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Record W3217650148 · doi:10.1111/jce.15308

Bayesian network meta‐analysis comparing cryoablation, radiofrequency ablation, and antiarrhythmic drugs as initial therapies for atrial fibrillation

2021· article· en· W3217650148 on OpenAlexaff
Mahmoud Elsayed, Omar Abdelfattah, Ahmed Sayed, Rohan Prasad, Amr F. Barakat, Islam Y. Elgendy, Jason G. Andrade, T. Jared Bunch, Amit J. Thosani, Walid I. Saliba, Oussama M. Wazni, Ayman A. Hussein

Bibliographic record

VenueJournal of Cardiovascular Electrophysiology · 2021
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité de MontréalMontreal Heart InstituteUniversity of British Columbia
Fundersnot available
KeywordsMedicineCryoablationAtrial fibrillationCatheter ablationRadiofrequency ablationInternal medicineAblationCardiologyRandomized controlled trialOdds ratioAtrial flutterMeta-analysisAdverse effect

Abstract

fetched live from OpenAlex

BACKGROUND: Antiarrhythmic drugs (AADs) and catheter ablation are first line treatments of paroxysmal atrial fibrillation (PAF), however, there exists a paucity of data regarding the potential benefit of different catheter ablation technologies versus AADs as an early rhythm strategy. OBJECTIVE: To assess the safety and efficacy of cryoablation versus radiofrequency ablation (RFA) versus AADs as a first line therapy of PAF. METHODS: MEDLINE, Embase, Scopus and CENTRAL were searched to retrieve randomized clinical trials (RCTs) comparing cryoablation, RFA or AADs to one another as first line therapies for atrial fibrillation (AF). The primary outcome was overall freedom from arrhythmia recurrence (AF, atrial flutter [AFL], atrial tachycardia). Secondary outcomes included freedom from symptomatic arrhythmia recurrence, hospitalization, and serious adverse events. A random-effects Bayesian network meta-analysis was used to calculate odds ratios (OR) and 95% credible intervals (CrI). RESULTS: Six RCTs (N = 1212) met the inclusion criteria (605 AADs, 365 Cryoablation, and 245 RFA). Compared with AADs, overall recurrence was reduced with RFA (OR: 0.31; 95% CrI: 0.10-0.71) and cryoablation (OR: 0.39; 95% CrI: 0.16-1.00). Comparing ablation (cryoablation and RFA) with AADs in respect to freedom from symptomatic AF recurrence, neither cryoablation (OR: 0.35; 95% CrI: 0.06-1.96) nor RFA (OR: 0.34; 95% CrI: 0.07-1.27) resulted in statistically significant reductions individually compared to AADs, though pooled ablation with both technologies showed lower odds of arrhythmia recurrence (OR: 0.35; 95% CrI: 0.13-0.79). In terms of serious adverse events rates, neither cryoablation (OR: 0.77; 95% CrI: 0.44-1.39) nor RFA (OR: 1.45; 95% CrI: 0.67-3.23) were significantly different to AADs. RFA resulted in a statistically significant reduction in hospitalizations compared to AAD (OR: 0.08; 95% CrI: 0.01-0.99), whereas cryoablation did not (OR: 0.77; 95% CrI: 0.44-1.39). The surface under the cumulative ranking curve showed RFA to be the most effective treatment at reducing overall rates of recurrence, symptomatic recurrence and hospitalizations; whereas cryoablation was most likely to reduce serious adverse events. CONCLUSION: Cryoablation and RFA are both effective and safe first line therapies for AF compared to AADs, with RFA being the most effective at reducing recurrences.

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)0.001
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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 designMeta-analysis
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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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