Relationship Between Diuretic Dose and Outcome Following Transcatheter Tricuspid Valve Repair for Severe Tricuspid Regurgitation
Bibliographic record
Abstract
BACKGROUND: Severe tricuspid regurgitation (TR) leads to right heart volume overload and failure. Diuretic treatment is a cornerstone of medical therapy, being so far the only option in inoperable patients. Transcatheter tricuspid valve repair (TTVr) has become a viable treatment for these patients. In this study we evaluated the association between baseline loop diuretic dose and clinical outcomes after TTVr in patients with severe symptomatic TR. METHODS: In this retrospective, single-center study, symptomatic patients with severe TR treated between 2017 and 2022 with TTVr were analyzed for the combined endpoint of heart failure hospitalization (HFH) or death at 1-year follow-up, depending on loop diuretic dose. RESULTS: For the 225 patients (median age 80 years, 67% women) analyzed, the receiver-operating characteristic curve revealed an optimal cutoff of 95-mg furosemide-equivalent, stratifying into high-diuretic-dose (HDD) and low-diuretic-dose (LDD) groups. Intraprocedural success according to Tricuspid Valve Academic Research Consortium criteria was poorer in HDD patients (46% vs 68%, P = 0.002), as were outcomes (1-year mortality: 44% vs 11%, P < 0.001; HFH: 22% vs 10%, P < 0.001). HDD was independently associated with the combined endpoint (hazard ratio 2.498, 95% confidence interval 1.537-4.058, P < 0.001). New York Heart Association classification improved by 1 class or more in 68% of patients. Overall, the median diuretic dose at follow-up (median 388 days, interquartile range 362-537 days) remained stable. CONCLUSIONS: Patients with HDD undergoing TTVr had worse procedural and clinical outcomes. However, they experienced symptom relief without diuretic escalation. Diuretic increases in patients with severe TR should be closely monitored and may indicate a need for prompt TTVr evaluation. This may prevent diuretic-related side effects and improve outcomes.
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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 | 0.004 |
| 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 | 0.001 | 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".