Echocardiographic assessment of left ventricular function in ex situ heart perfusion using pump-supported and passive afterload working mode: a pilot study
Bibliographic record
Abstract
Ex situ heart perfusion (ESHP) has been developed to decrease cold ischemia time and allow metabolic assessment of donor hearts prior to transplantation. Current clinical ESHP systems preserve the heart in an unloaded condition and only evaluate the cardiac metabolic profile. In this pilot study we performed echocardiographic functional assessment using two alternative systems for left ventricular (LV) loading: pump supported afterload working mode (SAM) and passive afterload working modes (PAM). Six hearts were procured from male Yorkshire pigs. During cold ischemia, hearts were mounted on our custom made ESHP circuit and a 3D-printed enclosure for the performance of echocardiography with a standard TEE probe. Following perfusion with Langherdorf mode of the unloaded heart, the system was switched into different working modes to allow LV loading and functional assessment: pump supported (SAM) and passive (PAM). Echocardiographic assessment of left ventricular function in the donor hearts was performed in vivo and at 1 h of ESHP with SAM, after 4.5 h with PAM and after 5.5 h with SAM. We obtained good quality epicardial echocardiographic images at all time points allowing a comprehensive LV systolic assessment. All indices showed a decrease in LV systolic function throughout the trial with the biggest drop after heart harvesting. We demonstrated the feasibility of echocardiographic functional assessment during ESHP and two different working modes. The expected LV systolic dysfunction consisted of a reduction in EF, FAC, FS, and strain throughout the experiment with the most significant decrease after harvesting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".