Clinical impact of residual gradient and 3D orifice area after transcatheter mitral valve-in-valve implantation
Bibliographic record
Abstract
AIMS: The mitral valve-in-valve (MVIV) procedure has emerged as an important therapy in failing surgical bioprosthetic valves. We aimed to evaluate factors associated with outcome following MVIV intervention, specifically the impact of 30-day MVIV gradient. We also explored the value of intraprocedural MVIV three-dimensional anatomic orifice area (3D-AOA) by transoesophageal echocardiography in a subset of patients (n = 68). METHODS AND RESULTS: Consecutive MVIV patients from a single institution with 30-day transthoracic echocardiography (TTE) were included (N = 100). Clinical and echocardiographic variables were evaluated. The primary outcome was one-year composite of all-cause mortality, heart failure hospitalization or re-intervention. Multivariable analysis was performed to determine predictors of primary outcome. Mean age was 77.3 ± 10.6 years and pre-intervention mean mitral gradient was 11.5 ± 4.0 mmHg. Thirty-day MVIV mean gradient was 7.4 ± 2.6 mmHg with ≤1+ residual regurgitation in 99.0% of patients. Multivariable analysis identified MVIV mean gradient as the only independent determinant of the primary outcome (HR 1.31, CI: 1.07-1.61, P = 0.009). MVIV 3D-AOA was associated with a 30-day MVIV mean gradient of >7 mmHg by TTE (ROC-AUC 0.8, P < 0.001), and patients with 3D-AOA > 2 cm2 had significantly lower 1-year all-cause mortality (2.5% vs. 18.7%, Kaplan-Meier log-rank P = 0.03). CONCLUSION: Elevated 30-day mean gradient is associated with worse outcomes after MVIV, and smaller intraprocedural MVIV 3D-AOA is associated with a higher 30-day mean gradient and worse mortality. Optimizing MVIV orifice area at the time of procedure may improve valve haemodynamics and patient outcomes.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.017 |
| 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".