Abstract 13877: 5-Year Impact of Catheter Ablation for Atrial Fibrillation in Patients With a Prior History of Stroke
Bibliographic record
Abstract
Background: Catheter ablation of atrial fibrillation (AF) is an established therapeutic rhythm approach. Patients with a prior history of a stroke (CVA) represent a unique high-risk population for recurrent thromboembolic events. The role of antiarrhythmic treatment on the long-term natural history of stroke recurrence in these high patients is not fully understood. Hypothesis: The process of AF ablation will impact the long-term history of disease progression in high risk CVA patients. Methods: 3 patient groups with a prior CVA and 5 years of follow-up were matched 1:3:3 by propensity score (±0.01): AF ablation patients receiving their first ablation (n=139), AF patients that did not receive an ablation (n=416), and CVA patients without clinical AF (n=416). Prior CVA was determined by medical chart review using ICD 9/10 codes. Patients were followed for 5-year outcomes of recurrent CVA, heart failure, death. Results: The average age of the population was 69±11 years and 51% male. The AF was paroxysmal in 25%, persistent in 34.7%, and longstanding persistent in 41%. AF ablation patients had higher rates of hypertension (p<0.0001), but diabetes prevalence was similar between the groups (p=0.50). Propensity-matched 5-year risk of CVA (HR=2.26, p<0.0001, Figure) and death (HR=2.43, p<0.0001) were higher in the AF, no ablation group compared those that were ablated. When comparing AF, ablation to no AF patients, there was not a significant difference in 5-year risk of for CVA (HR=0.82, p=0.39) and death (HR=0.92, p=0.70), however heart failure risk was increased (HR=3.08, p=0.001). Conclusion: In patients with a prior CVA, the system and approach towards the management of AF in patients that undergo ablation is associated with lower rates of recurrent stroke compared to AF patients that do not undergo ablation. The CVA rates are similar to a non AF population to suggest the potential of altering the natural history of disease progression.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".