Mitral regurgitation evolution after transcatheter tricuspid valve interventions—a sub-analysis of the TriValve registry
Bibliographic record
Abstract
AIMS: Transcatheter tricuspid valve interventions (TTVI) are increasingly used to treat patients with significant tricuspid regurgitation (TR). The evolution of concurrent mitral regurgitation (MR) severity after TTVI is currently unknown and may be pivotal for clinical decision-making. The aim of this study was to assess the evolution of MR after TTVI and to identify predictors of MR worsening and improvement. METHODS AND RESULTS: This analysis is a substudy of the TriValve Registry, an international registry designed to collect data on TTVI. This substudy included all patients with echocardiographic data on MR evolution and excluded those with a concomitant tricuspid and mitral transcatheter valve intervention or with a history of mitral valve intervention. The co-primary outcomes were MR improvement and worsening at two timepoints: pre-discharge and 2-month follow-up. This analysis included 359 patients with severe TR, mostly (80%) treated with tricuspid transcatheter edge-to-edge repair (T-TEER). MR improvement was found in 106 (29.5%) and 99 (34%) patients, while MR worsening was observed in 34 (9.5%) and 33 (11%) patients at pre-discharge and 2-month follow-up, respectively. Annuloplasty and heterotopic replacement were associated with MR worsening. Independent predictors of MR improvement were: atrial fibrillation, T-TEER, acute procedural success, TR reduction, left ventricular end-diastolic diameter> 60 mm, and beta-blocker therapy. Patients with moderate-to-severe/severe MR following TTVI showed significantly higher death rates. CONCLUSION: MR degree variation is common after TTVI, with most cases showing improvement. Clinical and procedural characteristics may predict the MR evolution, in particular procedural success and T-TEER play key roles in MR outcomes. TTVI may be beneficial, even in the presence of functional MR.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".