Lead-Specific Features Predisposing to the Development of Tricuspid Regurgitation After Endocardial Lead Implantation
Bibliographic record
Abstract
Background Endocardial lead in the right ventricle is recognized as a cause for tricuspid regurgitation (TR), but the mechanism remains elusive. We sought to evaluate lead-specific features on the development of TR after endocardial lead implantation. Methods This was a prospective single-center study. The patients underwent 2-dimensional echocardiograms before endocardial lead implantation and at follow-up visits at 4 to 6 weeks, 6 months, and 12 months. We assessed the position of the endocardial lead at the tricuspid annulus by 3-dimensional echocardiography, the tricuspid leaflet interference by the endocardial lead by both 2- and 3-dimensional echocardiography, and the degree of lead slack radiologically. Patient characteristics and lead-related factors were evaluated in the prediction of new or worse TR by univariable and multivariable analyses. Results New or increased TR was detected in 38 of 128 patients at the 12-month follow-up. The postero-septal commissure was the most common lead position, and tricuspid leaflet interference detected in 21 patients was associated with a noncommissural lead position. The implantation of an implantable cardioverter defibrillator lead was not associated with new TR compared with the implantation of a pacemaker lead. Tricuspid leaflet interference ( P < 0.0001), but not lead position or lead slack, was the only lead-specific factor associated with the development of TR. Conclusion After right ventricle endocardial lead implantation, leaflet interference determined by echocardiography, but not the nature of the lead, the lead position at the tricuspid annulus, and the radiological lead slack, predicted TR development at 1 year postimplantation.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".