The Cost-Effectiveness of First-Line Cryoablation vs First-Line Antiarrhythmic Drugs in Canadian Patients With Paroxysmal Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The EARLY-AF (NCT02825979), STOP AF First (NCT03118518), and Cryo-FIRST (NCT01803438) randomised controlled trials (RCTs) demonstrated that cryoballoon pulmonary vein isolation reduces atrial fibrillation (AF) recurrence compared with antiarrhythmic drugs (AADs) in patients with symptomatic paroxysmal atrial fibrillation (PAF). The present study developed a cost-effectiveness model (CEM) of first-line cryoablation compared with first-line AADs for PAF, from the Canadian health care payer's perspective. METHODS: Data from the 3 RCTs were analysed to estimate key CEM parameters. The model structure used a decision tree for the first 12 months and a Markov model with a 3-month cycle length for the remaining lifetime time horizon. Costs were set at 2023 Canadian dollars, health benefits were expressed as quality-adjusted life years (QALYs), and both were discounted 3% annually. Probabilistic sensitivity analysis (PSA) considered parameter uncertainty. RESULTS: The statistical analysis estimated that first-line cryoablation generates a 47% reduction (P < 0.001) in the rate of AF recurrence, a 73% reduction in the rate of subsequent ablation (P < 0.001), and a 4.3% (P = 0.025) increase in health-related quality of life, compared with first-line AADs. The PSA indicates that an individual treated with first-line cryoablation accrues less costs (-$3,862) and more QALYs (0.19) compared with first-line AADs. Cryoablation is cost-saving in 98.4% of PSA iterations and has a 99.9% probability of being cost-effective at a cost-effectiveness threshold of $50,000 per QALY gained. Cost-effectiveness results were robust to changes in key model parameters. CONCLUSIONS: First-line cryoballoon ablation is cost-effective when compared with AADs for patients with symptomatic PAF.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".