2 Ablation of scar-related ventricular tachycardia: paced electrogram feature analysis (PEFA) is a novel and effective substrate based strategy
Bibliographic record
Abstract
Background Ablation of scar related ventricular tachycardia (VT) has been shown to be superior to escalation of drug therapy. However, the incremental benefit remains modest, with 42% experiencing recurrent shocks and 64% appropriate anti-tachycardia pacing (ATP) in the VANISH study. Furthermore, VT induction to allow activation mapping and entrainment is not tolerated in 70% of cases. Improved substrate based ablation strategies are needed. Paced electrogram feature analysis (PEFA) is a novel and promising technique. This method utilizes close coupled extra-stimuli to reveal latency and increased electrogram (EGM) duration (figure 1) that is evident at critical VT isthmus(es) (figure 2). Purpose To investigate the effectiveness of PEFA based VT ablation. Methods A single centre, prospective study. Consecutive cases of scar related VT that had an implantable cardiac defibrillator (ICD) and no prior ablations were recruited. Close coupled pacing was performed at the right ventricular apex (VERP + 50 ms) and the VT isthmus(es) identified on high density mapping catheters (HD Grid™ or Reflexion™, St Jude) by increased electrogram (EGM) duration and latency (figure 1). An algorithm was developed to identify the latest EGM component after the S2 pacing artefact (St Jude EnSite Precision Electroanatomic Mapping). The amplitude sensitivity was set at 0.05 mV and manual assessment was used to correct the automated annotation when visibly inaccurate. This millisecond value was displayed on the geometry as a colour (PEFA map) (figure 2). PEFA identified VT isthmus sites were targeted for ablation (figure 1). The PEFA map was repeated to ensure comprehensive abolition. VT stim protocol with three extra-stimuli was performed at the end of each case. Follow up ICD interrogation data was utilized to assess for VT recurrence and mortality. Results A total of 23 cases were recruited. table 1 provides an overview of baseline characteristics. table 2 includes procedure details and outcomes. Conclusion PEFA based VT ablation is feasible and effective. A high proportion of cases were non-inducible, with low VT recurrence rates. This is the largest dataset to date on PEFA based VT ablation, the first to include non-ischaemic aetiologies, and reports a longer mean follow up. PEFA appears to be a promising substrate based VT ablation strategy.
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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 | 0.001 |
| 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".