The World Health Organization (WHO) has set a target to eliminate viral hepatitis by 2030, aiming for a 90% diagnosis rate and an 80% treatment rate for chronic hepatitis B (CHB). However, as of 2024, only 26.1% of CHB infections globally have been diagnosed, and only 14.6% have received treatment, with treatment coverage falling far short of the target. A large number of patients are in a "Diagnosed-but-Untreated (DBU)" state, with major barriers including: low disease awareness, concerns about medication side effects, fragmented healthcare pathways, and poor physician-patient communication. Traditional hospital-based follow-up models are constrained by human resources and the capacity for health information system integration, making them difficult to scale widely in primary care settings. Supported by the National Key R\&D Program of China, our team has successfully developed the world's first infectious disease agent (Union-Agent) after more than two years of research. This study aims to conduct a multicenter, prospective, two-cohort observational and interventional investigation to identify the reasons why DBU patients fail to initiate treatment and to explore whether an intervention using the Union-Agent can significantly increase the rate of antiviral treatment initiation within six months among DBU patients who meet the antiviral indications according to the 2022 Chinese guidelines for the prevention and treatment of chronic hepatitis B. The study hypothesizes that, compared to baseline, the Union-Agent can enable 50%-60% of treatment-eligible DBU patients to initiate antiviral therapy within six months.
1. Study Overview 1.1 Study Title An Intelligent-Agent Intervention (Union-FAST) to increase antiviral treatment uptake in diagnosed-but-untreated hepatitis B patients: a multicenter prospective study 1.2 Study Rationale The World Health Organization (WHO) 2016 Global Hepatitis Strategy targets ≥90% diagnostic coverage and ≥80% treatment coverage for chronic hepatitis B (CHB) by 2030 \[1\]. However, global progress remains sluggish: in 2024, only 26.1% of CHB patients were diagnosed and 14.6% received antiviral treatment, with merely 13% of infected individuals aware of their status and \<3% on therapy by the end of 2022 \[2-3\]. China, the country with the highest HBV burden globally, faces the same gap-antiviral uptake at Union Hospital Wuhan only rose from 55% to 62% after the 2022 Chinese national guidelines, still far below the WHO target \[4\]. Diagnosed-but-untreated (DBU) CHB patients face key barriers including limited disease knowledge, concerns about adverse drug effects, fragmented care pathways, and inadequate physician-patient communication. Traditional hospital-based follow-up relies on integrated hospital information systems, limiting scalability to secondary and community care \[5\]. In contrast, AI-based intelligent-agent models enable web/mobile-delivered education, dynamic follow-up, and risk assessment, with mature applications in CHB management (e.g., 95% diagnostic concordance, real-time guideline-concordant plan generation) \[6\]. This study evaluates Union-Agent-a cloud-based AI assistant developed under China's National Key R\&D Program (2022YFC2305100), integrating speech recognition, natural language processing, knowledge-graph retrieval, and large-language-model reasoning-for DBU CHB patients. The Union-FAST framework (Find DBU patients, Analyze barriers, Strategize interventions, Tool implementation) aims to address care gaps and improve antiviral treatment initiation. 1.3 Study Objectives Primary Objectives To evaluate whether Union-Agent increases the 6-month antiviral treatment initiation rate in DBU CHB patients meeting the 2022 Chinese Guidelines for the Prevention and Treatment of Chronic Hepatitis B by at least 10 percentage points (or a 50% relative increase) from baseline. Secondary Objectives 1. Identify the multilevel barriers (individual, healthcare system, societal) to antiviral treatment in DBU CHB patients via Union-Agent's data collection and analysis. 2. Improve core HBV knowledge recognition in ≥80% of DBU patients within 6 months through Union-Agent's intelligent education modules. 3. Enhance follow-up adherence (shorten review intervals, increase on-schedule visit rates) in DBU patients with a history of irregular follow-up. 4. Timely detect high-risk events (ALT flares, HBV DNA rebound, incipient cirrhosis, hepatocellular carcinoma risk, and reduce end-stage liver disease incidence via real-time risk alerts and early intervention). 5. Optimize hierarchical care pathways to reduce unnecessary tertiary hospital referrals, waiting times, and economic burden for patients. 1.4 Study Design A multicenter, prospective, dual-cohort (observational + interventional) study with a self-controlled baseline design. The study includes two core phases: 1. Baseline cross-sectional survey: Characterize DBU patients, identify treatment barriers, and establish stratification variables for intervention. 