Pulmonary Vein Isolation in Elderly Patients ≥ 75 Years: A Propensity Score-Matched Analysis With Focus on Differences Among Atrial Fibrillation Types
Bibliographic record
Abstract
BACKGROUND: Age is a major risk factor for development of atrial fibrillation (AF) and associated with increased recurrence rates in the setting of rhythm control. Current data tend to support catheter ablation in elderly patients, but uncertainties exist regarding efficacy and safety of ablation in elderly patients. METHODS: This was a prospective single-centre observational study with propensity score matching (PSM) to investigate the influence of age on efficacy and safety of cryoballoon ablation (CBA) stratified by age (< 75 years vs ≥ 75 years) and AF phenotype (paroxysmal vs persistent). Primary efficacy endpoint was recurrence of atrial arrhythmia after a 90-day blanking period. Safety endpoints were death, stroke, or procedure-associated complications. RESULTS: Consecutive patients (n = 953) underwent CBA for first-time AF ablation. Median follow-up was 18 months. By means of PSM, 268 matches were formed. At 1 year, primary efficacy endpoint occurred in 22.4% of young vs 33.2% of elderly patients, including both AF phenotypes (hazard ratio [HR], 0.65; 95% confidence interval [CI], 0.47-0.90; P = 0.01). AF relapse occurred in 19.7% of young vs 28.5% of elderly patients with paroxysmal (HR, 0.63; 95% CI, 0.40-0.99; P = 0.046) compared with 25.9% (30 of 116, young) vs 38.8% (45 of 116, elderly) patients with persistent AF (HR, 0.62; 95% CI, 0.39-0.97; P = 0.038). No difference was observed regarding the incidence of safety endpoints between young and elderly patients (P = 0.38). CONCLUSIONS: CBA is associated with higher recurrence rates in elderly (≥ 75 years) than in younger patients, with highest recurrence rates in elderly patients with persistent AF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".