Amiodarone and the risk of pacemaker insertion in elderly patients with atrial fibrillation : analysis of time-dependent exposure using nested case-control and survival analysis methodologies
Bibliographic record
Abstract
The nested case-control design is an efficient method of sampling from a cohort that is increasingly used in cardiovascular research to study causal relationships. This methodology can be used to analyze data from large administrative databases to evaluate potential cardiovascular adverse effects of medications. Using a nested case-control approach, this study investigated whether amiodarone therapy for atrial fibrillation (AF) was associated with an increased risk of bradyarrhythmia requiring permanent pacemaker insertion. This previously unpublished adverse effect of amiodarone was evaluated in a province-wide database of Quebec residents with AF and a previous myocardial infarction. We found that amiodarone use was associated with a greater than twofold increase in the risk of permanent pacemaker insertion after adjusting for potential confounding factors and exposures to other cardiovascular medications. The effect of amiodarone dose on this risk was then evaluated with survival analysis using Cox regression with time-dependent covariates. The risk of permanent pacemaker insertion was found to be dose-dependent and greatest during the initial months of treatment when higher doses of amiodarone were used. The effect of amiodarone dose was reanalyzed using nested case-control methods in order to compare nested case-control and survival analysis approaches for evaluating time-dependent exposure. Expectedly similar risk estimates were obtained with superior computational efficiency using nested case-control methods. In conclusion, amiodarone is associated with a dose-dependent increased risk of permanent pacemaker insertion that should be taken into consideration when initiating amiodarone therapy for elderly patients with AF. The nested case-control design is a useful alternative for analysis of a cohort with time-dependent exposure, particularly when studying rare outcomes in large databases.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".