Abstract 18767: Preoperative Echocardiographic Predictors of Left Ventricular Remodeling After Surgical Correction of Mitral Regurgitation Caused by Leaflet Prolapse
Bibliographic record
Abstract
Background: This study sought to determine whether preoperative echocardiography analysis before mitral valve surgery is predictive of postoperative left ventricular dysfunction in patients undergoing valve surgery for severe mitral regurgitation caused by leaflet prolapse. Methods: In 72 consecutive patients without coronary artery disease undergoing valve repair (n=29, 40%) or replacement (n=43, 60%), preoperative, early postoperative (1-2 days) and late postoperative (4.5 ± 2.5 months and 18 ± 8.0 months) echocardiographic parameters, including left ventricular (LV) dimensions, volumes and ejection fractions (EF) were measured. Patients were stratified and analysed in 3 different groups according to their respective baseline LVEF (group 1: EF ≥ 60, group 2: EF = 45-59%, group 3: EF < 45%). Results: Over a mean follow-up period of 546 ± 244 days, our cohort’s echocardiographic parameters showed the following reductions from baseline: LVEF: 56% ± 8 to 53% ± 9 (p<0.0001), LV end-diastolic dimension: 58.0mm ± 8.0 to 47.0mm ± 5.0 (p<0.0001) and LV end-systolic dimension: 41.0mm ± 8.0 to 33.0mm ± 6.0 (p<0.0001) respectively. Subgroup analysis revealed that only those with LVEF ≥ 60% recovered to a completely normal EF. Additionally, two-third of the observed changes in LV diameters and volumes occurred in the first 6 months. No difference in remodelling parameters was seen during the follow-up period between patients who underwent mitral valve replacement versus mitral valve repair. Finally, multivariate analysis demonstrated that pre-operative LVEF is an independent predictor of a poor post-operative LVEF (OR = 1.504 [1.249 - 1.966; p<0.0001]). Conclusions: In patients with pure severe mitral regurgitation due to leaflet prolapse, significant positive LV remodelling can be expected irrespective of patient’s baseline EF. Remodelling is more important in the first 6 months after surgery but is usually progressive and occurs over a long follow-up period (≥ 12 months); however, only those with baseline EF ≥ 60% will usually recover to a normal EF. This reinforces our understanding that contemplation of early surgical intervention for severe mitral regurgitation secondary to leaflet prolapse will most likely yield a favourable effect on LV remodelling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".