Prevalence and Risk of Potential Cytochrome P450–Mediated Drug-Drug Interactions in Older Hospitalized Patients with Polypharmacy
Bibliographic record
Abstract
BACKGROUND: As rates of polypharmacy rise and medication regimens become more complex, the risk of potential cytochrome P450 (CYP)-mediated drug-drug interactions (DDIs) is a growing clinical concern for older adults. OBJECTIVE: To determine the prevalence of potential CYP-mediated DDIs in older hospitalized adults with polypharmacy and analyze the relationship between the number of drugs dispensed and the probability of these interactions in this high-risk population. METHODS: A prospective 16-week cohort study was conducted among consecutive new patients aged 65 years and older with polypharmacy (>5 drugs) admitted to a community hospital. The medication profiles of these patients were analyzed with a new multidrug cytochrome-specific software program. The prevalence of potential CYP-mediated DDIs was determined, with the probability calculated as a function of the number of medications dispensed using multivariate Poisson regression adjusted for age and sex. Comparative performance of the software program and a standard 2-drug alert program for detecting these DDIs was evaluated using the Wilcoxon-Mann-Whitney rank-sum test. Pharmacists' decisions to recommend medication adjustment based on the probability of CYP-mediated DDIs were recorded. RESULTS: The prevalence of potential CYP-mediated DDIs detected among 275 older adults with polypharmacy was 80%. The probability of at least 1 CYP-mediated DDI was 50% for persons taking 5-9 drugs, 81% with 10-14 drugs, 92% with 15-19 drugs, and 100% with 20 or more drugs. Addition of each medication to a 5-drug regimen conferred a 12% increased risk of a potential CYP-mediated DDI after adjustment for age and sex (OR 1.12; 95% CI 1.09-1.14). The multidrug software identified a median increase of 3 (95% CI 2.5-3.5) potential CYP-mediated DDIs per patient, compared to use of the standard 2-drug alert software. Pharmacists targeted patients for medication adjustment or close clinical monitoring in 23% of cases. CONCLUSIONS: The prevalence of potential CYP-mediated DDIs is high in geriatric patients with polypharmacy. The risk of DDIs increases as a function of the number of medications dispensed. Pharmacists' decision to intervene for potential CYP-mediated DDIs depends on clinical judgment in addition to the output from drug alert software programs, but may be facilitated by a single multicomponent, multidrug potential CYP-mediated DDI assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".