Edge-to-edge repair for functional mitral regurgitation: an echocardiographic study of the hemodynamic consequences.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: The study aim was to characterize changes in mitral valve area and flow, and left ventricular (LV) size and function, following edge-to-edge (E-E) repair for severe functional mitral regurgitation (MR). The possibility that preoperative dobutamine stress echocardiography (DSE) might be used to predict post-repair recovery in LV function was also examined. METHODS: Seventeen patients underwent preoperative transthoracic echocardiography (TTE) and DSE, intraoperative transesophageal echocardiography, and three-month postoperative TTE. RESULTS: After repair, mitral valve area was reduced from 8.5 +/- 1.9 cm2 to 3.8 +/- 0.9 cm2 by planimetry (p < 0.0001) and to 2.9 +/- 0.9 cm2 by pressure half-time. Valve area by pressure half-time correlated with the planimetered area (r = +0.55), but was consistently lower (p = 0.004). Sixxteen of 17 patients had mean transmitral gradients <5 mmHg. Postoperative LV end-diastolic diameter improved from 72 +/- 11 to 64 +/- 10 mm (p < 0.01), and end-systolic diameter from 56 +/- 14 to 46 +/- 12 mm (p < 0.05). Mean ejection fraction improved from 25 +/- 12% before repair to 38 +/- 17% after repair (p < 0.02) in patients with evidence of LV function improvement on DSE, but was unchanged (15 +/- 5% versus 17 +/- 5%, p = NS) in patients without evidence of improvement. Postoperatively, 13 patients had no or mild MR, and two patients had moderate MR. There was one perioperative death. CONCLUSION: E-E repair, in combination with ring annuloplasty, reduces LV cavity dimensions and functional MR severity, without causing significant valve stenosis. Improvement on DSE may predict those patients in whom EF will improve following repair.
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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) | 0.001 | 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".