Computed simulation of transcatheter edge-to-edge mitral valve repair
Bibliographic record
Abstract
A 73-year-old man stented on the circumflex artery 4 months ago during a ST-elevation myocardial infarction was admitted in the intensive care unit for heart failure. He initially required assisted ventilation but responded well to medical therapy. Transthoracic and transoesophageal echocardiography showed a severe mitral regurgitation (MR) due to leaflet restrictive motion and prolapse of the medial segment of the posterior valve with a small rupture of chordae, limited inferior wall motion abnormalities with a 45% ejection fraction, and 60 mmHg systolic pulmonary pressure. There was no need for revascularization and a Mitraclip XTL® (Abbott Vascular Inc., Santa-Clara, CA, USA) was implanted between A3 and P3 (red arrow) enabling to decrease the MR degree from severe to moderate. The patient markedly improved. Using a finite element prototype software (PlanOp® Structural Heart, PrediSurge, France), we simulated the results of the intervention. First, a 3D model of the mitral valve was created reproducing the located medially MR before implantation (Panel A). The clip implantation was simulated using the same clip size positioned at an identical location. The simulated model predicted a residual moderate MR lateral to the clip and a 0.20 cm2 regurgitant orifice (Panel B). We obtained a similar diastolic orifice shape (Panel C). We present a proof-of-concept case report of a computational simulation using the finite element of transcatheter edge-to-edge mitral valve intervention, closely mimicking procedural results. With the expected increase in the number of centres and operators performing transcatheter interventions and as more devices become available, personalized medicine to help operators in procedural planning and in the clinical decision-making process will be critical. The study is partially funded by Predisurge. There are no new data associated with this article.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.027 |
| 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".