Acute Reduction in Left Ventricular Function Following Transcatheter Mitral Edge‐to‐Edge Repair
Bibliographic record
Abstract
Background Little is known about the impact of transcatheter mitral valve edge‐to‐edge repair on changes in left ventricular ejection fraction (LVEF) and the effect of an acute reduction in LVEF on prognosis. We aimed to assess changes in LVEF after transcatheter mitral valve edge‐to‐edge repair for both primary and secondary mitral regurgitation (PMR and SMR, respectively), identify rates and predictors of LVEF reduction, and estimate its impact on prognosis. Methods and Results In this international multicenter registry, patients with both PMR and SMR undergoing transcatheter mitral valve edge‐to‐edge repair were included. We assessed rates of acute LVEF reduction (LVEFR), defined as an acute relative decrease of >15% in LVEF, its impact on all‐cause mortality, major adverse cardiac event (composite end point of all‐cause death, mitral valve surgery, and residual mitral regurgitation grade ≥2), and LVEF at 12 months, as well as predictors for LVEFR. Of 2534 patients included (727 with PMR, and 1807 with SMR), 469 (18.5%) developed LVEFR. Patients with PMR were older (79.0±9.2 versus 71.8±8.9 years; P <0.001) and had higher mean LVEF (54.8±14.0% versus 32.7±10.4%; P <0.001) at baseline. After 6 to 12 months (median, 9.9 months; interquartile range, 7.8–11.9 months), LVEF was significantly lower in patients with PMR (53.0% versus 56.0%; P <0.001) but not in patients with SMR. The 1‐year mortality was higher in patients with PMR with LVEFR (16.9% versus 9.7%; P <0.001) but not in those with SMR ( P =0.236). LVEF at baseline (odds ratio, 1.03 [95% CI, 1.01–1.05]; P =0.002) was predictive of LVEFR for patients with PMR, but not those with SMR ( P =0.092). Conclusions Reduction in LVEF is not uncommon after transcatheter mitral valve edge‐to‐edge repair and is correlated with worsened prognosis in patients with PMR but not patients with SMR. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05311163.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".