Clinical Experience With a Novel Electromyographic Approach to Preventing Phrenic Nerve Injury During Cryoballoon Ablation in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Phrenic nerve palsy remains the most frequent complication associated with cryoballoon-based pulmonary vein (PV) isolation. We sought to characterize our experience using a novel monitoring technique for the prevention of phrenic nerve palsy. METHODS AND RESULTS: Two hundred consecutive cryoballoon-based PV isolation procedures between October 2010 and October 2013 were studied. In addition to standard abdominal palpation during right phrenic nerve pacing from the superior vena cava, all patients underwent diaphragmatic electromyographic monitoring using surface electrodes. Cryoablation was terminated on any perceived reduction in diaphragmatic motion or a 30% decrease in the compound motor action potential (CMAP). During right-sided ablation, a ≥30% reduction in CMAP amplitude occurred in 49 patients (24.5%). Diaphragmatic motion decreased in 30 of 49 patients and was preceded by a 30% reduction in CMAP amplitude in all. In 82% of cases, this reduction in CMAP amplitude occurred during right superior PV isolation. The baseline CMAP amplitude was 946.5±609.2 mV and decreased by 13.8±13.8% at the end of application. This decrease was more marked in the 33 PVs with a reduction in diaphragmatic motion than in those without (40.9±15.3% versus 11.3±10.5%; P<0.001). In 3 cases, phrenic nerve palsy persisted beyond the end of the procedure, with all cases recovering within 6 months. Despite the shortened application all veins were isolated. At repeat procedure the right-sided PVs reconnected less frequently than the left-sided PVs in those with phrenic nerve palsy. CONCLUSIONS: Electromyographic phrenic nerve monitoring using the surface CMAP is reliable, easy to perform, and offers an early warning to impending phrenic nerve injury.
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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 | 0.003 |
| 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".