Abstract 16230: Pulmonary Autograft Wall Stresses One Year After Ross Operation
Bibliographic record
Abstract
Introduction: The Ross procedure is an excellent option for children and young adults who need aortic valve replacement as this surgery can restore patient survival to that of a normal sex and aged-matched population. However, some patients experience aneurysmal formation during autograft remodeling and require reoperation. As the underlying biomechanics of autograft remodeling are unknown, we investigated patient-specific wall stresses in pulmonary autografts one year post-operatively to better understand systemic pressure-driven early autograft wall stresses. Methods: Ross patients (n=16) who underwent intraoperative collection of pulmonary root/aortic specimen, and subsequent one-year MRI follow-up were recruited. Patient-specific material properties from their tissue were experimentally determined and incorporated into autograft ± Dacron and ascending aorta finite element models. A multiplicative approach was used to account for pre-stress geometry from in-vivo MRI. Physiologic pressure loading was simulated with LS-DYNA software. Results: At systemic systole, first principal stresses were 567kPa (25-75% IQR, 485-675kPa), 809kPa (691-1219kPa), and 382kPa (334-413kPa) at autograft sinuses, sinotubular junction (STJ), and ascending aorta, respectively. Second principal stresses were 355kPa (320-394kPa), 360kPa (310-426kPa), and 184kPa (147-222kPa) at autograft sinuses, STJ, and ascending aorta, respectively. Mean autograft diameters were 38.3±5.3mm, 29.9±2.7mm, and 26.6±4.0mm at sinuses, STJ, and annulus, respectively. Conclusions: First principal stresses were mainly located at STJ, particularly when Dacron reinforcement was applied to constrain STJ dilatation. However, at one-year after the Ross operation, autograft dilatation was not seen despite elevated autograft wall stresses compared to their internal controls, the lower wall stresses in corresponding native distal ascending aorta. In this group of patients, higher risk of dilatation is expected in the sinuses and STJ if not constrained by Dacron than the corresponding ascending aorta. Future follow-up will elucidate the biomechanics of long-term autograft remodeling to develop predictive models for autograft dilatation.
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) | 0.000 | 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) | 0.003 | 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".