Impact of amiodarone use on metoprolol concentrations, <scp>α‐OH</scp>‐metoprolol concentrations, metoprolol dosing and heart rate: A cross‐sectional study
Bibliographic record
Abstract
Small studies suggest that amiodarone is a weak inhibitor of cytochrome P450 (CYP) 2D6. Inhibition of CYP2D6 leads to increases in concentrations of drugs metabolized by the enzyme, such as metoprolol. Considering that both metoprolol and amiodarone have β-adrenergic blocking properties and that the modest interaction between the two drugs would result in increased metoprolol concentrations, this could lead to a higher risk of bradycardia and atrioventricular block. The primary objective of this study was to evaluate whether metoprolol plasma concentrations collected at random timepoints from patients enrolled in the Montreal Heart Institute Hospital Cohort could be useful in identifying the modest pharmacokinetic interaction between amiodarone and metoprolol. We performed an analysis of a cross-sectional study, conducted as part of the Montreal Heart Institute Hospital Cohort. All participants were self-described "White" adults with metoprolol being a part of their daily pharmacotherapy regimen. Of the 999 patients being treated with metoprolol, 36 were also taking amiodarone. Amiodarone use was associated with higher metoprolol concentrations following adjustment for different covariates (p = .0132). Consistently, the association between amiodarone use and lower heart rate was apparent and significant after adjustment for all covariates under study (p = .0001). Our results highlight that single randomly collected blood samples can be leveraged to detect modest pharmacokinetic interactions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".