Hemodynamic Performance of Self-expandable Transcatheter Aortic Valve Replacement Systems During Valve Deployment
Bibliographic record
Abstract
OBJECTIVES: Little is known about valve hemodynamic performance during the Evolut and Neo deployment course. We aimed to evaluate transvalvular mean and peak-to-peak gradients over several intraprocedural timepoints during TAVR with Evolut PRO+ (Medtronic) and Neo (Boston Scientific) systems. METHODS: This was single-center pilot sub-study from the SavvyWire EFficacy and SafEty in Transcatheter Aortic Valve Implantation Procedures (SAFE-TAVI) trial. Participants received either the Evolut PRO+ or Neo for native valve severe aortic stenosis and the SavvyWire (OpSens Medical) was used for device delivery, pacing, and continuous left ventricular and aortic pressure measurements. For the Evolut, evaluation was done for baseline, two-thirds of valve deployment (still recapturable), 90% of valve deployment (no longer recapturable), and post-deployment hemodynamics. For the Neo, analysis was done at baseline, after the first step (top-crown deployment), and at final status. RESULTS: Nineteen patients were included (Evolut = 15; Neo = 4). There were no statistically significant changes in peak-to-peak gradients (44 mm Hg [IQR:33-69] vs 43 mm Hg [IQR:26-62], P = .41) between baseline and two-thirds of valve deployment in the Evolut patients. There was a significant decrease in mean (40 mm Hg [IQR:32-54] vs 14 mm Hg [IQR:10-18], P less than .001) and peak-to-peak (43 mmHg [IQRS:26-62] vs 9 mm Hg [IQR:8-13], P less than .001) transvalvular gradients between two-thirds and 90% of valve deployment for Evolut. Neo patients exhibited a decrease in transvalvular gradients after top-crown deployment (42.5 mm Hg baseline vs 13 mm Hg). CONCLUSIONS: Transvalvular gradients did not vary between the point of "no-recapture" compared to baseline values in patients receiving the Evolut, whereas a significant reduction in transvalvular gradients was observed when the valve was deployed at 90% and fully deployed. The Neo valve was slightly obstructive after the first step of deployment.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".