Outcomes of Left Atrial Appendage Patency After Device Closure Detected by Cardiac CT
Bibliographic record
Abstract
BACKGROUND: Residual leaks are common after left atrial appendage occlusion (LAAO). OBJECTIVES: The authors aimed to systematically evaluate the prognostic implications of residual left atrial appendage (LAA) patency and peridevice leaks (PDLs) detected by cardiac computed tomography (CT) following LAAO. METHODS: The authors used traditional meta-analytical methods and a Bayesian framework to assess the probability of increased risks associated with these residual leaks. RESULTS: Seventeen studies encompassing 2,036 patients met the inclusion criteria. During follow-up, the presence of any LAA patency was significantly associated with an almost 2-fold increased OR of thromboembolism (pooled OR: 1.87, 95% CI: 1.08-3.24), corresponding to 28 (95% CI: 3-68) additional events per 1,000 patients. Although PDL showed a similar trend, it was not significantly associated with increased thromboembolism risk (pooled OR: 1.50, 95% CI: 0.85-2.65). For thromboembolism with LAA patency, under a noninformative prior, the mean logOR was 0.59 (95% credible interval [Crl]: 0.06-1.15), and translated median OR of 1.87 (95% CrI: 1.06-3.14) and a mean absolute risk difference (ARD) of 2.8% (95% CrI: 0.2%-6.8%). The posterior probability of ARD >0% was 98.5%. For any PDL, the mean logOR was 0.38 (95% CrI: -0.22 to 0.95), with a median OR of 1.53 (95% CrI: 0.80-2.59) and mean ARD of 1.9% (95% CrI: -0.7% to 5.4%). The posterior probability of ARD >0% was 89.8%. CONCLUSIONS: LAA patency and PDL identified by CT may be associated with an increased risk of thromboembolism. These findings indicate that residual leaks detected by CT may be clinically significant and should not be regarded as benign.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".