Increase in Aortic Valve Mean Gradients One Day After Transcatheter Aortic Valve Implantation: The Role of Mitral Regurgitation
Bibliographic record
Abstract
Background: Following transcatheter aortic valve implantation (TAVI), transvalvular mean gradient is known to increase from immediate to 24 h post-procedure. While anesthesia, rapid-pacing, and volume status are blamed, the true etiology is unclear. To our knowledge, no prior studies have evaluated the effects of mitral regurgitation (MR) on the rise in post-TAVI transvalvular mean gradient. Methods: A single-center, retrospective analysis of patients who underwent TAVI at our institution between 2011 to 2020 was performed (n = 378, males = 206). Patients were divided into two groups, no-to-mild MR (n = 327) and moderate-to-severe MR (n = 51) based on echocardiograms obtained prior to TAVI. Transvalvular gradients were compared between immediate and 24-h post-TAVI echocardiograms. Results: The average age of no-to-mild MR patients (77 years (interquartile range (IQR): 71 - 84)) was similar to moderate-to-severe MR patients (79 years (IQR: 76 - 85), p=0.13). Both groups had similar procedural blood pressures and peri-procedural medication use. The change in 24-h post-TAVI mean transvalvular gradient was +6 mm Hg (IQR: 3.7 - 9) in the moderate-to-severe MR group and +6 mm Hg (IQR: 3.4 - 9) in the no-to-mild MR group (P = 0.87). Conclusions: In this study, we evaluated the impact of preexisting MR on changes in transvalvular gradients following TAVI. We observed no statistically significant difference in 24-h post-TAVI gradient changes between patients with moderate-to-severe MR and those with no-to-mild MR. These findings suggest that baseline MR may not be a major determinant of early post-TAVI hemodynamics; however, further prospective studies are needed to confirm this observation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".