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Scaling up the in-hospital hepatitis C virus care cascade in Taiwan
Chung-Feng Huang1,2,3, Pey-Fang Wu1,4, Ming-Lun Yeh1,2
1Hepatobiliary Division, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Insights
The R.N.A. model significantly improved hepatitis C virus (HCV) RNA testing and treatment rates, especially for patients outside of hepatology departments. This strategy enhances HCV care cascade and in-hospital elimination efforts.
Area of Science:
- Hepatology
- Infectious Diseases
- Public Health
Background:
- Hepatitis C virus (HCV) care cascade faces significant obstacles in diagnosis and treatment.
- Timely and accurate diagnosis is crucial for effective HCV management and elimination.
- In-hospital strategies are needed to improve patient access to care.
Purpose of the Study:
- To evaluate the effectiveness of the R.N.A. model (in-hospital HCV reflex testing, automatic appointments, and late call-back) in improving HCV treatment rates.
- To compare HCV treatment uptake in patients managed with the R.N.A. model versus standard care.
- To identify improvements in HCV diagnosis and treatment allocation within the care cascade.
Main Methods:
- A retrospective study comparing 125 patients managed with the R.N.A. model (2020) against 1,396 controls (2019).
- Analysis focused on comparing gaps in HCV RNA diagnosis to final treatment allocation.
- Patient data stratified by referring outpatient department (hepatology vs. non-hepatology).
Main Results:
- HCV RNA testing rates significantly increased with the R.N.A. model (100% vs. 84.8%, P<0.001), particularly in non-hepatology departments (100% vs. 23.3%, P<0.001).
- The overall HCV treatment rate rose to 83% in the R.N.A. model group.
- Significant improvements in treatment rates were observed for non-hepatology patients (73.9% vs. 27.8%, P=0.001), increasing in-hospital uptake from 6.4% to 73.9% (P<0.001).
Conclusions:
- The R.N.A. model effectively enhances the hepatitis C virus care cascade.
- This strategy significantly increases HCV treatment uptake, particularly for patients managed outside of specialized hepatology services.
- The R.N.A. model provides a viable framework for improving in-hospital HCV elimination initiatives.
Background/Aims:
Obstacles exist in facilitating hepatitis C virus (HCV) care cascade. To increase timely and accurate diagnosis, disease awareness and accessibility, in-hospital HCV reflex testing followed by automatic appointments and a late call-back strategy (R.N.A. model) was applied. We aimed to compare the HCV treatment rate of patients treated with this strategy compared to those without.
Methods:
One hundred and twenty-five anti-HCV seropositive patients who adopted the R.N.A. model in 2020 and another 1,396 controls treated in 2019 were enrolled to compare the gaps in accurate HCV RNA diagnosis to final treatment allocation.
Results:
The HCV RNA testing rate was significantly higher in patients who received reflex testing than in those without reflex testing (100% vs. 84.8%, P<0.001). When patients were stratified according to the referring outpatient department, a significant improvement in the HCV RNA testing rate was particularly noted in patients from non-hepatology departments (100% vs. 23.3%, P<0.001). The treatment rate in HCV RNA seropositive patients was 83% (83/100) after the adoption of the R.N.A. model, among whom 96.1% and 73.9% of patients were from the hepatology and non-hepatology departments, respectively. Compared to subjects without R.N.A. model application, a significant improvement in the treatment rate was observed for patients from non-hepatology departments (73.9% vs. 27.8%, P=0.001). The application of the R.N.A. model significantly increased the in-hospital HCV treatment uptake from 6.4% to 73.9% for patients from non-hepatology departments (P<0.001).
Conclusion:
The care cascade increased the treatment uptake and set up a model for enhancing in-hospital HCV elimination.
