Heart Failure Hospitalizations and Clinical Outcomes in Patients Undergoing Tricuspid Transcatheter Edge-To-Edge Repair: Insights from EuroTR
Bibliographic record
Abstract
AIMS: To assess the prevalence, prognostic significance, and predictors of heart failure hospitalization (HFH) before and after tricuspid transcatheter edge-to-edge repair (T-TEER) in a large real-world cohort of patients with tricuspid regurgitation (TR). METHODS AND RESULTS: Data from the European Registry of Transcatheter Repair for Tricuspid Regurgitation (EuroTR registry) were analysed. Among 1000 patients undergoing T-TEER for symptomatic TR, 361 (36.1%) had no HFH, 459 (45.9%) had one single HFH, and 180 (18.0%) had multiple HFH the year before T-TEER. Patients with any HFH had more severe heart failure compared with those without. Procedural success (residual TR ≤2) did not differ between patients with single, multiple, or no HFHs before T-TEER. Multivariable analysis showed that a history of HFH was associated with an increased mortality risk (adjusted hazard ratio [HR] 1.51, 95% confidence interval [CI] 1.11-2.06 for single vs. no HFH; adjusted HR 1.63, 95% CI 1.15-2.31 for multiple vs. no HFH), and a higher risk of the combined endpoint of all-cause mortality or HFH. HFH risk decreased by 72% in the 1 year following T-TEER compared to the previous year. Procedural success was the sole independent predictor for reducing HFHs. CONCLUSIONS: In the EuroTR cohort, a history of HFH was highly prevalent and associated with worse clinical outcomes. Among high-risk patients with symptomatic TR, T-TEER significantly lowered HFH risk, with residual TR grade ≤2 being the key predictor for reduced HFH incidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".