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Record W4388595533 · doi:10.1093/eurheartj/ehad655.371

Cryoballoon versus radiofrequency catheter ablation for atrial fibrillation: insights from the Netherlands Heart Registration (2013-2021)

2023· article· en· W4388595533 on OpenAlexaff
Michelle Samuel, M Van Der Stoel, Yuri Blaauw

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineAblationAtrial fibrillationCatheter ablationPopulationCardiologyRadiofrequency ablationIncidence (geometry)Internal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract Background Cryoballoon ablation has emerged as a viable alternative to radiofrequency (RF) ablation in the treatment of atrial fibrillation (AF) with catheter ablation. Randomized trials and observational studies have demonstrated no statistically significant difference in the overall safety, efficacy, and effectiveness of RF and cryoballoon technologies. Despite over a decade of cryoballoon ablation use, there is limited population-level data comparing uptake in the utilization, populations referred, complications, and rates of repeat ablation of cryoballoon versus RF ablation. Purpose Our objective was to compare the real-world temporal trends in the utilization and complication rates of cryoballoon versus RF ablation for AF in the Netherlands between 2013 and 2021. Methods The Netherlands Heart Registration was used for the present study and included all consecutive patients who underwent AF ablation at 16 hospitals in the Netherlands (2013-2021). Only patients who underwent RF or cryoballoon ablation were included. Crude, age-, and sex-standardized annual incidence rates for AF ablation were calculated. Incidence rate ratios (IRR) and temporal trends were assessed with Poisson regression models with robust variances. Multivariable logistic regressions were used to compare the incidence complications between ablation methods. Results Of 37,538 AF ablations performed in the Netherlands, 20,799 (55.4%) and 12,549 (33.4%) underwent RF and cryoballoon ablations, respectively. At index AF ablation, patients referred to cryoballoon ablation were more likely women (34.3% vs 31.9%) and more frequently treated for paroxysmal AF (76.8% vs 68.0%) compared to RF patients [cryoballoon vs RF, respectively; p<0.05 for both]. Age of patients did not differ between ablation methods [median age for both: 63 (IQR 56-69) years; p=0.9]. From 2013 to 2021, the utilization of cryoballoon ablation increased by 16% for index ablations (p<0.05; Figure 1). Further, the population-level incidence of cryoballoon ablation was similar to RF ablation for index ablations from 2016 onwards (Figure 1). Repeat ablations (n=10,511) were primarily performed with RF (91.5%). Patients with cryoballoon ablation had a reduced risk of referral for repeat ablation [aHR 0.7 (95% CI 0.6-0.7)]. After multivariable adjustment, cryoballoon ablation was a risk factor for phrenic nerve paralysis [aOR 18.6 (95% CI 9.5- 36.4)] and RF was a risk factor for cardiac tamponade [aOR 2.7 (95% CI 1.7-4.3)] and minor vascular complications [aOR 1.4 (95% CI 1.1-1.8)] for index ablation. No difference was detected for the incidence of other complications between ablation methods (Figure 2). Incidence of all complications remained stable over time (p>0.05 for both). Conclusion The utilization of cryoballoon and RF ablation has rapidly increased from 2013 to 2021 in the Netherlands. Further, cryoballoon was used as frequently as RF ablations for index AF ablation procedures from 2016 onwards.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.119
GPT teacher head0.346
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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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Citations0
Published2023
Admission routes1
Has abstractyes

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