Ultra-Low–Temperature Cryoablation for Ventricular Tachycardia: An Early Single-Centre Report of Acute Results
Bibliographic record
Abstract
Background: Endocardial catheter ablation for ventricular tachycardia (VT) may fail because of the inability to deliver transmural lesions. Ultra-low-temperature cryoablation (ULTC) uses near-critical nitrogen and can generate temperatures as low as -196 °C. We report a series of 18 patients who underwent ULTC at the McGill University Health Centre (MUHC), representing the largest single-centre experience to date. Methods: Eighteen patients with monomorphic drug-refractory VT underwent VT ablation with ULTC at our institution as part of the first-in-human CryoCure-VT trial (NCT04893317). After voltage map, the mapping catheter was replaced with the ULTC catheter, and lesions were applied over a fixed duration of time (60-180 seconds), followed by a 60-second thaw and another application at the original duration (freeze-thaw-freeze). Duration of ablation time was selected depending on the wall thickness of the left ventricle monitored with intracardiac echo to achieve tissue depths of 4.5 to 7.5 mm. Results: Baseline left ventricular ejection fraction was 32%, mean age 71 years, 94% were male. A total of 32 sustained VTs were induced in 16 of 18 patients. A total of 177 cryoablation lesions were delivered (9.8 lesions per patient). Of the 16 patients with inducible VT, 15 (94%) were rendered noninducible postablation, and 1 was inducible only for a nonclinical VT. Complications included 1 pericardial effusion that required drainage. From 18 patients, 16 (89%) were discharged within the first 24 hours postablation. Conclusions: ULTC is feasible and permits acute control of monomorphic VT during VT ablation procedures in drug-refractory patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".