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

2023· article· en· W4388600423 on OpenAlexaff
Andreas Goette, J. Moss, Florian Straube, Jason G. Andrade, Oussama M. Wazni, Gian‐Battista Chierchia, Lukas Schwegmann, Eleni Ismyrloglou, Alicia Sale, Lucy Hillcoat, Stuart Mealing, Tom Bromilow, David A. Lewis, Malte Kuniss

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAtrial fibrillationCryoablationPulmonary veinRandomized controlled trialParoxysmal atrial fibrillationEmergency medicineDigoxinInternal medicineIntensive care medicineCardiologyAblationHeart failure

Abstract

fetched live from OpenAlex

Abstract Background Three recent randomized controlled trials demonstrated that, in patients with symptomatic paroxysmal atrial fibrillation (PAF), first-line pulmonary vein isolation (PVI) with cryoballoon ablation reduces atrial arrhythmia recurrence when compared to initial antiarrhythmic drug (AAD) therapy. Purpose To evaluate the cost-effectiveness of initial rhythm control therapy from a German healthcare service payer perspective. Methods The cost-effectiveness model (CEM) structure consisted of a hybrid decision tree and Markov model, where the decision tree informed the initial health state allocation in the first cycle of the Markov model and had a one-year time horizon. The Markov model had a 40-year time horizon using a three-month cycle length. Health benefits were expressed in quality-adjusted life years (QALYs). Costs and benefits were discounted at 3.5% p.a. Individual patient-level data from 703 participants with untreated PAF enrolled into Cryo-FIRST (NCT01803438), STOP AF First (NCT03118518) and EARLY-AF (NCT02825979) were used to estimate efficacy, resource use and health-related quality of life parameters. Cost inputs were sourced from diagnosis-related groups and the Institute for the Hospital Remuneration System (InEK). Where parameters could not be derived, inputs were sourced from available published literature or determined through clinical expert opinion. Probabilistic sensitivity analyses were conducted to explore the impact of any assumptions on model outputs. Results In those treated with cryoablation, the three-month rate of AF recurrence was reduced by 46.7% (p<0.001) on average. Similarly, the average monthly rate of receiving an ablation following initial treatment was reduced by 72.8% (p<0.001). Cryoablation was also associated with a 4.3% (p=0.025) increase in health-related quality of life at 12 months, assessed through the standard EQ-5D-3L instrument. There was no difference in the rate of AF resolution in those who failed initial treatment. CEM results are shown in Table 1. Analysis shows that cryoablation is cost-effective, incurring a cost of ∼€1,000 per patient over a lifetime compared to AADs, while offering an increase in QALYs. Cryoablation attains an average ICER of ∼€5,500, with a 94.1% probability of being cost-effective at a willingness-to-pay threshold of €35,000 per QALY gained. Through 5,000 iterations, the probabilistic sensitivity analysis indicates that cryoablation has ∼20% probability of being cost-saving. Individuals are expected to receive a total of ∼1.2 ablations over a lifetime, regardless of initial treatment. Although, those initially treated with cryoablation as opposed to AADs experience a 45% reduction in time spent in AF health states. Conclusion Initial rhythm control with cryoballoon ablation in PAF is a cost-effective treatment option in a German healthcare setting.Table 1:Key results (per patient)

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)0.010
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.434
GPT teacher head0.482
Teacher spread0.047 · 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 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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Citations2
Published2023
Admission routes1
Has abstractyes

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