2. Prospective interventional cohort: Patients receive routine care plus four Union-Agent-driven services, with real-time data collection to evaluate treatment uptake and clinical outcomes. Study Setting: 8 collaborating centers across China (tertiary, secondary, and community hospitals) * Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (Leading) * Zhongnan Hospital Affiliated to Wuhan University * First Affiliated Hospital of the School of Medicine, Shihezi University * Taihe Hospital in Shiyan * Yichang Central People's Hospital * The Second Affiliated Hospital of Anhui Medical University * Wuhan Jinyintan Hospital * Zigui County People's Hospital Intervention: Union-Agent (web/mobile-based AI platform) with four core modules for patients and a dedicated physician portal (see Section 3 for details). Comparator: Self-baseline (no additional digital intervention beyond routine clinical care). 2\. Study Population 2.1 Diagnostic Criteria CHB is defined as persistent HBsAg positivity for ≥6 months, in accordance with the latest Chinese Guidelines for the Prevention and Treatment of Chronic Hepatitis B. 2.2 Inclusion Criteria 1. Aged ≥18 years with persistent HBsAg positivity for ≥6 months. 2. Treatment-naïve to nucleos(t)ide analogues (NUCs), or previous NUC treatment with no medication for the past 6 months (DBU status). 3. Capable of using a smartphone independently or with family member assistance. 4. Voluntarily provides written informed consent (electronic version) and agrees to study procedures and follow-up. 2.3 Exclusion Criteria 1. Severe mental or cognitive impairment that impairs study cooperation. 2. Current participation in other interventional clinical trials that may influence HBV treatment decisions. 3. Inability to use a smartphone even with supervision. 4. Refusal to sign the informed consent form. 2.4 Sample Size Justification The sample size is calculated using the classical single-proportion precision formula for a 95% confidence interval (CI): N = (Z\_{0.975}\^2 \* p\*(1-p)) / d\^2 * Z0.975=1.96 (95% CI standard normal deviate) * p=0.5 (conservative estimate to maximize variance) * d=0.022 (allowable margin of error: ±2.2 percentage points) Calculated sample size: 1,985 patients. Accounting for: DEFF = 1 + (m - 1) \* ρ * Design effect (DEFF): , 1.5-3.0 to adjust for intra-center correlation in the multicenter design. * Loss to follow-up: 10-15%. Final planned enrollment: 2,000 DBU CHB patients (expected screen: 2,200 patients; enrollment period: 6 months). 3\. Intervention: Union-Agent AI Platform Union-Agent is a cloud-based intelligent agent developed for HBV care, with a bidirectional interface (physician + patient) and closed-loop management of education, medication, follow-up, and referral. All functions comply with the 2022 Chinese CHB Guidelines and 2024 WHO HBV Treatment Guidelines. 3.1 Core Services for Patients 1. Guideline-based personalized education: Tailored to baseline HBV knowledge, including concise disease information, antiviral benefits, drug safety, and short knowledge assessments. 2. Standardized treatment recommendations: Clear, interpretable guidance on first-line NUC regimens (entecavir \[ETV\], tenofovir disoproxil fumarate \[TDF\], tenofovir alafenamide fumarate \[TAF\]). 3. Individualized medication reminders: Real-time adherence logging, alerts for missed doses, and coping strategies for common adverse events. 4. Semantic follow-up and risk assessment: Patient self-report of symptoms (voice/image input), conversion to structured data, and automated risk stratification with real-time alerts for high-risk events. 5. Automated scheduling: Reminders for routine reviews, lab tests, and appointment booking; support for uploading lab results for physician review. 3.2 Physician Portal Functions 1. Patient profile creation and verification of antiviral treatment eligibility per national guidelines. 2. Automated alerts for eligible but untreated DBU patients. 3. Review of patient-reported symptoms, structured risk assessments, and high-risk event alerts. 4. Push of tailored education, medication reminders, and follow-up tasks to the patient interface. 5. Direct communication with patients (reminders, referral links, and clinical evaluations) for missed follow-up or incomplete tasks. 6. Generation of individualized long-term risk assessments (cirrhosis, hepatocellular carcinoma, liver failure). 3.3 Data Security and Privacy * Access limited to authorized personnel via multi-factor authentication. * All data exported in de-identified, encrypted formats (k-anonymization, federated learning; ISO/IEC 27799 compliant). * Unified data governance framework adhering to Chinese national information security regulations, the Declaration of Helsinki, and Good Clinical Practice (GCP) standards. * Privacy breach risk: \<2%. 