Late referrals and high mortality in tricuspid regurgitation: a call for timely intervention
Bibliographic record
Abstract
Abstract Aims Tricuspid regurgitation (TR) is associated with increased morbidity and mortality. The optimal timing for referral and intervention remains uncertain. To evaluate outcomes in patients with TR referred for tricuspid valve intervention. Methods and results Fifty-eight consecutive patients were referred from May 2018 to April 2023. Patients were stratified into two groups: Group 1 who underwent either tricuspid valve transcatheter edge-to-edge repair (T-TEER) or transcatheter tricuspid valve replacement (TTVR); Group 2 who died without intervention due to: awaiting candidacy assessment; awaiting intervention; deemed unsuitable for intervention. Key endpoints: in-patient, 30-day, 12- and 18-month mortality; new pacemaker implantation; echocardiographic TR grading; improvement in NYHA functional class; and heart failure-related readmissions at 30 days and 12 months. Among 58 patients, 43 underwent intervention (TTVR, n = 29; T-TEER, n = 14), 15 died without intervention (awaiting assessment n = 11; awaiting procedure n = 1, unsuitable n = 3). At the time of referral, the mean age was 77.0 ± 9.8 years, and 52 patients (90%) were diagnosed with functional TR; 30-day mortality in Group 1 was 12%, and 12-month mortality reached 33%, with heart failure readmission (37%); 12-month mortality in Group 2 was 73%. At 18 months, mortality reached 37% in Group 1 and 100% in Group 2. Baseline characteristics differed significantly between the groups for body mass index, severity of TR (massive or torrential), NYHA III–IV symptoms, and validated mortality scores. Conclusion Referrals for TR often occur after substantial comorbidities have developed resulting in high mortality but should be considered for a referral and intervention at an earlier stage.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".