Cost-effectiveness of Catheter Ablation Versus Antiarrhythmic Drug Therapy for the Treatment of Atrial Fibrillation: A Canadian Perspective
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) affects approximately 350,000 Canadians and has an estimated annual economic burden exceeding $800 million dollars. Anti-arrhythmic drug (AAD) therapy and catheter ablation (CA) are the two common treatments for paroxysmal AF. However, the upfront costs of CA are quite substantial. Objective: The objective of this study was to assess the cost-effectiveness of CA compared to AAD for AF based on community practice. Methods: A Markov simulation model was developed for a hypothetical cohort of 55-year-old patients with paroxysmal AF and a low stroke risk. Patients received either CA or AAD. Costs and quality-adjusted life years (QALYs) were computed over lifetime, 10-year, and 5-year time horizons. Model inputs were obtained from a large, prospectively collected, single-center Canadian registry and augmented with the published literature, using Canadian cost estimates for disease states. Threshold values of $25,000, $50,000, and $100,000 per QALY, respectively, were used to determine cost-effectiveness. All costs were expressed in 2012 Canadian dollars. Results: The incremental cost-effectiveness ratio for CA versus AAD therapy was $1,228, $22,879, and $63,647 for the lifetime, 10-year, and 5-year time horizons, respectively. Over a lifetime horizon, the probability of achieving cost-effectiveness was 100% for all 3 cost per QALY thresholds. The 10-year probability of achieving cost-effectiveness was 74%, 100%, and 100% at the $25,000, $50,000, and $100,000 thresholds, respectively. The 5-year probability of achieving cost-effectiveness was 0%, 0.9%, and 100% at the 3 cost per QALY thresholds. Results were most sensitive to time horizon, probability of repeat AF ablation, and stroke rate. Conclusions: From the perspective of the Canadian Healthcare system, CA is a potentially cost-effective treatment compared to AAD therapy in a low stroke risk population using real-world data when examining a time horizon of greater than 5 years.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".