Adverse events with co‐prescription of angiotensin receptor blockers and clarithromycin compared to azithromycin: A population‐based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Clinically relevant drug-drug interactions (DDIs) are a common cause of adverse drug reactions (ADRs). Hepatic organic anion transporting polypeptides (OATPs) have recently been studied for their role in DDIs. The commonly prescribed antihypertensive angiotensin receptor blockers (ARBs) are known to be eliminated by hepatic OATPs. ARBs are commonly prescribed to patients with reduced kidney function, and kidney disease can result in profound changes to nonrenal drug elimination through reduced hepatic drug transport-mediated excretion. The antibiotic clarithromycin inhibits OATP activity whereas azithromycin does not, making them useful comparators to study DDIs with OATP substrate drugs. OBJECTIVE: To investigate whether co-prescription of ARBs and clarithromycin results in increased adverse events compared to azithromycin and whether kidney function modifies this risk. METHODS: We conducted a retrospective population-based cohort study in Ontario, Canada (2010-2021) using linked health care data for 106,322 older individuals (≥66 years) receiving an OATP substrate ARB (candesartan, olmesartan, telmisartan, valsartan) and newly co-prescribed clarithromycin (n = 32,693) or azithromycin (n = 73,629). Primary outcomes were hospital admissions or emergency department visits for hyperkalemia or acute kidney injury (AKI) within 14 days of antibiotic prescription. Adjusted risk ratios (aRR) were obtained using modified Poisson regression after controlling for eight potential confounders. Pre-specified effect measure modification analysis evaluated whether kidney function influenced these outcomes. RESULTS: Compared to those co-prescribed azithromycin, patients receiving clarithromycin had a significantly higher risk of hyperkalemia (aRR 2.05, 95% confidence interval (CI) 1.32-3.18) and AKI (aRR 1.75, 95% CI 1.41-2.17). The risk of hyperkalemia increased as kidney function declined (multiplicative interaction; p = 0.01). CONCLUSIONS: This population-based retrospective cohort study provides evidence of OATP-mediated drug interactions between ARBs and clarithromycin that warrants further investigation to guide clinical practice, especially for patients with reduced kidney function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".