Management of drug interactions with direct‐acting antivirals in Dutch <scp>HIV</scp>/hepatitis C virus‐coinfected patients: adequate but not perfect
Bibliographic record
Abstract
OBJECTIVES: Direct-acting antivirals (DAAs) for treatment of chronic hepatitis C virus (HCV) infection can cause drug-drug interactions (DDIs) with combination antiretroviral therapy (cART) and non-cART co-medication. We mapped how physicians manage DDIs between DAAs and co-medication and analysed treatment outcomes. METHODS: Data were prospectively collected as part of the ATHENA HIV observational cohort and retrospectively analysed. Dutch patients with HIV/HCV coinfection who initiated treatment with DAAs between January 2015 and May 2016 were included. Co-medication 3 months prior to and during DAA therapy was identified. Potential DDIs with the DAAs were checked using http://hep-druginteractions.org. DDIs were categorized as: (1) no interaction expected; (2) potential interaction; (3) contra-indication; (4) no recommendation. These categories were used to determine which patients switched or had a DDI during DAA therapy with co-medication. RESULTS: A total of 423 patients were treated with DAAs, of whom 418 (99%) used cART and 251 (59%) used non-cART co-medication. Before commencing DAA treatment, in 17 of 84 (20%) patients the non-cART co-medication which could result in a category 2/3 DDI was discontinued before DAA initiation, including two of six (33%) prescriptions of category 3 drugs. A total of 196 of 418 (47%) patients had a category 2/3 DDI between their DAA regimen and cART. Category 2/3 DDIs were prevented by switching cART in 78 of 147 (53%) and 47 of 49 (98%) patients. Overall, 367 of 423 (87%) patients have achieved a sustained virological response (33 in follow-up). CONCLUSIONS: Prescription patterns suggest that physicians are aware of potential DDIs between co-medication and DAAs, in particular potential DDIs with cART. Greater awareness is needed concerning category 3 interactions between non-cART co-medication and DAAs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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, 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".