Clinical impact and evolution of mitral regurgitation following transcatheter aortic valve replacement: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Mitral regurgitation (MR) is a common entity in patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR), but its influence on outcomes remains controversial. The purpose of this meta-analysis was to assess the clinical impact of and changes in significant (moderate-severe) MR in patients undergoing TAVR, overall and according to valve design (self-expandable (SEV) vs balloon-expandable (BEV)). METHODS: All national registries and randomised trials were pooled using meta-analytical guidelines to establish the impact of moderate-severe MR on mortality after TAVR. Studies reporting changes in MR after TAVR on an individual level were electronically searched and used for the analysis. RESULTS: Eight studies including 8015 patients (SEV: 3474 patients; BEV: 4492 patients) were included in the analysis. The overall 30-day and 1-year mortality was increased in patients with significant MR (OR 1.49, 95% CI 1.16 to 1.92; HR 1.32, 95% CI 1.12 to 1.55, respectively), but a significant heterogeneity across studies was observed (p<0.05). The impact of MR on mortality was not different between SEV and BEV in meta-regression analysis for 30-day (p=0.360) and 1-year (p=0.388) mortality. Changes in MR over time were evaluated in nine studies including 1278 patients. Moderate-severe MR (SEV: 326 patients; BEV: 192 patients) improved in 50.5% of the patients at a median follow-up of 180 (30-360) days after TAVR, and the degree of improvement was greater in patients who had received a BEV (66.7% vs 40.8% in the SEV group, p=0.001). CONCLUSIONS: Concomitant moderate-severe MR was associated with increased early and late mortality following TAVR. A significant improvement in MR severity was detected in half of the patients following TAVR, and the degree of improvement was greater in those patients who had received a BEV.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.059 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".