Impact of Mitral Stenosis on Early and Late Outcomes of Transcatheter Aortic Valve Replacement for Aortic Stenosis: A Single-Center Analysis
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of concomitant mitral stenosis (MS) on early and late outcomes of transcatheter aortic valve replacement (TAVR) for aortic stenosis. METHODS: This study involved 952 patients undergoing TAVR for severe tricuspid aortic stenosis. The patients were classified into 3 groups: without MS, with progressive MS, and severe MS (mitral valve area ≤ 1.5 cm2). Clinical outcomes between these groups were compared. RESULTS: The median age of the overall cohort was 82 years, and patients in the progressive (n = 49) and severe (n = 24) MS groups were more likely to be female than those in the no-MS group (n = 879). Periprocedural mortality rate was lowest in the no-MS group (1.8%) compared with the progressive (4.1%) and severe (4.2%) MS groups, which were not significantly different (P = .20). During 5 years of follow-up (median: 27, range: 0-72 months), there was no significant difference in all-cause mortality (log-rank P = .99), a composite of all-cause mortality or rehospitalization for heart failure (log-rank P = .84), or cardiovascular death (log-rank P = .57) between groups. Although crude analysis showed a significant difference in rehospitalization for heart failure in the severe MS group compared with the no-MS group (P = .049), the difference was not significant in the multivariate analysis (adjusted hazard ratio: 1.36 [95% CI, 0.66-2.80], P = .41). CONCLUSIONS: TAVR can be safely performed in patients with severe tricuspid aortic stenosis and concomitant MS, with early and mid-term outcomes comparable to those in patients without MS.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".