Left atrial appendage exclusion during open cardiac surgery in patients without atrial fibrillation reduces 4-year ischemic stroke and mortality
Bibliographic record
Abstract
Objective This study assessed the influence of surgical left atrial appendage exclusion (LAAE) during cardiac surgery in patients with no preoperative history of atrial fibrillation (AF). Methods Real World Data Insights, an all-payers' claims database, with approximately 90% Medicare patients was utilized. Patients with no preoperative history of AF (older than age 65 years) undergoing open coronary artery bypass or valve procedures with/without concomitant surgical LAAE with an epicardial clip between 2015 and 2020 and a minimum of 2-year follow-up were included. Inverse probability treatment weighting and logistic regression were used. Results Open coronary artery bypass represented 48.8% (n = 29,954) and valve 51.2% (n = 31,466) of procedures after adjustment. Thirty-day postoperative AF was present in 12.2% patients (n = 175) for LAAE and 5.8% (n = 3485) for no-LAAE ( P < .01). By day 90 after surgery, rates of new AF were similar between groups through 4-year follow-up. During 4 years of follow-up more patients received oral anticoagulation with LAAE ( P < .01). LAAE had 28% lower adjusted ischemic stroke odds (odds ratio, 0.72; 95% CI, 0.53-0.98; P = .02) and 34% lower adjusted all-cause mortality (odds ratio, 0.66; 95% CI, 0.52-0.85; P < .01). In patients who developed postoperative AF, LAAE + oral anticoagulation showed 74% lower odds of adjusted ischemic stroke (odds ratio, 0.26; 95% CI, 0.10-0.70; P = .01) and 58% lower odds of adjusted all-cause mortality (odds ratio, 0.42; 95% CI, 0.18-1.01; P = .05) than no-LAAE + oral anticoagulation therapy alone. Conclusions LAAE during open cardiac surgery in patients without AF was safe, associated with higher postoperative AF, and lower ischemic stroke and all-cause mortality. Randomized controlled studies are ongoing in a similar population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".