Trimethoprim/sulfamethoxazole‐induced phenytoin toxicity in the elderly: a population‐based study
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Drug interactions are an important and avoidable, yet underappreciated cause of phenytoin toxicity. • Trimethoprim (TMP), a potent inhibitor of the CYP2C8 isoenzyme that is commonly administered with sulfamethoxazole (SMX) for the treatment of urinary tract infections, is known to reduce phenytoin clearance by 30%. Given the saturable nature of phenytoin metabolism, decreases in phenytoin clearance of this magnitude may be clinically significant. WHAT THIS STUDY ADDS • Prescription of TMP/SMX was associated with a more than doubling of the risk of hospitalization for phenytoin toxicity [adjusted odds ratio 2.11, 95% confidence interval (CI) 1.24, 3.60]. • Co‐prescription of TMP/SMX and phenytoin is common. In our study, approximately 20% of phenytoin users received at least one prescription for TMP/SMX, thereby being placed at excess risk of phenytoin toxicity. AIMSPharmacokinetic studies suggest that trimethoprim (TMP) can inhibit the hepatic metabolism of phenytoin, but the clinical relevance of this is uncertain. We studied the risk of phenytoin toxicity following the prescription of trimethoprim/sulfamethoxazole (TMP/SMX), a commonly used antibiotic, among elderly patients receiving phenytoin. METHODSWe conducted a population‐based, nested case–control study of a cohort of Ontario residents aged 66 years of age or older treated with phenytoin over a 17‐year period (April 1 1992 to March 31 2009). Within this group, case patients were those hospitalized with phenytoin toxicity. For each case, we identified up to four control patients from the same cohort, matched for age and sex, and determined the odds ratio (OR) for the association between phenytoin toxicity and receipt of TMP/SMX in the preceding 30 days. RESULTSAmong 58 429 elderly patients receiving phenytoin during the study period, we identified 796 case patients hospitalized for phenytoin toxicity and 3148 matched controls. Following multivariable adjustment for potential confounders, we observed a more than doubling of the risk of phenytoin toxicity following the receipt of TMP/SMX [adjusted OR 2.11, 95% confidence interval (CI) 1.24, 3.60]. In contrast, we observed no such risk with amoxicillin, an antibiotic with similar indications but not expected to interact with phenytoin (adjusted OR 1.12, 95% CI 0.64, 1.98). CONCLUSIONAmong older patients receiving phenytoin, treatment with TMP/SMX is associated with a more than twofold increase in the risk of phenytoin toxicity. When clinically appropriate, alternate antibiotics should be considered for these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".