Improving the hepatitis C virus care cascade with the in-hospital Reflex tEsting ALarm-C (REAL-C) model.

ABSTRACT

BACKGROUND: The World Health Organization (WHO) has set targets to eliminate viral hepatitis, including hepatitis C virus (HCV) infection, by 2030. We present the results of the in-hospital Reflex tEsting ALarm-C (REAL-C) model, which incorporates reflex HCV RNA testing and sending alerts to physicians. METHODS: We conducted a retrospective study analysing the data of 1730 patients who newly tested positive for anti-HCV between March 2020 and June 2023. Three distinct periods were defined pre-REAL-C (n = 696), incomplete REAL-C (n = 515) and complete REAL-C model periods (n = 519). The primary outcome measure was the HCV RNA testing rate throughout the study period. Additionally, we assessed the referral rate to the gastroenterology department, linkage time for diagnosis and treatment and the treatment rate. RESULTS: The rate of HCV RNA testing increased significantly from 51.0% (pre-REAL-C) to 95.6% (complete REAL-C). This improvement was consistent across clinical departments, regardless of patients' comorbidities. Among patients with confirmed HCV infection, the gastroenterology referral rate increased from 57.1% to 81.1% after the REAL-C model. The treatment rate among treatment-eligible patients was 92.4% during the study period. The mean interval from anti-HCV positivity to HCV RNA testing decreased from 45.1 to 1.9 days. The mean interval from the detection of anti-HCV positivity to direct-acting antiviral treatment also decreased from 89.5 to 49.5 days with the REAL-C model. CONCLUSION: The REAL-C model, featuring reflex testing and physician alerts, effectively increased HCV RNA testing rates and streamlined care cascades. Our model facilitated progress towards achieving WHO's elimination goals for HCV infection.