Association of Age With Polypharmacy and Risk of Drug Interactions With Antiretroviral Medications in HIV-Positive Patients
Bibliographic record
Abstract
BACKGROUND: Interactions between antiretroviral (ARV) therapy and medications to treat age-related comorbidities are a growing concern in the aging HIV population. OBJECTIVE: To investigate the association of age with potential drug-drug interactions (PDDIs) involving ARVs. METHODS: We studied ARV-treated patients attending a tertiary care center. PDDIs were classified as "red flag" (contraindicated) or "orange flag" (use with caution or dose adjustment). Logistic regression was used to determine the association of age with the occurrence of ≥1 PDDI. RESULTS: Of 914 patients (78% male, median age 49 years), older patients (age ≥50 years) were on more drugs than younger patients (total 9 vs 7; P < .0001) and were more likely to be on ritonavir-boosted protease inhibitors (PIs), integrase inhibitors, and non-ARV medications. Older patients were more likely to have ≥1 orange flag PDDI (71% vs 55%, P < .0001) and to have a red flag PDDI (5% vs 2%, P = .07), although the latter did not reach statistical significance. A 10-year increase in age was associated with an increased likelihood of ≥1 PDDI (odds ratio [OR] = 1.72; P < .0001) after adjusting for gender, race and number and class of ARVs. The effect of age was diminished after adjusting further for the number of non-ARV medications (OR = 1.28; P = .02) and use of cardiovascular drugs (OR = 1.16; P = .21). CONCLUSIONS: In our clinic population, older patients were more likely to have a PDDI because of the greater number of non-ARV medications, particularly cardiovascular agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".