4. Study Procedures and Timeline The total study duration is 18 months, divided into 4 phases (Union-FAST implementation aligned with all phases): Phase Timeframe Key Activities 1. Study Setup \& System Validation Months 0-2 Finalize Union-Agent modules; 2 simulation cycles; obtain Institutional Review Board (IRB)/Ethics Committee (EC) approval; train investigators at all centers. 2. Patient Recruitment \& Baseline Survey Months 3-8 Screen 2,200 patients; enroll 2,000 eligible DBU patients; conduct cross-sectional baseline survey (demographics, medical history, HBV knowledge, treatment barriers) via Union-Agent's mobile interface; establish baseline treatment initiation rate and stratification variables. 3. Intervention \& Real-Time Data Collection Months 9-14 Implement 6-month Union-Agent intervention; collect real-time data (treatment initiation, medication adherence, follow-up visits, lab results, high-risk events); deliver routine clinical care plus Union-Agent services. 4. Data Analysis \& Reporting Months 15-18 Comprehensive data cleaning and statistical analysis; prepare clinical study report; draft 1-2 peer-reviewed manuscripts; present interim results at AASLD 2026/APASL 2027; submit final manuscript in 2027. 4.1 Follow-Up Requirements * Frequency: Real-time digital follow-up via Union-Agent; in-person/clinical follow-up per routine CHB care guidelines (adjusted per patient risk stratification). * Assessments: HBV knowledge tests (baseline + 6 months); lab tests (ALT, HBV DNA, liver function; per clinical need); treatment initiation status (6 months); high-risk event detection (real-time); patient satisfaction survey (6 months). 4.2 Study Termination for Individual Patients 1. Voluntary withdrawal by the patient. 2. Severe non-study-related illness or death that prevents study participation. 3. Protocol violation by the patient (e.g., non-compliance with Union-Agent use). 4. Investigator decision (e.g., clinical deterioration requiring urgent intervention). 5. Outcome Measures 5.1 Primary Outcome 6-month antiviral treatment initiation rate in DBU patients meeting 2022 Chinese CHB guideline indications: * Absolute increase: ≥10 percentage points from baseline. * Relative increase: 50% from baseline. 5.2 Secondary Outcomes <!-- --> 1. Barrier characterization: Identification of individual/healthcare system/societal barriers to treatment via baseline and intervention-phase data (Union-Agent survey and clinical records). 2. HBV knowledge acquisition: Proportion of patients with correct recognition of core HBV knowledge (≥80% at 6 months). 3. Follow-up adherence: Change in average review intervals (30% reduction from baseline); on-schedule visit rate; follow-up completion rate. 4. High-risk event detection: Proportion of high-risk events (ALT flares, HBV DNA rebound, incipient cirrhosis) detected in real time (≥90%); incidence of end-stage liver disease events (compared with historical controls). 5. Care pathway optimization: Reduction in unnecessary tertiary hospital referrals; decrease in average appointment waiting time; patient satisfaction score (≥80/100 at 6 months). 6. Medication adherence: Proportion of patients with ≥80% medication adherence (real-time logging via Union-Agent). 5.3 Safety Outcomes This study involves no additional drug/surgical intervention (only digital AI-based care support). Safety outcomes include: • Adverse events (AEs) related to routine HBV antiviral treatment (captured via Union-Agent and clinical records). * Severe adverse events (SAEs): Any life-threatening, disabling, or fatal event related to HBV or its treatment (reported to the IRB/EC within 24 hours). * No expected AEs related to Union-Agent use (non-invasive digital intervention). 6. Statistical Analysis 6.1 Analysis Sets 1. Intention-to-Treat (ITT) Set: All enrolled patients (primary analysis set), regardless of intervention compliance or loss to follow-up. 2. Per-Protocol (PP) Set: Patients who complete the 6-month intervention and adhere to all study procedures (sensitivity analysis set). 6.2 Descriptive Statistics * Continuous variables: Mean ± standard deviation (SD) or median (interquartile range, IQR) (per normality test). * Categorical variables: Frequency and percentage (n, %). 6.3 Inferential Statistics 1. Primary outcome: 6-month treatment initiation rate estimated via Generalized Estimating Equations (GEE) (adjusted for intra-center correlation); Generalized Linear Mixed Models (GLMM) as a robustness check. 2. Secondary outcomes: o Proportional outcomes (HBV knowledge, high-risk event detection): Chi-square test/Fisher's exact test; logistic regression (adjusted for clustering). o Continuous outcomes (review intervals, waiting time): t-test/Mann-Whitney U test; linear regression (adjusted for clustering). * Subgroup analyses: Conducted via interaction terms in regression models (e.g., age, gender, hospital tier, baseline HBV knowledge). 