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Record W4377984171 · doi:10.1093/europace/euad122.301

Flecainide is associated with a reduction in arrhythmic events in a large cohort of patients with catecholaminergic polymorphic ventricular tachycardia

2023· article· en· W4377984171 on OpenAlexaff
Auke T. Bergeman, Michael W.T. Tanck, Dania Kallas, Shubhayan Sanatani, Antoine Leenhardt, Michael J. Ackerman, Jan Till, Esther Zorio, Prince J. Kannankeril, Janneke A.E. Kammeraad, Takeshi Aiba, Christopher Semsarian, Tomas Robyns, Arthur A.M. Wilde, Christian van der Werf

Bibliographic record

VenueEP Europace · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsBC Children's Hospital
FundersNederlandse Federatie van Universitair Medische CentraZonMw
KeywordsFlecainideCatecholaminergic polymorphic ventricular tachycardiaMedicineVentricular tachycardiaInternal medicineCardiologyImplantable cardioverter-defibrillatorCohortRetrospective cohort studySudden cardiac deathRyanodine receptor 2Atrial fibrillation

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was supported by the ZonMW Priority Medicines for Rare Diseases and Orphan Drugs (grant 113304045 to Dr Van der Werf) and the Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development and the Royal Netherlands Academy of Sciences (PREDICT2 to Dr. Wilde). Background In patients with catecholaminergic polymorphic ventricular tachycardia (CPVT), β-blocker may be insufficiently protective. A randomized clinical trial showed that flecainide reduces ventricular arrhythmias (VAs) in CPVT and small retrospective cohort studies suggest that this translates into a reduction of arrhythmic events. Objective To evaluate the efficacy of flecainide when added to β-blockers for the prevention of arrhythmic events (AE) in a large cohort of patients with CPVT using paired analysis. Methods From two international registries, this multicentre retrospective cohort study included patients with a clinical and/or genetic diagnosis of CPVT using flecainide as an adjunct to β-blockers. The study period, which comprised a pre- and on-flecianide period, was defined as the period in which background therapy, i.e. β-blocker type (β1-selective or nonselective), left cardiac sympathetic denervation (LCSD), and implantable cardioverter-defibrillator (ICD) treatment status, remained unchanged within individual patients. The primary endpoint was AEs, defined as SCD, sudden cardiac arrest (SCA), appropriate ICD shock, and arrhythmic syncope. The effect of flecainide on AE rates was assessed using a generalized linear mixed model assuming Poisson distribution and random effects for patients. Results Two-hundred forty-seven patients (median age at start of flecainide 18 years [IQR 14-29], 123 [50%] female, median flecainide dose 2.2 mg/kg/day [IQR 1.7-3.1]) were included. At baseline, 70 (28%) patients had an ICD and 21 (9%) had a LCSD performed. Nonselective β-blockers were used by 133 (54%) patients. During a median pre-flecainide follow-up of 2.1 years (IQR 0.4-7.2), 41 patients experienced 58 AEs. During a median on-flecainide follow-up of 2.9 years (IQR 1.0-6.0), 23 patients experienced 38 AEs. There was a significant reduction in AEs after initiation of flecainide (OR 0.55 [0.38-0.83]; p=0.007). Among patients who were symptomatic before diagnosis or before the initiation of flecainide (n=167), flecainide was associated with a significant reduction in AEs (OR 0.49 [0.31-0.77]; p=0.002). Among patients with ≥1 AE on β-blocker therapy (n=41), adding flecainide was associated with a significant reduction in AEs (OR 0.25 [0.14-0.45]; p<0.001). Conclusion Flecainide was associated with a reduction in AEs when added to β-blockers in patients with CPVT in the overall cohort, symptomatic patients, and particularly in patients with breakthrough AEs on β-blocker therapy.

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 score0.004
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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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Citations2
Published2023
Admission routes1
Has abstractyes

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