Prevalence of Left Atrial Appendage Thrombus in Patients Anticoagulated With Direct Oral Anticoagulants: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Multiple studies have examined the prevalence of left atrial appendage thrombus (LAAT) in patients anticoagulated with direct oral anticoagulants (DOACs) and have reported conflicting results. Methods Studies reporting the prevalence of LAAT on transesophageal echocardiography (TEE) after 3 or more weeks of DOAC therapy were identified. The proportions of anticoagulated patients diagnosed with LAAT were pooled using random-effects models. Prespecified subgroup analyses by the indication of TEE (pre–atrial fibrillation [AF] ablation vs cardioversion) and TEE strategy (routine use vs selective) were conducted via stratification. Results Forty studies were identified: 22 full manuscripts and 18 abstracts. Only 11 studies performed TEE routinely. Most studies included patients with paroxysmal AF and low thromboembolic risk. The pooled prevalence of LAAT was 2.5% (95% confidence interval [1.6%-3.4%]). The prevalence of LAAT is lower in the pre-AF ablation group compared with pre-cardioversion (1.1% vs 4.0%, P = 0.033). Routine TEE strategy yielded a lower LAAT prevalence in both groups (0.1% vs 2.3%, P = 0.002 and 3.2% vs 5.8%, P = 0.432, respectively). Conclusion The reported prevalence of LAAT on TEE in patients treated with DOACs is highly variable. Factors associated with a high LAAT prevalence were pre-cardioversion indication and selective TEE strategy. Routine use of TEE before AF ablation may not be warranted.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".