Transfabric Leaks After Percutaneous Left Atrial Appendage Occlusion Procedures with the WATCHMAN FLX Device
Bibliographic record
Abstract
Background: Cardiac computed tomography imaging with contrast is being used increasingly to image left atrial appendage occlusion (LAAO) devices. Contrast flow across a device, also known as a transfabric leak (TFL), may indicate a lack of complete LAAO-device endothelialization. The data on the rate, predictors, and clinical events associated with TFL are limited. Methods: All patients who underwent an LAAO-device implantation with a WATCHMAN FLX device and received a postimplantation computed tomography scan were included in this single-centre retrospective cohort study. Patients were classified as either having or not having a TFL, according to 3 currently proposed definitions of TFL. Clinical and procedural differences between the 2 groups were determined. An exploratory univariate logistic regression model to evaluate predictors of TFL was constructed. Results: A total of 56 individuals were included in the cohort. The rate of TFL varied from 27% to 52%, depending on the radiographic definition employed. No clinically important patient or procedural characteristics were noted between the groups with vs without TFL. No predictors of TFL were identified. Six deaths and one stroke occurred during a median follow-up period of 673 days. Conclusions: TFLs occur commonly post-LAAO procedures, suggesting that complete endothelialization of LAAO devices in humans may not be similar to that reported in animal models. Additional study into the best imaging approach to identify TFLs, and clinical events associated with TFLs, is necessary to clarify the significance of TFLs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".