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Record W2887430283 · doi:10.1016/j.hrthm.2018.08.015

Continuous monitoring after second-generation cryoballoon ablation for paroxysmal atrial fibrillation in patients with cardiac implantable electronic devices

2018· article· en· W2887430283 on OpenAlexaff
Rajin Choudhury, Hugo‐Enrique Coutiño, Radu Darciuc, Erwin Ströker, Valentina De Regibus, Giacomo Mugnai, Gaetano Paparella, Muryio Terasawa, Varnavas Varnavas, Francesca Salghetti, Saverio Iacopino, Ken Takarada, Juan‐Pablo Abugattas, Juan Sieira, Pedro Brugada, Carlo de Asmundis, Gian‐Battista Chierchia

Bibliographic record

VenueHeart Rhythm · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsQueen Elizabeth II Health Sciences Centre
FundersBiotronikBoston Scientific CorporationMedtronicAbbott Laboratories
KeywordsMedicineParoxysmal atrial fibrillationCardiologyAtrial fibrillationInternal medicineAblation

Abstract

fetched live from OpenAlex

Background The second-generation cryoballoon (CB) is effective in achieving pulmonary vein isolation. Continuous monitoring would eliminate any over- or underestimated freedom from atrial fibrillation (AF) postablation. Objective The purpose of this study was to differentiate between arrhythmias occurring after cryoballoon ablation (CBA), detecting true AF in symptomatic patients and detecting silent subclinical AF. Methods Between June 2012 and January 2015, 54 patients with a preexisting cardiac implantable electronic device (CIED) who had undergone CBA for paroxysmal atrial fibrillation (PAF) were included in our retrospective study. Regular CIED controls, physical examination, and ECG recordings were performed by an experienced cardiologist blinded to the ablation procedure. Data on any hospitalization during follow-up were gathered. Patients were encouraged to note all clinical symptoms during follow-up. Results Continuous monitoring showed a success rate of 83.3% after 1 year and 75.93% after 3 years of follow-up. During the first year, 68% of episodes of palpitations after ablation were due to sinus tachycardia, nonsustained ventricular tachycardia, or supraventricular tachycardia. AF recurrence was detected in 15.6% of asymptomatic patients during follow-up. Total AF burden post-CBA had decreased to 0.64% ± 4.34% (P <.001) during long-term follow-up of 3.3 years. Conclusion Although this is a selected group of patients with a preexisting CIED, continuous monitoring showed freedom from AF in 83.3% of patients post-CBA after 1 year and 75.93% after 3 years of follow-up. The second-generation cryoballoon (CB) is effective in achieving pulmonary vein isolation. Continuous monitoring would eliminate any over- or underestimated freedom from atrial fibrillation (AF) postablation. The purpose of this study was to differentiate between arrhythmias occurring after cryoballoon ablation (CBA), detecting true AF in symptomatic patients and detecting silent subclinical AF. Between June 2012 and January 2015, 54 patients with a preexisting cardiac implantable electronic device (CIED) who had undergone CBA for paroxysmal atrial fibrillation (PAF) were included in our retrospective study. Regular CIED controls, physical examination, and ECG recordings were performed by an experienced cardiologist blinded to the ablation procedure. Data on any hospitalization during follow-up were gathered. Patients were encouraged to note all clinical symptoms during follow-up. Continuous monitoring showed a success rate of 83.3% after 1 year and 75.93% after 3 years of follow-up. During the first year, 68% of episodes of palpitations after ablation were due to sinus tachycardia, nonsustained ventricular tachycardia, or supraventricular tachycardia. AF recurrence was detected in 15.6% of asymptomatic patients during follow-up. Total AF burden post-CBA had decreased to 0.64% ± 4.34% (P <.001) during long-term follow-up of 3.3 years. Although this is a selected group of patients with a preexisting CIED, continuous monitoring showed freedom from AF in 83.3% of patients post-CBA after 1 year and 75.93% after 3 years of follow-up.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.518

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 head0.283
Teacher spread0.263 · 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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Citations9
Published2018
Admission routes1
Has abstractyes

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