Impact of left ventricular function on clinical outcomes of functional mitral regurgitation patients undergoing transcatheter mitral valve repair
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of baseline left ventricular (LV) function on the clinical outcomes of patients with functional mitral regurgitation (FMR) treated with MitraClip. BACKGROUND: It is unknown whether patients with significant FMR and severe LV dysfunction benefit from MitraClip. METHODS: A cohort of 77 patients with significant FMR undergoing MitraClip procedure between December 2010 and January 2015 was categorized by baseline LV ejection fraction (LVEF) into tertiles: LVEF <27% (n = 27), LVEF 27-37% (n = 25), and LVEF >37% (n = 25). We sought to evaluate the impact of LVEF on all-cause mortality at follow-up. RESULTS: There were no significant differences in baseline comorbidities, medical treatment and MR severity among tertiles of LVEF. Overall procedural success was 94%, with no differences among groups (LVEF <27%: 89%; LVEF 27-37%: 100%; LVEF >37%: 92%; P = 0.25). Median follow-up was 372 days (interquartile range: 128-627 days). MR severity improved in all three groups, as compared to baseline. There were no differences in the prevalence of MR ≤2+ on follow-up (P = 0.40). Mortality was highest in patients with LVEF <27% (41%), as compared with LVEF 27-37% (16%) and LVEF >37% (4%), P = 0.004. Patient who died had a lower baseline LVEF compared to those who survived (24.8 ± 7.7% versus 35.5 ± 13.7%, P < 0.001). An LVEF <27% was an independent predictor of mortality after adjusting for procedural success: hazard ratio 3.4 (95% CI: 1.1 to 10.0; P = 0.030). CONCLUSIONS: MitraClip is effective in FMR patients regardless of the severity of LV dysfunction. However, low baseline LVEF is associated with increased mortality, despite procedural success. © 2016 Wiley Periodicals, Inc.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.056 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".