Chronic Amiodarone Use and the Risk of Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Observational studies have identified inconsistent associations between chronic use of amiodarone and cancer-related outcomes. We performed a systematic review and meta-analysis to evaluate cancer risk among patients receiving amiodarone. Methods We searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) to May 1, 2020. We included randomized controlled trials (RCTs) with follow-up ≥2 years that compared amiodarone (any dose) to any comparator (placebo, active pharmacologic or interventional comparator, or usual care), and reported ≥1 outcome of interest. We contacted authors of published chronic amiodarone trials for potentially unreported cancer outcomes. The primary outcome was cancer incidence. Secondary outcomes were cancer-related death and site-specific cancers. We determined risk ratios and 95% confidence intervals using a fixed-effect model, and statistical heterogeneity using I 2 . We conducted prespecified subgroup and sensitivity analyses for amiodarone indication, amiodarone dose, duration of therapy, and trial-level risk of bias. Results From 1439 articles, we included 5 RCTs (n = 4357). Mean follow-up duration ranged from 21 to 37 months. We included previously unpublished cancer outcome data from 1 RCT. Our primary outcome was not reported in any RCT. There was no significant difference in cancer-related death between amiodarone (1.69%) and the comparator (1.75%) (risk ratio 0.96, 95% confidence interval 0.57-1.63; I 2 = 0%). There were no significant interactions from our subgroup or sensitivity analyses. Conclusions Chronic amiodarone use did not increase cancer-related deaths. Data from RCTs do not support an increased risk of cancer-related harms with amiodarone use, and these concerns should not deter use of amiodarone when indicated.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".