3. Missing data: Addressed via multiple imputation (MI); inverse probability weighting (IPW) as a sensitivity analysis. 6.4 Statistical Software * SPSS (Version 26.0) and R (Version 4.3.0) * Significance level: Two-sided P \< 0.05 considered statistically significant. * Confidence interval: 95% CI reported for all primary/secondary outcomes. 7. Study Team and Responsibilities 7.1 Core Study Team Role Name Affiliation Key Responsibilities PI Xin Zheng Union Hospital, HUST Study design, overall coordination, data quality control, manuscript finalization Co-PI Yanqin Du Union Hospital, HUST Protocol development, data collection/analysis, manuscript drafting, investigator training Sub-PI Zhiyong Ma Zhongnan Hospital, WHU Patient enrollment/management in sub-site Sub-PI Qingfeng Zhu First Affiliated Hospital of the School of Medicine to Shihezi University Patient enrollment/management in sub-site Sub-PI Zhongji Meng Taihe Hospital in Shiyan Patient enrollment/management in sub-site Sub-PI Qing Fang Yichang Central People's Hospital Patient enrollment/management in sub-site Sub-PI Zhenhua Zhang The Second Affiliated Hospital of Anhui Medical University Patient enrollment/management in sub-site Sub-PI Huadong Li Wuhan Jinyintan Hospital Patient enrollment/management in sub-site Sub-PI Junlin Lu Zigui County People's Hospital Patient enrollment/management in sub-site AI R\&D Lead Di Liu Beijing Jiakang Zhongzhi Technology Co., Ltd. Union-Agent software development, algorithm optimization AI R\&D Specialist Ang Min Beijing Jiakang Zhongzhi Technology Co., Ltd. Data statistics, system integration, platform maintenance Clinical Investigators Baoju Wang, Jun Wu, Bin Zhu, Junzhong Wang Union Hospital, HUST Patient enrollment/management, clinical data collection Clinical Research Nurse Juan Xu Union Hospital, HUST Study coordination, task allocation, patient follow-up Administrative Coordinator Xue Bai Union Hospital, HUST Inter-center communication, logistics support Research Fellow Yiwen Shu Union Hospital, HUST Data entry, quality control, survey administration External Statistician Independent - Statistical design, interim/final analysis, data interpretation 7.2 Investigator Qualifications • All clinical investigators hold national GCP certificates and specialize in infectious diseases/hepatology. * AI R\&D specialists have expertise in machine learning, natural language processing, and digital health platform development. * The study team has extensive experience in multicenter clinical trials and AI-based chronic disease management (with national key R\&D program funding). 8\. Ethics and Regulatory Compliance 8.1 Ethics Approval The study protocol has been submitted to the IRB/EC of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (and local IRBs/ECs of all collaborating centers). All study procedures will be implemented only after written IRB/EC approval (approval number pending). The protocol will be amended and re-approved for any significant changes, with all amendments communicated to investigators and trial registries. 8.2 Informed Consent * Written informed consent (electronic version) will be obtained from all patients before any study procedure. * Consent forms use plain language (verbal explanation provided for illiterate patients, with a neutral witness present). * Patients receive a signed copy of the consent form; the original is stored at the leading center. * Patients are informed of their right to withdraw from the study at any time, without penalty or impact on routine clinical care. 8.3 Regulatory Compliance * Adheres to the Declaration of Helsinki (2013), ICH-GCP guidelines, and Chinese national clinical trial regulations. * All study records (informed consent forms, case report forms, lab results, data logs) are retained for at least 5 years after study completion (per GCP requirements). * The study will be registered in a public international clinical trial registry (e.g., ClinicalTrials.gov, WHO ICTRP) before patient enrollment. 8.4 Conflict of Interest No individual or institutional financial/non-financial conflicts of interest exist for this study. All study team members have completed a conflict of interest declaration form. 9\. Budget and Funding Total Study Budget: CNY 968137.92 (no patient compensation provided) Budget breakdown (key categories): * Study start-up (IRB/EC fees, coordination): CNY 33621.86 * Personnel (PI/Co-PI, investigators, AI R\&D): CNY 436368.84 * Union-Agent development \& lab tests: CNY 143071.75 * Data analysis \& medical writing/publishing: CNY 57228.7 * Conference presentation (AASLD/APASL): CNY 78689.46 * Inter-center investigator travel: CNY 57228.7 * Indirect costs (overhead, platform support): CNY 161241.86 Funding Source: This project was funded by Gilead company. 