Aortic Atherosclerosis Detection on Transesophageal Echocardiography is Associated with Left Atrial Appendage Thrombus in Low Thromboembolic Risk Patients
Bibliographic record
Abstract
Background: Elevated CHA2DS2-VASc scores are considered to be predictors of left atrial appendage (LAA) thrombus (LAAT); however, individuals with low scores remain at risk. Studies have indicated that aortic atherosclerosis (AA) is associated with increased stroke risk. AA on transoesophageal echocardiography (TOE) has been overlooked as a ‘vascular’ variable in the CHA2DS2-VASc score. Aims: Determine the prevalence of LAAT in patients with low thromboembolic risk and the correlation of AA with LAAT. Methods: We performed a retrospective review of all TOEs performed for patients who underwent electrophysiology procedures at the McGill University Health Centre from 2012 to 2017 and collected pertinent clinical and echocardiography variables. We reviewed all TOEs to evaluate the presence and severity of AA using the Katz score, American Society of Echocardiography (ASE) grade and the Ferrari score. In patients with a CHADS2 of 0 and CHA2DS2-VASc score of ≤1, logistical regression and receiver operating characteristic curves were used to identify predictors for LAAT. Results: 592 patients underwent a pre-procedure TOE and were included in the analysis. Among 249 patients with CHA2DS2-VASc scores ≤1, 7.5% had LAA. AA burden by Katz score was an independent predictor of LAAT (area under the curve (AUC) 0.76 95% CI [0.60–0.92]) for CHA2DS2-VASc ≤1. Conclusion: AA visualised on TOE was significantly associated with an increased risk of LAAT development in patients with low CHA2DS2-VASc scores. Incorporating AA assessment into risk stratification may enhance clinical decision-making for the use of anticoagulation for patients with AF. Future studies are warranted to evaluate the use of other imaging modalities for AA detection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".