Ablation Index Outcome in Redo Persistent Atrial Fibrillation Ablation: Results of a Short-Term Study
Bibliographic record
Abstract
Background: Ablation index (AI) is a novel catheter-based parameter that has improved the outcome and safety of radiofrequency (RF) ablation of pulmonary vein isolations (PVIs). This index incorporates contact force (CF) (g), time (s), and power (W) parameters. The role of AI in redo ablations for persistent atrial fibrillation (peAF) has not been fully investigated. Hence, the impact of AI on the success of the redo PVI during the short-term follow-up period is the aim of this study. Methods: A retrospective analysis of 39 consecutive patients who underwent redo PVI ablations for peAF was carried out between January 2016 and December 2018. Target values for AI were 500 - 550 for anterior and roof and 400 - 380 for posterior and inferior regions. We compared outcomes between AI-guided and catheter CF ablations (i.e., forced time integral (FTI) of more than 400 g/s) during a follow-up of 24 months. Results: Pulmonary vein reconnections at redo procedure were similar in both groups (P = 0.1). AF free burden period was non-significant (mean 15.53 ± 2.4 months in AI group vs. 15.22 ± 1.9 months in CF group, P = 0.79) at 24 months. The AI group demonstrated greater numbers of patients for whom anti-arrhythmic therapy could be de-escalated over 1 year (n = 11 (65%) in AI vs. n = 6 (27%) in CF, P = 0.02). Fewer patients underwent escalation of their anti-arrhythmic therapy (n = 2 (12%) in AI vs. n = 7 (32%) in CF, P = 0.15). The AI group trended towards a shorter procedure time (111.6 ± 27 min) compared to the CF group (133 ± 40 min) (P = 0.06). Other procedural details were comparable. Conclusion: Redo PVI interventions using AI lead to a significant de-escalation in medication during follow-up. Procedure time and radiation dose using AI tends to be shorter. Both techniques are safe with minimal complications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".