Clinical Research Atrial Fibrillation and Congestive Heart Failure: A Cost Analysis of Rhythm-Control vs Rate-Control Strategies
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is common in patients with heart failure. Rhythm- and rate-control strategies are associated with similar efficacy outcomes. We compared the economic impact of the 2 treatment strategies in patients with AF and heart failure from the province of Quebec, Canada. Methods: In a substudy of the Atrial Fibrillation and Congestive Heart Failure trial, health care expenditures of patients from Quebec randomized to rhythm and rate-control treatment strategies were compared from a single-payer perspective using a cost-minimization approach. In-trial resource utilization and unit costs were estimated from Quebec Health Insurance Board databases supplemented by disease-specific costs from the Ontario Case Costing Initiative. Results: In all, 304 patients were included, aged 68 � 9 years; 86% male; ejection fraction, 26% � 6%. Baseline characteristics were similar in rhythm-control (n ¼ 149) and rate-control (n ¼ 155) groups. Arrhythmia-related costs accounted for 45% of total expenditures. Rate-control patients had fewer cardiac procedures (146 vs 238, P < 0.001), driven by fewer cardioversions, and lower costs related to antiarrhythmic drugs (CAD$48 per patient [95% confidence interval {CI}, $21-$96] vs $1319 per patient [95% CI, $1124-$1522]). However, these differences were offset by higher expenditures due to hospitalizations for noncardiovascular diagnoses, implantable cardiac arrhythmia devices, and noncardiovascular drugs in the rate-control group. The total cost per patient was not significantly different RESUME
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".