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Record W4388784573 · doi:10.1016/j.cjco.2023.11.009

Ultra-Low–Temperature Cryoablation for Ventricular Tachycardia: An Early Single-Centre Report of Acute Results

2023· article· en· W4388784573 on OpenAlexafffundabout
Paula Sánchez-Somonte, Atul Verma, L. Bravosi Da rosa, Valeria Anglesio, Vladimir Poletaev, Ahmed AlTurki, Martin Bernier, Jacqueline Joza, Vidal Essebag

Bibliographic record

VenueCJC Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsMcGill University Health Centre
FundersBiosense WebsterMcGill University Health CentreFonds de Recherche du Québec - SantéBoston Scientific Corporation
KeywordsCryoablationMedicineVentricular tachycardiaAblationVentricleCardiologyInternal medicineCatheter ablationEjection fractionTachycardiaCatheterCryosurgeryAnesthesiaSurgeryHeart failure

Abstract

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BackgroundEndocardial catheter ablation for ventricular tachycardia (VT) may fail owing to 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 that underwent ULTC at the McGill University Health Centre (MUHC), representing the largest single center experience to date.MethodsEighteen 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 s), 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 4.5–7.5 mm.ResultsBaseline 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 non-inducible post ablation and 1 was inducible only for a non-clinical VT. Complications included 1 pericardial effusion that required drainage. From 18 patients, 16 (89%) were discharged within the first 24 hours post ablation.ConclusionsULTC is feasible and permits acute control of monomorphic VT during VT ablation procedures in drug-refractory patients. Endocardial catheter ablation for ventricular tachycardia (VT) may fail owing to 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 that underwent ULTC at the McGill University Health Centre (MUHC), representing the largest single center experience to date. 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 s), 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 4.5–7.5 mm. 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 non-inducible post ablation and 1 was inducible only for a non-clinical VT. Complications included 1 pericardial effusion that required drainage. From 18 patients, 16 (89%) were discharged within the first 24 hours post ablation. ULTC is feasible and permits acute control of monomorphic VT during VT ablation procedures in drug-refractory patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.307
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations3
Published2023
Admission routes3
Has abstractyes

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