First-in-human trial of atrial fibrillation ablation using real-time tissue optical assessment to predict pulsed field lesion durability
Bibliographic record
Abstract
AIMS: Loss of bipolar electrograms immediately after pulsed field ablation (PFA) makes lesion durability assessment challenging. OBJECTIVE: The aim of this trial (NCT06700226) was to evaluate a novel ablation system that can optically predict lesion durability by detecting structural changes in the tissue during ablation. METHODS AND RESULTS: Patients with paroxysmal atrial fibrillation underwent pulmonary vein isolation (PVI) using PFA (AblaView®, MedLumics). Using polarization-sensitive optical coherence reflectometry (PS-OCR), reflective characteristics of myocardial tissue and visualization of real-time contrast between healthy tissue and ablated tissue using a drop in tissue birefringence (BiR) was assessed. Wide antral PVI was performed using single point irrigated PFA (unipolar, 1800V, 3 trains, 21 s). Remapping was performed at 3 months. Primary efficacy outcome was the ability of PS-OCR to predict lesion durability at 3-month remapping. Serious adverse events were recorded. Ten patients were included. In total, 38/40 PVs could be isolated with the system. The mean drop of BiR was 17.3 ± 11.5%. Dragging across the ablation lines showed a persistent drop in BiR. During the remap procedures (8/10 patients ablated only with PFA), 12 PVs (37.5%) were found to be electrically reconnected. The mean loss of BiR during all PFA for durable lesions was 20.9%, while only 10.1% BiR loss was observed during the index ablation for reconnected areas (P < 0.001). None of the points with ≥17% loss of birefringence was found to be reconnected. CONCLUSION: This first-in-human study supports the use of real-time drop in tissue BiR for lesion assessment and durability during PFA delivery, and its procedural safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".