Hepatitis C treatment can be provided safely, effectively within community-based setting

NewsGuard 100/100 Score

A new study, presented today, demonstrates treatment for Hepatitis C can be provided safely and effectively within a community-based and non-specialist setting. This illustrates the potential for alternative providers to ease pressure on currently overburdened specialists. The study, sponsored by the National Institutes of Health, was presented at The International Liver Congress 2016 in Barcelona, Spain.

Between 130 and 150 million people globally have chronic Hepatitis C virus (HCV) infection. It is estimated that 15 million people in the World Health Organization's EU Region are living with Hepatitis C, representing 2% of adults.2

"With such a large patient cohort, ensuring that patients can access safe, effective and appropriate treatment is essential," said Dr Sarah Kattakuzhy of the Institute of Human Virology at the University of Maryland, Baltimore, USA and lead author of the study. "Currently, the limited availability of experienced specialists restricts rapid expansion of Hepatitis C treatment, compromising the goal of global eradication. As such, care models which bypass this therapeutic bottleneck must be explored."

The multi-centre, open label, Phase 4 clinical trial assessed chronic HCV-infected patients at community health centres in the United States. Patients received non-randomised treatment from a specialist provider, primary care physician or nurse practitioner. According to study protocols, providers underwent uniform three hour training on the Infectious Disease Society of America (IDSA) - American Association for the Study of Liver Disease (AASLD) guidelines for HCV.

To ensure continuity, patients received the same standardised treatments with direct-acting antivirals (ledipasvir and sofosbuvir), with outcomes assessed via unquantifiable HCV RNA viral load 12 weeks after the completion of treatment (SVR12) and by a composite score of attendance.Patients participating in the study were inclusive of challenging subpopulations; predominantly they were black (96%) and genotype 1a (72%), 24% were co-infected with HIV and HCV, 18% were treatment experienced and 20% had cirrhosis, or scarring of the liver.

The study found that of the 304 patients, 285 achieved SVR12 (93.8% per protocol; 88.2% intention-to-treat including patients who discontinued medication early), with no significant difference identified between providers for achieving this outcome. SVR12 was achieved by 92.1% of patients receiving care from specialists, 96.7% of patients receiving care from primary care physicians and 94.9% of patients receiving care from nurse practitioners.

"The data presented here is extremely welcome and shows great potential to escalate treatment options and protocols for Hepatitis C. We have the therapies, we now need to make sure we can effectively roll them out to patients," said Professor Tom Hemming Karlsen, EASL Vice-Secretary. "We know we have too few experienced specialists treating HCV and this is severely hampering our ability to eradicate this disease once and for all. This research has the potential to be a genuine game changer in the way we look at HCV treatment across the board, and could provide the opportunity to increase access to care and treatment to many regions of the world."

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Machine learning model to determine associations between metabolic syndrome and lactation