Temporal Trends in Atrial Fibrillation Ablation in the Elderly
Bibliographic record
Abstract
BACKGROUND: The elderly population with atrial fibrillation (AF) is growing. There is limited evidence to suggest AF ablation as an effective treatment for the elderly. OBJECTIVES: This study aimed to investigate the temporal trends of first-time ablations in the elderly, the impact of age on major adverse cardiovascular events (MACE), and a composite endpoint of AF-related hospitalizations, repeat AF ablation, or use of antiarrhythmic drugs (AADs). METHODS: Utilizing the Danish administrative registers, we incorporated individuals undergoing their first-time AF ablation from 2001 to 2020. Our cohort was divided into 3 age groups (<60, 60-74, and ≥75 years) and scrutinized across 4 consecutive 5-year intervals. Cox proportional-hazard multivariable analyses and cumulative incidences were used to evaluate the endpoints of 5-year MACE incidence and a 1-year composite endpoint of AF-related hospitalizations, repeat AF ablation, or use of antiarrhythmic drugs. RESULTS: Elderly patients who underwent AF ablation increased significantly, from none in 2001 to 9% in 2020. The 5-year incidence of MACE in the elderly decreased from 61.9% (95% CI: 41.1%-82.7%) to 38.1% (95% CI: 31.9%-44.2%). The HR for age ≥75 years in the last time period was 1.52 (95% CI: 1.26-1.83). The 1-year composite outcome varied from 35.6% to 52.0%; age was not a consistent predictor. CONCLUSIONS: AF ablation use in the elderly has significantly increased over time. A notable decrease in MACE was evident across all age cohorts, with a particularly pronounced trend observed among the elderly population. Age was not an independent predictor of the composite endpoint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".