Prescription patterns of comedications associated with drug-drug interactions risk in HCV-infected patients undergoing direct-acting antiviral treatment: an analysis of an administrative claims database in Japan
Bibliographic record
Abstract
Abstract Introduction While direct-acting antivirals (DAA) are effective treatment for hepatitis C virus (HCV) patients, concerns about drug-drug interactions (DDIs) remain a significant challenge. Although there are several studies investigating the risk of DDIs associated with DAA therapy, there is limited research evaluating DDIs of DAA therapy in real-world settings in Japan. We investigated prescription patterns of comedication associated with DDIs risk in HCV patients receiving DAA therapy using a large Japanese database. Methods This was a descriptive epidemiological study, using the Japanese administrative claims database provided by DeSC Healthcare, Inc. Patients who initiated sofosbuvir/velpatasvir (SOF/VEL) or glecaprevir/pibrentasvir (GLE/PIB) between April 2017 and August 2023 were identified from the data. The primary outcome was DDIs associated with comedications which were assessed based on both Japanese package inserts and the Liverpool HEP Drug Interaction Checker (Liverpool HEP checker). Results Patients included in this study were 7,338, with 467 prescribed SOF/VEL and 6,871 prescribed GLE/PIB. The mean age of the patients was 69.9 years (SD = 13.1), with 50% being male. The median number of comedications was higher in the SOF/VEL group (14.0; IQR = 14.0) than in the GLE/PIB group (9.0; IQR = 12.0) and based on package insert and Liverpool HEP checker, the DDI risk was present in 59.3% (277) of the SOF/VEL group and 51.5% (3,542) of the GLE/PIB group. DDI risk involving two or more medications in combination with a DAA was 14.1% (66) in the SOF/VEL group and 24.0% (1,648) in the GLE/PIB group. In terms of DDI severity, in the SOF/VEL group there were no patients identified under the level “Contraindication (Red)” category, indicating medications that do not co-administered, in contrast with the 1.7% (115) in the GLE/PIB group who were identified as “contraindication (red)”. Conclusion A considerable proportion of patients were prescribed medications with DDI risk during DAA treatment. A small but notable proportion of patients were on “Contraindication (Red)” medications. Consideration of the potential DDI risks associated with comedications by healthcare professionals is advised, referring not only to package inserts but also tools such as Liverpool HEP checker to guide safe prescribing when initiating DAA therapy for HCV patients.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".