Abstract 11042: New-Onset Atrial Fibrillation After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: New-onset atrial fibrillation (NOAF) is a common complication after transcatheter aortic valve replacement (TAVR), though estimates of the precise incidence are variable. We sought to quantify the incidence of NOAF after TAVR, explore the associated outcomes and identify predictors for this complication. Methods: Using a broad strategy, we searched Medline, EMBASE and the Cochrane database from 2015-2020 for articles that reported any outcomes of TAVR. We extracted data for studies published prior to 2015 from a previous systematic review (22 studies in total). Reviewers performed screening and data extraction in duplicate. We pooled data using a random effects model with Mantel-Haenszel weighting. Results: We identified 183 studies with 296,986 total participants that reported NOAF from 2008 to 2020. The pooled incidence of NOAF after TAVR was 9.9% (95%CI 8.1-12%). NOAF after TAVR was associated with longer index hospitalization (MD 2.66 days, 95% CI 1.05-4.27), higher risk of stroke (RR 1.65, 95% CI 1.09-2.5) and 30-day mortality (RR 1.76, 95%CI 1.12-2.76). NOAF after TAVR was also associated with increased risk of major or life-threatening bleeding (RR 1.60, 95%CI 1.39-1.84) and new permanent pacemaker implantation (RR 1.12, 95%CI 1.05-1.18). Risk factors for the development of NOAF after TAVR included trans-apical access, pulmonary hypertension, chronic kidney disease, peripheral vascular disease, and severe mitral regurgitation. Conclusions: NOAF is common after TAVR and associated with a longer hospital stay, a higher risk of stroke, major bleeding, mortality and permanent pacemaker implantation. Whether this risk is modifiable requires further study.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".