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Record W4377983911 · doi:10.1093/europace/euad122.688

An economic evaluation of first-line cryoballoon ablation versus antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation from an English NHS perspective

2023· article· en· W4377983911 on OpenAlexaff
Jason G. Andrade, J. Moss, Derick Todd, Malte Kuniss, Oussama M. Wazni, Gian‐Battista Chierchia, Stuart Mealing, Alicia Sale, Eleni Ismyrloglou, Michael J. Souter, Tom Bromilow, David A. Lewis, John Paisey

Bibliographic record

VenueEP Europace · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAtrial fibrillationPulmonary veinCryoablationRandomized controlled trialCost effectivenessQuality of life (healthcare)Quality-adjusted life yearCost-effectiveness analysisIntensive care medicineAblationInternal medicineEmergency medicineCardiology

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): This cost-effectiveness study was funded by Medtronic Background The UK National Institute of Care Excellence (NICE) recommend the use of pulmonary vein isolation (PVI) to treat paroxysmal atrial fibrillation (PAF) in those who have not responded to drug treatment. Recently, three randomized controlled trials have demonstrated that as an initial rhythm control strategy, PVI with cryoballoon ablation, reduces atrial arrhythmia recurrence compared to antiarrhythmic drug (AAD) therapy in patients with symptomatic PAF. Purpose To evaluate the cost-effectiveness of first-line cryoablation versus first-line optimized AADs in an English National Health Service (NHS) setting. Methods Individual patient-level data from 703 participants with untreated PAF recruited into Cryo-FIRST, STOP AF First and EARLY-AF were used to derive equations to predict the following outcomes: rates of ablation, AF recurrence and resolution, AF-related hospital attendance, and health-related quality of life (HRQoL) utilities. Where parameters could not be sourced from the trial data, inputs were taken from the published literature or derived using clinical expert opinion. The cost-effectiveness model (CEM) was a hybrid of a decision tree with a one-year time horizon and a Markov model (three-month cycle length) with a lifetime time horizon and was developed from the perspective of the English NHS. Health benefits were expressed in quality-adjusted life years (QALYs), and all benefits and costs were discounted at 3.5% per year in line with NICE requirements. Uncertainty in the CEM inputs was explored using probabilistic sensitivity analysis. The results include an initial 12-week blanking period for all studies. Results The three-monthly rate of AF recurrence was reduced on average by 46.7% (p<0.001) in those treated with cryoablation. Similarly, the monthly rate of receiving an ablation following initial treatment was reduced by 72.8% (p<0.001) in the cryoablation arm. Furthermore, the average cryoablation patient was associated with a 4.3% (p=0.025) increase in their HRQoL. While the likelihood of failure was greater in the AAD group, in those who failed initial treatment, there was no difference in the rate of AF symptom resolution. The CEM indicates that cryoablation is more effective (+0.17 QALYs) and more costly (+£1,414) over a lifetime compared to optimized AADs. Cryoablation resulted in an Incremental Cost-Effectiveness Ratio of £8,435 with a 78.5% probability of being cost-effective at a willingness-to-pay threshold of £20,000 per QALY gained. Individuals in both treatment arms were predicted to receive ~1.2 ablations over a lifetime regardless of initial treatment. However, there was a 45% relative reduction in the amount of time spent in symptomatic AF states for those initially treated with cryoablation. Conclusions AF rhythm control in drug naïve patients with cryoballoon ablation is cost-effective compared to optimized AADs in an English NHS setting.

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)0.008
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.294
GPT teacher head0.450
Teacher spread0.155 · 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
Published2023
Admission routes1
Has abstractyes

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