Abstract 16759: Repair of Functional Mitral Regurgitation Restores Life Expectancy in Selected Patients Less Than 70 Years of Age
Bibliographic record
Abstract
Introduction: Functional mitral regurgitation (MR) is common and associated with significant morbidity and mortality. Notably, correction of functional MR has not been demonstrated to improve survival. We therefore evaluated outcomes following repair of functional MR utilizing prospective population data. Hypothesis: We hypothesized that correction of functional MR may be associated with a survival benefit in selected patients. Methods: Between 2001 and 2018, 1896 patients underwent complex mitral surgery at our institution, of which 168 underwent repair of functional MR in whom the etiology of MR was due to annular dilation in 37, leaflet tethering in 100 and mixed in 31. Mean patient age was 66.2±9.9 years and 66 (39%) were female. Concomitant coronary artery bypass grafting was performed in 79 (47%) with an average of 2.5 grafts per patient. The mean preoperative left ventricle (LV) ejection fraction was 38.3±11.4% and indexed LV end-systolic dimension, 25.1±5.4 mm/m 2 . Average clinical and echocardiographic follow up averaged 5.5±3.8 years. Results: Thirty-day mortality was 0.6%. Overall survival for the patients following repair of functional MR was 93.3±2.0% and 75.6±3.8% 1-, and 5-years after surgery. Not surprisingly, sensitivity analysis demonstrated that survival was influenced by age at operation (hazard ratio (HR) 1.05±0.02 per increasing year) and preoperative left ventricle (LV) class (HR 1.32±0.18 per increasing LV grade) (Both p<0.05). For patients <70 years of age and with a preoperative LVEF >35%, 5- year survival was 92.3±7.4%, respectively. This compares to an expected 5- year survival of 99.1%, for age and gender matched Canadians (Figure). For patients <70 and with a preoperative LVEF >35%, 5-year freedom from recurrent MR ≥2+ was 81.3±9.8%. Conclusions: Repair of functional MR is associated with favorable early and intermediate term results in selected patients. Notably, for the subset of functional MR patients <70 and with a LVEF>35%, 5-year survival was comparable to age and gender matched patients from the general population.
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 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) | 0.000 | 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".