Population‐level evaluation of complications after catheter ablation in patients with atrial fibrillation and heart failure
Bibliographic record
Abstract
INTRODUCTION: Catheter ablation (CA) has been increasingly used to treat atrial fibrillation (AF) in patients with heart failure (HF), however, its safety at the population-level has not yet been evaluated. To assess the safety of CA in AF-HF patients, the frequency and potential risk factors for adverse events (AEs) within 30 days post-CA were determined. METHODS: A population-based cohort of AF-HF patients who underwent CA in Quebec, Canada (2000-2017) was constructed using administrative databases. Major AEs included all-cause mortality, cerebrovascular accident (CVA), pericardial effusion requiring drainage (PERD), vascular AEs, hemorrhage/hematoma, and pulmonary embolism. Univariate logistic regression models were employed to assess potential risk factors for major AEs. RESULTS: -Vasc 3 [IQR, 2-4]), 14 (2.0%) patients developed 16 major AEs within 30 days of CA. Hemorrhage/hematoma was the most frequent major AE (four patients; 0.6%) followed by all-cause mortality, CVA/TIA, PERD, and vascular AEs (three patients each; 0.4%). Coronary artery disease (odds ratio [OR], 3.9 [95% confidence interval, CI, 1.2-12.3]) and age ≥65 years (OR, 3.1 [95% CI, 1.1-9.8]) were identified predictors for the composite outcome of major AEs. More than half of the patients (57.2%) underwent a second CA within a median of 0.8 (IQR, 0.2-2.2) years from the date of first CA. CONCLUSION: CA performed in the AF-HF population portends a relatively low incidence of major AEs. A larger study is required to determine whether certain patient factors are independently associated with a higher risk of post-CA AEs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".