery of Hearts Donated After Circulatory Death
Bibliographic record
Abstract
The resuscitation of hearts donated after circulatory death (DCD) is gaining widespread interest; however, the method of initial reperfusion (IR) that optimizes functional recovery has not been elucidated. We sought to determine the impact of IR temperature on the recovery of myocardial function during ex vivo heart perfusion (EVHP). Eighteen pigs were anesthetized, mechanical ventilation was discontinued, and cardiac arrest ensued. A 15-min standoff period was observed and then hearts were reperfused for 3 min at three different temperatures (5°C; N = 6, 25°C; N = 5, and 35°C; N = 7) with a normokalemic adenosine-lidocaine crystalloid cardioplegia. Hearts then underwent normothermic EVHP for 6 h during which time myocardial function was assessed in a working mode. We found that IR coronary blood flow differed among treatment groups (5°C = 483 ± 53, 25°C = 722 ± 60, 35°C = 906 ± 36 mL/min, p < 0.01). During subsequent EVHP, less myocardial injury (troponin I: 5°C = 91 ± 6, 25°C = 64 ± 16, 35°C = 57 ± 7 pg/mL/g, p = 0.04) and greater preservation of endothelial cell integrity (electron microscopy injury score: 5°C = 3.2 ± 0.5, 25°C = 1.8 ± 0.2, 35°C = 1.7 ± 0.3, p = 0.01) were evident in hearts initially reperfused at warmer temperatures. IR under profoundly hypothermic conditions impaired the recovery of myocardial function (cardiac index: 5°C = 3.9 ± 0.8, 25°C = 6.2 ± 0.4, 35°C = 6.5 ± 0.6 mL/minute/g, p = 0.03) during EVHP. We conclude that the avoidance of profound hypothermia during IR minimizes injury and improves the functional recovery of DCD hearts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".