P1476 Percutaneous paravalvular leak closure after transcatheter aortic valve replacement expedited with multimodality imaging
Bibliographic record
Abstract
Abstract A 75-year-old male admitted to our hospital with decompansated heart failure symptoms. He had a history of 3 vessel coronary artery bypass grafting 10 years ago and a 29 mm Evolut R bioprosthetic transcatheter aortic valve replacement (TAVR) history 2 months ago. His physical examination revealed a 3/6 diastolic murmur on the aortic valve area. We performed a transeosophagel echocardiography (TOE) as the transthoracic echocardiography (TTE) images were not so clear and didn’t guide enough for the procedure . TOE showed a severe paravalvular leak . After we found out from his medical reports that postprocedural ad-hoc post dilatation was performed but didn’t work, we decided to close this paravalvular leak percutanaously . According to TOE, the paravalvular leak was at 12 o’clock position. We identified the corresponding location of the leak on previous CT images which was scanned pre-TAVR for selection of the valve size and planning of the procedure. We recognized that the leak location was corresponding to a very calcified part of the aortic annulus and the reason of the severe PVL seemed to be due to this nodular extensive calcification. We planned the procedure according to TOE-CT integrated analysis and selected the optimal flouroscopic viewing angle.The defect was found and crossed in 20 seconds after the wire passed through arcus aorta.The selected VSD Occluder(No:12) was deployed precisely by extending the device throughout the defect. Succesful complete closure was confirmed with TOE. In the past 1o years, TAVR has become the treatment of choice for patients with severe aortic stenosis with a higher operative risk. Different studies have depicted a higher incidence of PVR in patients who undergo TAVR compared to SAVR. Percutaneous postTAVR PVL closure is a technically challenging procedure. Echocardiography remains the primary imaging modality for assessing PVR immediately following TAVR. Finding and crossing the PVL defect is one of the most difficult and time-consuming parts of the procedure. The reason of this difficulty can be different anatomies, bad quality imaging and lack of common language between interventional cardiologist and cardiac imaging expert. To facilitate this part of the procedure integration of echocardiograohy and CT and finding the best angulation for flouroscopy seems to be quite promising. Abstract P1476 Figure. Step by step approach to PostTAVR
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.010 |
| 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) | 0.000 | 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".