Clinically significant drug interactions between antiretroviral and co-prescribed drugs in HIV infected patients: retrospective cohort study
Bibliographic record
Abstract
Introduction: There are limited data on human immunodeficiency viruses (HIV) infected people in the UAE and the Gulf region. This study aimed at assessing the prevalence and risk factors for potential clinically significant drug interactions (CSDIs) in a cohort of 181 HIV infected people in Dubai. Methods: A retrospective study was conducted at the outpatient infectious diseases clinic of Rashid hospital. Consecutive HIV seropositive people on anti-retroviral therapy (ART) were included. All potential CSDIs were analyzed and classified using Liverpool HIV drug interactions database. Results: Nucleoside reverse transcriptase inhibitors (NRTIs) and integrase strand transfer inhibitors (INSTIs) were the most frequently used antiretroviral agents (ARVs), while the most common (non-ARV) were cardiovascular medication followed by antilipidemic statins. A total of 140 potential CSDIs were found in nearly half (n=86, 47.5%) of the 181 included HIV persons. Of the 140 potential CSDIs, 27 (19%) were of weak clinical relevance, 108 (77%) were of potential clinical relevance, and 5 (4%) were of contraindicated clinical relevance interactions. Moreover, 52 (37.14%) of CSDIs were between two ARVs and 88 (62.85%) were between ARV and non-ARV drugs. In the univariate analysis, age, dyslipidemia, number of medications, analgesics use, statin use, supplement intake, time since diagnosis of HIV, number of ART, and use of a protease inhibitor (PI) were significant. In the logistic regression, factors independently associated with CSDIs were the number of medications (odds ratio [OR] 1.165, 95% CI 1.021-1.329, P = 0.023) and the time since diagnosis of HIV (OR 1.156, 95% CI 1.008-1.327, P = 0.038). Conclusion: The frequency of CSDIs between ART and co-medications is high in HIV seropositive people. Awareness of the risk factors may assist clinicians to recognize and manage CSDIs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".