Transcatheter Mitral Valve Repair and Replacement. A Critical Review of Current Real-World Practice
Bibliographic record
Abstract
OBJECTIVES: Transcatheter mitral valve interventions (TMVIs) provide less invasive alternatives for patients deemed ineligible for surgery. This manuscript reviews the evolving landscape of TMVI, limited to a comparative analysis of transcatheter edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR) in terms of safety and efficacy outcomes. METHODS: A comprehensive review of the existing literature was conducted, encompassing data from initial device trials to the most recent real-world studies, ranging from 2009 through 2025. A narrative synthesis was conducted to integrate findings and evaluate the current state of TMVI. RESULTS: Initially approved based on trials with stringent anatomical and clinical inclusion criteria, TEER has been increasingly applied to broader patient populations in real-world settings. Despite its expanded use, there remains a lack of randomized trial evidence for TEER in complex anatomies. TEER remains the preferred option for patients with suitable anatomy, given its well-established safety and efficacy, with over 200 000 patients treated. TMVR demonstrates promise in reducing mitral regurgitation (MR) but is associated with higher periprocedural risks, demands careful patient selection, and is supported by a more limited clinical experience, with just over 1400 patients treated. Emerging technological advancements are anticipated to reduce reliance on transapical access, aiming to improve procedural safety and outcomes. CONCLUSIONS: TEER and TMVR represent significant advancements in the management of MR. However, the field requires additional high-quality studies to clarify the efficacy and long-term outcomes of these interventions. Further technological developments in TMVR may enhance its safety profile and expand its applicability.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".