An economic evaluation of first-line cryoballoon ablation versus antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation from a German healthcare payer perspective
Bibliographic record
Abstract
BACKGROUND: Three recent randomized controlled trials demonstrated that, in patients with symptomatic paroxysmal atrial fibrillation (PAF), first-line pulmonary vein isolation with cryoballoon catheter ablation reduces atrial arrhythmia recurrence compared to initial antiarrhythmic drug (AAD) therapy. This study aimed to evaluate the cost-effectiveness of first-line cryoablation compared to first-line AADs from a German healthcare payer perspective. METHODS: Individual patient-level data from 703 participants with untreated PAF enrolled into three randomized clinical trials (Cryo-FIRST, STOP AF First and EARLY-AF) were used to derive parameters for the cost-effectiveness model (CEM). The CEM structure consisted of a hybrid decision tree and Markov model. The decision tree (one-year time horizon) informed initial health state allocation in the first cycle of the Markov model (40-year time horizon; three-month cycle length). Health benefits were expressed in quality-adjusted life years (QALYs). Cost inputs were sourced from German diagnosis-related groups and the Institute for the Hospital Remuneration System (InEK). Costs and benefits were discounted at 3% per annum. RESULTS: Cryoablation was cost-effective, incurring ~ €200 per patient while offering an increase in QALYs (~ 0.18) over a lifetime. This produced an average incremental cost-effectiveness ratio of ~ €1,000 per QALY gained. Individuals were expected to receive ~ 1.2 ablations over a lifetime, regardless of initial treatment. However, those initially treated with cryoablation as opposed to AADs experience 0.9 fewer re-ablations and a 45% reduction in time spent in AF health states. CONCLUSION: Initial rhythm control with cryoballoon ablation in symptomatic PAF is a cost-effective treatment option in a German healthcare setting.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".