Therapy interruptions and drug interactions during acute hospitalization in patients taking direct acting antivirals for chronic hepatitis C virus infection: a retrospective cross-sectional study
Bibliographic record
Abstract
Background: Direct acting antivirals (DAA) are highly effective antivirals targeting hepatitis C virus (HCV) with real-world efficacy exceeding 90%. However, DAAs are susceptible to drug interactions; and suboptimal treatment adherence has been associated with treatment failure. DAA therapy has historically been studied in the ambulatory setting, challenges associated with acute hospitalization during DAA therapy has not been previously evaluated. We investigated the frequency of therapy interruptions, and drug interactions in patients admitted to hospital while on DAA therapy. We examined drug interaction type, severity, and mitigation strategies by clinicians. Methods: We designed a retrospective cross-sectional study including all patients hospitalized while receiving active DAA therapy between January 1st, 2015 and June 30th, 2018. Eligibility criteria included all adults older than 18 years of age and receiving active DAA therapy as an outpatient. Baseline demographics were extracted electronically; therapy interruptions and drug interactions were identified through medical chart review. Treatment success was obtained through linking our database to the provincial virology laboratory database. Results: Fifty-seven hospitalizations occurred in 54 unique patients who were receiving DAA therapy. HCV therapy was interrupted in nearly one third of all hospitalizations (18/57) with the majority of cases (14/18) representing an unintentional therapy cessation. Median duration of therapy interruption was 1 day (IQR 0–1 day, range 0–46 days) Logistical delays in obtaining the patient’s own supply of DAAs was the most common reason for un-intentional therapy interruptions. Nearly two-thirds of patients were prescribed at least one new interacting medication during their hospital admission. Most drug interactions (78.7%) were not discovered nor addressed by clinicians. However, all patients except one (98%) achieved sustained virological response at week 24 (SVR24). Conclusions: DAA therapy interruptions and drug interactions were common in hospitalized patients. Clinician education, clinical decision support tools and availability of DAAs on hospital formularies can mitigate drug interactions and supply chain issues in order to optimize care for patients admitted to hospitals while receiving HCV DAA therapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".