Reported adverse events related to use of hepatitis C virus direct-acting antivirals with opioids: 2017–2021
Bibliographic record
Abstract
INTRODUCTION: Due to concerns over potential interactions between some hepatitis C direct-acting antivirals (DAAs) and opioids, we describe adverse event (AE) reports of concomitant use of opioids and DAAs. METHODS: AEs reported (July 28, 2017-December 31, 2021) with the administration of the DAAs glecaprevir/pibrentasvir, sofosbuvir/velpatasvir, ledipasvir/sofosbuvir, sofosbuvir/velpatasvir/voxilaprevir, and elbasvir/grazoprevir as suspect products were downloaded from the US Food and Drug Administration AE Reporting System Public Dashboard. The number of AE reports containing opioids (fentanyl, hydrocodone, oxycodone) as co-suspect products/concomitant products were counted and summarized by severity, reporting country and whether an outcome of death was reported. Overdose AEs were counted irrespective of opioid use, and changes over time were assessed. RESULTS: In total, 40 AEs were reported for DAAs and concomitant fentanyl use, 25 (62.5%) were in the USA, 35 (87.5%) were considered serious, and 14 (35.0%) resulted in death; and 626 were reported with concomitant oxycodone/hydrocodone use, 596 (95.2%) were in the USA, 296 (47.3%) were considered serious, and 28 (4.5%) resulted in death. There were 196 overdose AEs (32 [16%] deaths) declining from 2018 (N = 56) to 2021 (N = 29). CONCLUSIONS: Treating people with hepatitis C virus (HCV) infection who use drugs is key to achieving HCV elimination. Low numbers of DAA AE reports with opioids may provide reassurance to prioritize HCV treatment in this population. These data contribute to evidence supporting the continued scale-up of DAA treatment among people who use drugs to achieve HCV elimination goals.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".