Abstract 11924: Guiding Strategies in Tricuspid Edge-to-Edge Intervention
Bibliographic record
Abstract
Introduction: Echocardiographical guiding gains further importance as transcatheter interventions for heart valve diseases advance. In transcatheter edge-to-edge repair for tricuspid regurgitation (TEER-TR), the use of transesophageal echocardiography (TEE) employing biplane imaging with views perpendicular to the inflow-outflow-view is suggested. However, robust data on echocardiographical guiding in this setting is lacking, as well as on the role of fluoroscopy settings. Hypothesis: The aim of the study was to evaluate different guiding-approaches and fluoroscopy settings in TEER-TR procedures. Methods: We retrospectively assessed the effect of different echocardiographical guiding strategies and fluoroscopy angulations on immediate TR reduction (ΔTR), and device time in the TEER-TR interventions. Echocardiographical guiding was rated using a new quality scoring system, MACES 3 . Results: From July 2016 to December 2019 a total of 190 cases of TEER-TR procedures were conducted. In all but 2, the procedure was successfully completed. Device deployment was achieved using transesophageal imaging in 65 cases (n[mid-esophageal]=47, (midE); n[deep-esophageal]=18, (deepE)), while live-MPR was employed 4 times. The transgastric en-face view (TG) constituted the grasping view 119 times (Fig. 1). The best imaging quality with the highest MACES 3 scores were observed in deepE and TG. MACES 3 scores significantly influenced the acute outcome. In cases of secondary TR, the use of TG was superior to other imaging in achieving a ΔTR of ≥ 2 grades. Both the use of TG and special fluoroscopy alignment significantly reduced the device time. Conclusions: TEER-TR can be effectively guided using different echocardiographic strategies. To strive for the best imaging quality clearly increases procedural success. Using the TG approach offers benefits in immediate procedural result and device time, with the latter further reduced using fluoroscopic alignment.
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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 | 0.001 | 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 | 0.000 |
| 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".