Comparative Effectiveness of Rhythm Control vs Rate Control Drug Treatment Effect on Mortality in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Controversy continues concerning the choice of rhythm control vs rate control treatment strategies for atrial fibrillation (AF). A recent clinical trial showed no difference in 5-year mortality between the 2 treatments. We aimed to determine whether the 2 strategies have similar effectiveness when applied to a general population of patients with AF with longer follow-up. METHODS: We used population-based administrative databases from Quebec, Canada, from 1999 to 2007 to select patients 66 years or older hospitalized with an AF diagnosis who did not have AF-related drug prescriptions in the year before the admission but received a prescription within 7 days of discharge. Patients were followed until death or administrative censoring. Mortality was analyzed by multivariable Cox regression. RESULTS: Among 26,130 patients followed for a mean (SD) period of 3.1 years (2.3 years), there were 13,237 deaths (49.5%). After adjusting for covariates, we found that the effect of rhythm vs rate control drugs changed over time: after a small increase in mortality for patients treated with rhythm control in the 6 months following treatment initiation (hazard ratio [HR], 1.07; 95% CI, 1.01-1.14), the mortality was similar between the 2 groups until year 4 but decreased steadily in the rhythm control group after year 5 (HR, 0.89; 95% CI, 0.81-0.96; and HR, 0.77; 95% CI, 0.62-0.95, after 5 and 8 years, respectively). CONCLUSIONS: In this population-based sample of patients with AF, we found little difference in mortality within 4 years of treatment initiation between patients with AF initiating rhythm control therapy vs those initiating rate control therapy. However, rhythm control therapy seems to be superior in the long-term.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".