Effects of baroreceptor reflex modification on efficacy of ablation for atrial fibrillation
Bibliographic record
Abstract
Abstract Background The autonomic nervous system (ANS) plays a significant role in atrial fibrillation (AF). Catheter ablation (CA) is a well-established treatment method for AF and significantly affects ANS, including baroreceptors (BR) function. However, little is known about the changes in the BR function caused by radiofrequency (RF) or cryo-energy (CB) influence the efficacy of AF ablation. Purpose To assess one-year efficacy of CA of AF in relation to BR function modification and type of ablation energy used. Methods The study group consisted of 78 patients (25 females, mean age 58±9 years) with paroxysmal AF and first CA (39 patients (RF group) and 39 (CB group)). The BR function was assessed non-invasively using tilt testing and three parameters: event count (BREC) depicting overall BR activity, slope mean depicting BR sensitivity (BRS) and BR effectiveness index (BEI). The efficacy of CA was assessed at 3, 6, and 12 months after CA using 24-hour Holter ECG recordings and dedicated scale (University of Toronto Atrial Fibrillation Severity Scale (AFSS)). Results After CA, BR function decreased in the whole group (mean BREC 12.0±3.0-22.0 vs 6.0±3.0-18.0, p=0.004; mean BRS 4.8±3.6-6.8 vs 4.0±3.0-5.8, p=0.014; mean BEI 18.7±8.3-27.4 vs 12.0±5.1-21.0, p=0.009). BREC was significantly more decreased in the CB vs RF (mean 12.0±3.0-22.0 vs 6.0±3.0-18.0, p=0.004). Similar trend was noted for BRS and BEI. On repeated Holter ECG monitorings efficacy of CB and RF was similar (89.7% vs 82.9% p=0.502, 89.7 vs 85.7%; p=0.727 and 84.6 vs 80%; p=0.602, respectively). According to AFSS, the AF symptoms were significantly reduced in both groups (CB baseline vs 3, 6 a and 12 months, respectively (median): 6.0 (IQR 2.0 – 13.0) vs 2.5 (IQR 1.0 – 4.0), p<0.001; 6.0 (IQR 2.0 – 13.0) vs 1.5 (IQR 0.0 – 4.0), p<0.001; 6.0 (IQR 2.0 – 13.0) vs 1.5 (IQR 0.0 – 4.0), p=0.002; RF baseline vs 3, 6 and 12 months, respectively (median): 10.5 (IQR 5.0 – 14.0) vs 7.0 (IQR 1.0 – 12.0), p=0.173; 10.5 (IQR 5.0 – 14.0) vs: 6.0 (IQR 2.0 – 12.0), p=0.015; 10.5 (IQR 5.0 – 14.0) vs 6.5 (IQR 2.0 – 12.0), p=0.005). After the adjustment for baseline there was no significant differencies between both groups (Table). Conclusions CA significantly affected BR function. These changes were more pronounced following CB than RF CA. However, the efficacy of RF and CB was similar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".