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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".