Abstract 4309: Ventricular Elastic Support Therapy (VEST) Reduces Transmural Pressure and Myocardial Strain
Bibliographic record
Abstract
Background: Transmural pressure (TMP) determines myocardial strain and chamber dimensions. Epicardial ventricular support may improve mechanical function and promote reverse remodeling by reducing TMP. HeartNet (Paracor Medical Inc) is an elastic intra-pericardial ventricular support device currently evaluated in the PEERLESS-HF clinical trial. To explore the mechanism of action of VEST, we directly measured the in vivo effects of HeartNet on regional hemodynamics and myocardial strain. Methods: We instrumented open-chest anesthetized dogs (n=6) with flat silastic-balloon epicardial pressure transducers, intravascular pressure catheters, aortic flow probes, and sonomicronometry crystals. The pericardium was re-approximated. We obtained pericardial, epicardial, LV, and aortic pressures. We measured epicardial segment length (end-diastolic myocardial strain) and LV dimensions throughout the cardiac cycle. Loading conditions were altered by volume infusion, caval occlusion, and LAD ligation to induce regional ischemia / dysfunction. Parameters were obtained in every animal with and without the HeartNet in situ (applied at 45% baseline stretch) and compared. Results: HeartNet epicardial support was 2.8±0.2 mmHg (mean at end-diastole) and was maintained throughout the cardiac cycle and over a wide range of physiologic filling conditions (LVEDP 0 –15 mmHg) indicating that the device is compliant, elastic, and self-adjusting. TMP and end-diastolic strain were significantly reduced (P<0.01) supporting a mechanism of action for the device. After LAD ligation, HeartNet also reduced TMP (−38±8%, P<0.0001) and end-diastolic strain over the anterior wall. Negative regional stroke work was also lowered (−56±2%, P<0.001) suggesting improved overall ventricular mechanical efficiency. Aortic pressure and LV stroke volume (20±2 vs 22 ±3 mL, P=0.20) was maintained. Conclusions: These are the first in vivo data to demonstrate that ventricular elastic support therapy improves regional myocardial mechanics throughout the cardiac cycle by reducing transmural pressure and end-diastolic strain. Elastic support therapy may provide significant benefits on acute hemodynamics and chronic remodeling in the failing heart.
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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 | 0.000 | 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) | 0.007 | 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".