A Cost Analysis of Catheter Ablation for Atrial Fibrillation: A Canadian Pre-Post Study
Bibliographic record
Abstract
Background: There is a paucity of Canadian studies using patient-level data to analyze the costs of catheter ablation (CA) for atrial fibrillation (AF). We sought to identify the health care resource use, costs, and cost predictors of CA. Methods: A cost analysis was performed in a population of AF patients treated with CA in Central Zone Nova Scotia from 2010 to 2018. Costs were compared 2 years before ablation (pre-CA) with costs 2 years after (post-CA); the 3-month period post-CA was defined as the treatment window. Costs were also compared according to CA technology defined as before 2015 for patients treated with non-contact force sensing CA and after 2015 for patients treated with contact force sensing CA. Results: Heart failure hospitalizations, AF-related emergency department visits, acute inpatient admissions, and cardioversions all decreased after ablation. The cost difference post-CA vs pre-CA was CAD$18,869 (95% confidence interval [CI], $15,570-$22,168). This increase in costs was driven by costs incurred during the treatment window, which was $21,439 (95% CI, $20,468-$22,409). After excluding treatment window costs, the mean year 1 post-CA cost was $11,223 (95% CI, $9113-$13,334) and year 2 post-CA cost was $4555 (95% CI, $3145-$5965); both were lower than the pre-CA costs. Costs remained stable over the time frame of the study period, with no influence from new technologies on cost. The post-CA cost difference between the post-2015 and pre-2015 groups was $2573 (95% CI, -$2336 to $7481). Conclusions: We showed that although CA is expensive, it might be a cost-effective treatment modality for AF because of the associated reduction in costs and health care resource use.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".