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Record W4398767902 · doi:10.1093/europace/euae102.111

An economic evaluation of first-line cryoballoon ablation versus antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation from a Danish healthcare perspective

2024· article· en· W4398767902 on OpenAlexaff
M Lock Hansen, J. Moss, Jacob Tønnesen, M Lundsby Johansen, Malte Kuniss, Eleni Ismyrloglou, Jason G. Andrade, Oussama M. Wazni, Stuart Mealing, Alicia Sale, Daniela Afonso, Tom Bromilow, Gian‐Battista Chierchia

Bibliographic record

VenueEP Europace · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineParoxysmal atrial fibrillationAtrial fibrillationCardiologyQuinidineInternal medicineDanishPerspective (graphical)DrugAblationCatheter ablationIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

Abstract Background As demonstrated in three randomized clinical trials (RCTs), initial rhythm control with first-line pulmonary vein isolation (PVI) using cryoballoon ablation decreases atrial arrhythmia recurrence compared to antiarrhythmic drug (AAD) therapy in patients with symptomatic paroxysmal atrial fibrillation (PAF). Purpose To assess the cost-effectiveness of first-line cryoablation versus first-line AADs in a Danish healthcare setting. Methods Data from 703 participants with symptomatic PAF enrolled into Cryo-FIRST, STOP AF First and EARLY-AF RCTs were used to estimate risk equations (rate of ablation, AF recurrence and resolution, AF-related hospital attendance in addition to health-related quality-of-life (HRQoL) utilities). These were then incorporated into a cost-effectiveness model (CEM) . The Danish cost data was derived from published Danish literature, DRG tariffs and pharmaceutical list prices in Denmark. Where parameters could not be derived, inputs were sourced from published literature or expert opinion. The CEM consisted of a decision tree (one-year time horizon) and a Markov model (three-month cycle length) hybrid, with a lifetime (40 years) time horizon. The CEM is from the perspective of the Danish healthcare system. Health benefits were expressed in quality-adjusted life-years (QALYs), and all costs and benefits were discounted at 3%. As there is no official cost-effectiveness threshold in Denmark, the UK threshold of £20,000 (~€23,200) was assumed. Input uncertainty within the CEM was explored using probabilistic sensitivity analysis. The results presented include data from the initial 12-week blanking period for all studies. Results The three-monthly rate of AF symptom recurrence had a reduction, on average, of 46.7% (p<0.001) in those treated with cryoablation. In addition, the cryoablation arm had a 72.8% (p<0.001) reduction in the monthly rate of receiving an ablation following initial treatment. The average cryoablation patient was also associated with a 4.3% (p=0.025) increase in their HRQoL. Finally, there was no difference in the rate of AF symptom resolution in those who failed initial treatment. Base case results per patient are presented in Table 1. Cryoablation was shown to be dominant when compared to AADs (0.159 more QALYs and €2,519 less costly), with a 99.96% probability of being cost-effective at a willingness-to-pay threshold of ~€23,200 per QALY gained. Individuals in both arms of the model were also expected to receive ~1.2 ablations over a lifetime horizon. Nonetheless, there was a 45% relative reduction in time spent in symptomatic AF states for individuals who were initially treated with cryoablation. Scenario analysis results are presented in Table 2. Cryoablation remained cost-effective over AADs in all scenarios conducted. Conclusions Initial rhythm control with cryoballoon ablation is cost-effective (dominant) when compared to AADs in the Danish healthcare system.Table 1:Key results (per patient)Table 2.Deterministic scenario analysis

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.410

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 head0.106
GPT teacher head0.402
Teacher spread0.296 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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