Abstract 17213: Does Left Ventricular - Mitral Ring Mismatch Predict Risk of Recurrent Mitral Regurgitation Post Annuloplasty in Patients With Ischemic Mitral Regurgitation?
Bibliographic record
Abstract
Background: In ischemic mitral regurgitation (IMR), ring annuloplasty is associated with a 30-50% rate of recurrent IMR. Ring size is based on inter-trigonal distance without consideration of LV size. However, LV size is an important determinant of mitral valve (MV) leaflet tethering pre and post repair. We aimed to determine if LV - MV ring mismatch (mismatch of LV size relative to ring size) is associated with recurrent IMR in patients post ring. Methods: Patients with moderate or severe IMR from the two cardiothoracic surgical network IMR trials (NCT-00806988 and 00807040) who received MV repair were examined at 1-year post-surgery. Baseline LV size was assessed by LV end-diastolic (LVIDed) and end-systolic (LVIDes) dimensions. LV- MV ring mismatch was calculated by dividing LV size by ring size (LVIDed/ring and LVIDes/ring). Multivariable regression models for each of these measures adjusting for age, sex, baseline LVEF and severe IMR were used to determine association with recurrent (≥moderate) IMR at one year. Results: 1-year post ring, 45 (21%) of 216 repair patients had ≥moderate IMR. In univariate analysis, larger LVIDes (p=0.02) and higher LVIDes/ring (p=0.006) were associated with recurrent IMR; LVIDed and LVIDed/ring was not (p>0.05). In multivariable models both LVIDes/ring (p=0.01) and LVIDes (p=0.02) remained significantly associated with 1-year IMR recurrence; the odds of recurrence increasing by a relative 72% per 10% increase in LVIDes/ring and by 70% per 10mm increase in LVIDes. Conclusion: We found that increasing LV-MV ring mismatch and LVIDes are both associated with increased risk of IMR recurrence. The high correlation between LVIDes and LVIDes/ring in our data precludes a definitive determination of the relative contribution of each measure to the increased risk of IMR recurrence. While further research is needed to confirm our findings, these data may be helpful in guiding ring size to prevent recurrent IMR in patients undergoing MV 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".