Risk of adverse events among older adults following co-prescription of clarithromycin and statins not metabolized by cytochrome P450 3A4
Bibliographic record
Abstract
BACKGROUND: The cytochrome P450 3A4 (CYP3A4) inhibitor clarithromycin may also inhibit liver-specific organic anion-transporting polypeptides (OATP1B1 and OATP1B3). We studied whether concurrent use of clarithromycin and a statin not metabolized by CYP3A4 was associated with an increased frequency of serious adverse events. METHODS: Using large health care databases, we studied a population-based cohort of older adults (mean age 74 years) who were taking a statin not metabolized by CYP3A4 (rosuvastatin [76% of prescriptions], pravastatin [21%] or fluvastatin [3%]) between 2002 and 2013 and were newly prescribed clarithromycin (n=51,523) or azithromycin (n=52,518), the latter an antibiotic that inhibits neither CYP3A4 nor OATP1B1 and OATP1B3. Outcomes were hospital admission with a diagnostic code for rhabdomyolysis, acute kidney injury or hyperkalemia, and all-cause mortality. All outcomes were assessed within 30 days after co-prescription. RESULTS: Compared with the control group, patients co-prescribed clarithromycin and a statin not metabolized by CYP3A4 were at increased risk of hospital admission with acute kidney injury (adjusted relative risk [RR] 1.65, 95% confidence interval [CI] 1.31 to 2.09), admission with hyperkalemia (adjusted RR 2.17, 95% CI 1.22 to 3.86) and all-cause mortality (adjusted RR 1.43, 95% CI 1.15 to 1.76). The adjusted RR for admission with rhabdomyolysis was 2.27 (95% CI 0.86 to 5.96). The absolute increase in risk for each outcome was small and likely below 1%, even after we considered the insensitivity of some hospital database codes. INTERPRETATION: Among older adults taking a statin not metabolized by CYP3A4, co-prescription of clarithromycin versus azithromycin was associated with a modest but statistically significant increase in the 30-day absolute risk of adverse outcomes.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".