Electrophysiological study prior to planned pulmonary valve replacement in patients with repaired tetralogy of Fallot
Bibliographic record
Abstract
AIM: Ventricular arrhythmias (VAs) are the most common cause of death in patients with repaired Tetralogy of Fallot (rTOF). However, risk stratifying remains challenging. We examined outcomes following programmed ventricular stimulation (PVS) with or without subsequent ablation in patients with rTOF planned for pulmonary valve replacement (PVR). METHODS: We included all consecutive patients with rTOF referred to our institution from 2010 to 2018 aged ≥18 years for PVR. Right ventricular (RV) voltage maps were acquired and PVS was performed from two different sites at baseline, and if non-inducible under isoproterenol. Catheter and/or surgical ablation was performed when patients were inducible or when slow conduction was present in anatomical isthmuses (AIs). Postablation PVS was undertaken to guide implantable cardioverter-defibrillator (ICD) implantation. RESULTS: Seventy-seven patients (36.2 ± 14.3 years old, 71% male) were included. Eighteen were inducible. In 28 patients (17 inducible, 11 non-inducible but with slow conduction) ablation was performed. Five had catheter ablation, surgical cryoablation in 9, both techniques in 14. ICDs were implanted in five patients. During a follow-up of 74 ± 40 months, no sudden cardiac death occurred. Three patients experienced sustained VAs, all were inducible during the initial EP study. Two of them had an ICD (low ejection fraction for one and important risk factor for arrhythmia for the second). No VAs were reported in the non-inducible group (p < .001). CONCLUSION: Preoperative EPS can help identifying patients with rTOF at risk for VAs, providing an opportunity for targeted ablation and may improve decision-making regarding ICD implantation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".