An economic evaluation of first-line cryoballoon ablation vs antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation from a U.S. Medicare perspective
Bibliographic record
Abstract
Background: Three recent randomized controlled trials have demonstrated that, as an initial rhythm control strategy, first-line cryoballoon ablation (cryoablation) reduces atrial arrhythmia recurrence compared with antiarrhythmic drugs (AADs) in patients with symptomatic paroxysmal atrial fibrillation (PAF). Objective: The study sought to evaluate the cost-effectiveness of first-line cryoablation compared with first-line AADs for treating symptomatic PAF from a U.S. Medicare payer perspective. Methods: Individual patient-level data from 703 participants with PAF enrolled into the Cryo-FIRST (NCT01803438), STOP AF First (NCT03118518), and EARLY-AF (NCT02825979) trials were used to derive parameters for the cost-effectiveness model. The cost-effectiveness model used a hybrid decision tree and Markov structure. The decision tree had a 1-year time horizon and was used to inform the initial health state allocation in the first cycle of the Markov model. The Markov model used a 40-year time horizon (3-month cycle length). Health benefits were expressed in quality-adjusted life years (QALYs). Costs and benefits were discounted at 3% per year. Results: Cryoablation was estimated to yield higher QALYs (+0.17) and higher costs (+$4274) per patient over a 40-year time horizon than AADs. Ultimately, this produced an average incremental cost-effectiveness ratio of $24,637 per QALY gained. Independent of initial treatment, individuals were expected to receive ∼1.2 ablations over a lifetime. There was a 45% relative reduction in time spent in atrial fibrillation health states for those initially treated with cryoablation compared with AADs. Conclusion: Initial rhythm control with first-line cryoballoon ablation is highly cost-effective compared with first-line AADs from a U.S. Medicare payer perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".