Association of tricuspid regurgitation with clinical events and quality of life after surgery for severe ischemic mitral regurgitation
Bibliographic record
Abstract
Background: The clinical consequences of coexistent tricuspid regurgitation (TR) in patients with severe ischemic mitral regurgitation (IMR) remain unclear. We examined the association of baseline TR severity with outcomes after mitral valve (MV) surgery for IMR. Methods: We conducted a secondary analysis of a randomized trial evaluating the effectiveness and safety of MV replacement versus repair for severe IMR. Patients were stratified by baseline TR (none/trace/mild vs moderate/severe). The primary endpoint was all-cause mortality. Secondary endpoints included major adverse cardiac and cerebrovascular events (MACCE) and quality of life (QoL) using the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Cox proportional hazards and logistic models were used in the analysis. Results: Of 251 randomized patients with severe IMR, 246 undergoing MV repair or replacement (123 each) were included in this secondary analysis. Sixty-one patients (25%) had ≥ severe TR, of whom 43% underwent MV repair and 57% underwent MV replacement. The 2-year all-cause mortality was significantly higher for those with moderate/severe TR compared to those with none/trace/mild TR (38% vs 16%; adjusted hazard ratio [aHR], 2.93; 95% confidence interval [CI], 1.59-5.38). MACCE rates were higher in patients with moderate/severe TR (53%) compared to those with none/trace/mild TR (39%) (aHR, 1.91; 95% CI, 1.21-3.03). No significant difference in 1-year QoL, measured as being alive with a 5-point improvement in the MLHFQ, was observed (odds ratio, 0.61; 95% CI, 0.28-1.33). Conclusions: In patients undergoing surgery for severe IMR, preoperative moderate/severe TR was significantly associated with increased all-cause mortality and MACCE. Whether concomitant TV surgery would improve postoperative outcomes in patients with severe IMR and different degrees of TR should be evaluated in a randomized trial.
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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 | 0.003 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".