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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".