10. Dissemination Plan 1\. Conference Presentations: Interim results at AASLD 2026 or APASL 2027; final results at major hepatology/infectious diseases conferences. 2\. Peer-Reviewed Publications: 1-2 manuscripts submitted to high-impact journals (e.g., Journal of Hepatology, Hepatology, BMC Public Health) in 2027 (open access preferred). 3\. Clinical Practice Translation: Study results shared with Chinese and international hepatology societies to inform HBV care guidelines and digital health policy. 4\. Public Health Communication: Summary of findings for healthcare providers (especially primary/community care) and HBV patient groups to improve disease awareness and treatment access. 5\. Data Sharing: De-identified study data will be shared with qualified researchers upon request (per data governance framework and IRB/EC approval). 11\. References 1. World Health Organization. Global health sector strategy on viral hepatitis 2016-2021: Towards ending viral hepatitis. Geneva: WHO; 2016. 2. Yan R, Sun M, Yang H, et al. 2024 latest report on hepatitis B virus epidemiology in China: current status, changing trajectory, and challenges. Hepatobiliary Surgery And Nutrition. 2025;14(1):66-77. 3. World Health Organization. Global hepatitis report 2024: action for access in low- and middle-income countries. Geneva: WHO; 2024. 4. Du Y, Zheng Y, Wang H, Zheng X. The epidemiological characteristics of patients with chronic hepatitis B: a single-center retrospective study. APASL 2025, Beijing (PP0154); 2025. 5. Deng Q, Wu S, Liu W. Effect of social influence on antiviral therapy behavior among chronic hepatitis B patients with different disease knowledge levels. BMC Public Health. 2025 Apr 17;25(1):1441. doi: 10.1186/s12889-025-22683-7. PMID: 40247234; PMCID: PMC12004565. 6. Romero-Vico J, Feliu Anna, Vargas-Accarino Elena, et al. Enhancing Chronic Hepatitis B and D Management Through a Tailored Mobile Health Application: Real-World Outcomes From the Adaptation of the NORA App. J Viral Hepat.2026;33(4):e70160. doi:10.1111/jvh.70160
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
2,000
Patients, while receiving usual care, will also receive four services from Union-Agent: (i) guideline-based educational modules tailored to patients' baseline knowledge of hepatitis B; (ii) personalized medication reminders with real-time adherence records; (iii) semantic follow-up that converts patient-reported symptoms into structured data and generates early risk alerts; and (iv) algorithm-driven referral suggestions aligned with China's three-tiered healthcare referral system.
Department of Infectious Disease, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
Proportion of DBU patients initiating antiviral treatment
6-month antiviral treatment initiation rate in DBU patients meeting 2022 Chinese CHB guideline indications: * Absolute increase: ≥10 percentage points from baseline. * Relative increase: 50% from baseline.
Time frame: from enrollment to six months post-enrollment
Multilevel Barriers to Antiviral Treatment in DBU CHB Patients
Identification of multilevel barriers (individual, healthcare system, societal) to antiviral treatment in diagnosed-but-untreated (DBU) chronic hepatitis B (CHB) patients, via Union-Agent's built-in structured questionnaire and clinical record review
Time frame: From baseline assessment until 6 months of intervention, assessed at baseline and 6 months of intervention via Union-Agent's built-in structured questionnaire and clinical record review.
Proportion of DBU Patients with Correct Recognition of Core HBV Knowledge
Proportion of DBU CHB patients who achieve ≥80% correct answers on the Union-Agent HBV Knowledge Assessment Scale (a validated, 20-item scale).
Time frame: From baseline assessment until 6 months of intervention, assessed at baseline and 6 months of intervention using the Union-Agent HBV Knowledge Assessment Scale.
Change in Average Review Intervals in DBU Patients with Irregular Follow-Up History
Change from baseline in average review intervals of DBU CHB patients with a history of irregular follow-up, assessed via Union-Agent follow-up logs.
Time frame: From the start of Union-Agent intervention until 6 months of intervention, assessed up to 6 months via Union-Agent follow-up logs to determine changes in average review intervals.
Proportion of Real-Time High-Risk Event Detection in DBU Patients
Proportion of high-risk events (ALT flares \[defined as ALT \>2× upper limit of normal\], HBV DNA rebound \[defined as HBV DNA increase \>1 log10 IU/mL from nadir\], incipient cirrhosis \[assessed by FibroScan and FIB-4 formula \]) detected in real time via the Union-Agent risk stratification module
Time frame: From the start of Union-Agent intervention until 6 months of intervention, assessed up to 6 months via the Union-Agent risk stratification module to detect high-risk events in real time.
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