Left ventricular ejection fraction and benefit of tricuspid valve interventions: Insights from the international TRIGISTRY
Bibliographic record
Abstract
Abstract Background The impact of treatment for tricuspid regurgitation (TR) across different levels of left ventricular ejection fraction (LVEF) remains uncertain. Purpose This study aimed to compare the outcomes of surgical and transcatheter tricuspid valve interventions (TTVI) to conservative (medical) management across all LVEF categories. Methods Patients with severe isolated TR from the TRIGISTRY, a multicenter international registry, were categorized based on LVEF (preserved EF-pEF:≥50%, mildly reduced EF-mrEF:41–49%, and reduced EF-rEF:≤40%). We assessed the impact of treatment modality and procedural success, defined as residual TR ≤mild-to-moderate, on two-year survival within each LVEF category. Results Among 2,384 patients, 1,383 had pEF, 400 had mrEF, and 601 had rEF. Compared to conservative management, surgery (P<0.0005) and TTVI (P<0.0001) were associated with a survival benefit in patients with pEF. No significant survival advantage was observed in patients with mrEF (P=0.28 for both), nor in those with rEF (P=0.76 and P=0.22 respectively). Similar results were obtained when surgical and transcatheter interventions were grouped together (P<0.0001, P=0.17 and P=0.29 in patients with pEF, mrEF and rEF respectively). Patients with significant residual TR after TTVI exhibited a trend toward worse survival compared to those managed conservatively across all LVEF categories (P=0.47, P=0.33, and P=0.008 respectively)(Figure 1). Conclusion Tricuspid valve intervention, whether surgical or transcatheter-based, was associated with improved survival in patients with preserved LVEF but not in those with mildly reduced or reduced LVEF. Residual TR remained a significant prognostic factor across the entire LVEF spectrum. These findings highlight the need for careful patient selection when considering tricuspid interventions in individuals with reduced LVEF.Figure 1
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".