MétaCan
Menu
Back to cohort
Record W1523483459 · doi:10.1111/add.12893

Treating substance use disorders in patients with hepatitis C

2015· editorial· en· W1523483459 on OpenAlexaboutno aff
Michael A. Cucciare, Ramsey Cheung, Catherine Rongey

Bibliographic record

VenueAddiction · 2015
Typeeditorial
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatitis CLiver diseasePopulationHepatitis C virusInternal medicineComorbidityExacerbationIntensive care medicineImmunologyVirus

Abstract

fetched live from OpenAlex

Substance use disorders are prevalent among people with hepatitis C virus (HCV) and adversely affect HCV management. Addressing this comorbidity is paramount as more effective HCV treatment options emerge. Existing screening tools and treatment options for addressing the spectrum of substance use disorders have the potential to improve HCV treatment outcomes Between 2 and 3% (130–170 million) of the global population has been infected with the hepatitis C virus (HCV) 1, which is associated with progressive liver fibrosis and end-stage liver disease 2. Further, cirrhosis from HCV is the leading indication for liver transplantation world-wide and in the United Kingdom, Australia, Canada and the United States. Achieving sustained virological response (SVR, a marker of virological clearance) has been to shown to markedly reduce mortality and morbidity from HCV-associated liver disease. Comorbid psychiatric conditions including substance use disorders (SUDs) remain significant barriers to HCV treatment and require that providers be prepared to address these conditions to optimize care. Starting in late 2013, HCV antiviral therapy moved into the interferon-free all-oral therapy era. The US Food and Drug Administration has approved four all-oral regimens, and more therapeutic options are expected 3. Weekly injectable pegylated interferon for 24–48 weeks was once the mainstay of therapy, complicating regimens with exacerbation or precipitation of underlying psychiatric conditions. New oral regimens promise higher SVR rates in shorter duration (8–24 weeks) and fewer neuropsychiatric side effects. Consequently, some subgroup conditions thought previously to be relative contraindications for therapy (i.e. history of severe depression) are now potentially eligible for HCV treatment. However, the detection and treatment of SUDs remain an important component of HCV treatment guidelines for optimizing care in this patient population 4. SUDs are highly prevalent among people with HCV. Among patients with HCV, 58–78% had a life-time (past or present) drug or alcohol use disorder 5, 6, while 21% of out-patients screened positive for current heavy drinking 6. In a study of privately insured patients with HCV, 93% reported consuming alcohol prior to their HCV diagnosis, while 64% reported risky drinking prior to receiving HCV treatment 7. High rates of SUDs observed in this population pose a significant challenge for providers in managing HCV. Comorbid SUDs can impact HCV management adversely, with challenges ranging from complicating access to HCV treatment to reducing treatment effectiveness. Patients with active intravenous drug abuse are less likely and willing to seek HCV treatment 8. Patients with alcohol use disorders are less likely to be eligible for and/or complete HCV treatment, reducing the likelihood of SVR 9. Furthermore, excessive alcohol use remains a major cause of liver disease and cirrhosis, even in the absence of HCV. These findings highlight the importance of detecting and treating SUDs in this patient population. Brief screening instruments to identify existing SUDs are used world-wide. The Alcohol Use Disorders Identification Test (AUDIT) is used in Brazil, South Africa, the United Kingdom and the United States to screen for alcohol misuse. The US Department of Veterans Affairs (VA) mandates the use of the three consumption items of the AUDIT (or AUDIT-C) as a screening tool for identifying alcohol misuse in out-patients 10. The AUDIT-C has been shown to accurately detect risky drinking, defined as drinking above recommended gender-specific limits set by the National Institute on Alcohol Abuse and Alcoholism, in out-patients 10. Similarly, researchers at Boston University have found that a single-item drug screener, ‘How many times in the past year have you used an illegal drug or used prescription medication for non-medical reasons?’, is also accurate for detecting current illicit drug use and drug use disorders among patients presenting to primary care 11. The brief nature of these screening instruments makes them well suited for use in busy out-patient clinics, as they can be completed quickly, by various means including paper or computer, and without the need for provider involvement. The results of these screening instruments can also provide the clinical benefit of indicating an appropriate course of SUD treatment. For example, scores on the AUDIT-C indicating risky (but not dependent) drinking may suggest the use of a brief alcohol intervention (BAI), while scores indicating probable alcohol dependence suggest the need for more intensive addiction treatment 12. Providers have several options for treating SUDs in patients with HCV. BAIs have long been considered to be a low-cost, effective frontline intervention for patients screening positive for risky drinking 13. They are also well suited to the out-patient clinic setting, as they are brief (ranging from a 10–15-minute single session to four sessions) in nature and can be administered by a computer or provider. BAIs typically consist of personalized feedback that includes normative comparisons of drinking behavior and psychoeducation about the consequences of alcohol misuse. Research shows that BAIs can reduce weekly alcohol consumption and result in fewer heavy drinking episodes 1 year post-intervention in out-patient populations 13. Unfortunately, the evidence for the effectiveness of brief interventions for reducing drug use remains limited 14. Patients with alcohol dependence often require more intensive addiction treatment. This poses a significant challenge for clinics that operate on limited resources, including little available staff time, expertise and knowledge and availability of addiction treatment options. Within-clinic SUD interventions are possible 15, but referral to addiction treatment will probably remain the most feasible method for clinics to support patients with comorbid SUDs. However, challenges to linking patients to addiction treatment are well documented and include fear of treatment, privacy concerns and poor treatment availability 16. Overcoming these barriers through case management 17 or other brief, potentially low-cost interventions to promote linkage to addiction treatment is paramount for optimizing the management of HCV. Improving linkage to addiction treatment in the United States will be especially important in the context of the Affordable Care Act, which promises greater access to SUD care, particularly for patients with limited financial resources 18. In summary, SUDs are endemic in patients with HCV and represent a significant proportion of the treatment-naive cohort. As antiviral therapies have now evolved to high efficacy, with shorter duration and fewer side effects, more rapid identification and mitigation of patient comorbid conditions including SUDs is becoming increasingly paramount, as well as the treatment of alcohol use disorders with or without HCV to prevent cirrhosis. Dr Cheung has received research funding from Gilead Sciences. This material is based upon work supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development (CRE 12-009), to Dr Cucciare. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.228
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.278
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAddictionSame topicHepatitis C virus researchFrench-language works237,207