Assessment of donor heart viability during ex vivo heart perfusion
Bibliographic record
Abstract
Ex vivo heart perfusion (EVHP) may facilitate resuscitation of discarded donor hearts and expand the donor pool; however, a reliable means of demonstrating organ viability prior to transplantation is required. Therefore, we sought to identify metabolic and functional parameters that predict myocardial performance during EVHP. To evaluate the parameters over a broad spectrum of organ function, we obtained hearts from 9 normal pigs and 37 donation after circulatory death pigs and perfused them ex vivo. Functional parameters obtained from a left ventricular conductance catheter, oxygen consumption, coronary vascular resistance, and lactate concentration were measured, and linear regression analyses were performed to identify which parameters best correlated with myocardial performance (cardiac index: mL·min(-1)·g(-1)). Functional parameters exhibited excellent correlation with myocardial performance and demonstrated high sensitivity and specificity for identifying hearts at risk of poor post-transplant function (ejection fraction: R(2) = 0.80, sensitivity = 1.00, specificity = 0.85; stroke work: R(2) = 0.76, sensitivity = 1.00, specificity = 0.77; minimum dP/dt: R(2) = 0.74, sensitivity = 1.00, specificity = 0.54; tau: R(2) = 0.51, sensitivity = 1.00, specificity = 0.92), whereas metabolic parameters were limited in their ability to predict myocardial performance (oxygen consumption: R(2) = 0.28; coronary vascular resistance: R(2) = 0.20; lactate concentration: R(2) = 0.02). We concluded that evaluation of functional parameters provides the best assessment of myocardial performance during EVHP, which highlights the need for an EVHP device capable of assessing the donor heart in a physiologic working mode.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".