Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 40
- 主要终点
- Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care
研究概览
简要总结
The goal of this clinical trial is to learn whether an artificial intelligence (AI) tool called FibroX can help primary care providers better diagnose significant liver fibrosis (≥F2) and clinically significant portal hypertension in adults with metabolic dysfunction-associated steatotic liver disease (MASLD).
The main questions it aims to answer are:
- Can FibroX improve the accuracy of diagnosing significant liver fibrosis (≥F2) and clinically significant portal hypertension compared to usual care?
- Is FibroX easy to use and acceptable to primary care providers in simulated clinical settings?
- Do providers trust FibroX as a decision-support tool?
Researchers will compare FibroX-assisted care to usual care to see if FibroX improves diagnostic accuracy, provider trust, and supports better decision-making.
Participants will:
- Be primary care providers (MDs, DOs, NPs, PAs) from diverse clinics
- Review simulated patient cases with MASLD risk factors
- Use either usual care tools (standard labs and optional FIB-4 calculator) or FibroX (AI-generated risk score, triage band, and explainability panel)
- Make diagnostic and referral decisions for each case
- Complete surveys on usability, trust in AI, confidence, and cognitive workload
This study will help determine whether FibroX can be integrated into real-world primary care workflows to support earlier and more accurate detection of liver fibrosis and portal hypertension, potentially reducing missed diagnoses, unnecessary referrals, and improving patient outcomes.
详细描述
This study is a 12-month pilot clinical trial designed to evaluate the feasibility, usability, provider trust, and preliminary effectiveness of FibroX, an explainable artificial intelligence (AI) tool developed to improve the diagnosis of significant liver fibrosis (≥F2) and clinically significant portal hypertension in adults with metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD is a common and progressive liver condition that can lead to cirrhosis, liver failure, and increased cardiovascular risk. Early detection of these conditions is critical because current guidelines recommend initiating therapy (e.g., resmetirom or semaglutide for ≥F2 fibrosis and beta-blockers for portal hypertension). However, existing tools like FIB-4 often lack accuracy and usability in routine primary care.
FibroX addresses these limitations by using routinely available clinical data-such as age, liver enzymes, platelet count, BMI, and kidney function-to estimate the probability of significant fibrosis and portal hypertension. It provides a triage band (rule-out, indeterminate, rule-in) and a one-line explanation of which clinical factors most influenced the prediction. This transparency is achieved using Shapley Additive Explanations (SHAP), which helps clinicians understand how the AI reached its conclusion.
In retrospective studies, FibroX demonstrated superior diagnostic performance compared to FIB-4 (AUROC 0.97 vs. 0.62) and was associated with long-term mortality risk, suggesting prognostic value beyond diagnostic utility.
This pilot trial will simulate real-world primary care workflows to test whether FibroX can be effectively used by clinicians. The study will recruit 30-40 primary care providers (MDs, DOs, NPs, PAs) from 4-6 diverse clinics. Each provider will participate in two simulation periods, each involving 16 synthetic or de-identified patient cases reflecting adults with MASLD risk factors. Ground truth for fibrosis stage and portal hypertension will be determined by biopsy or expert consensus using Vibration-Controlled Transient Elastography (VCTE) and guideline-based criteria.
Providers will be randomly assigned to review cases in one of two sequences:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Licensed primary care providers (MD, DO, NP, or PA)
- •Currently practicing in adult primary care (≥0.5 Full-Time Equivalent)
- •Affiliated with one of the participating clinics (academic, community, or Federally Qualified Health Center)
- •Willing and able to participate in simulated electronic health record (EHR)-based case reviews
- •Able to provide informed consent
排除标准
- •Providers not actively practicing in adult primary care
- •Providers with less than 0.5 FTE in clinical practice
- •Prior involvement in the development or validation of the FibroX tool
- •Inability to complete both simulation periods due to scheduling or other constraints
结局指标
主要结局
Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care
时间窗: Immediately after each simulation period, up to 24 weeks
Within-provider diagnostic accuracy for detecting significant liver fibrosis (≥F2) and clinically significant portal hypertension in simulated primary care encounters. Accuracy will be assessed using sensitivity, specificity, and AUROC at clinically relevant thresholds. Ground truth for fibrosis stage and portal hypertension will be derived from biopsy, Vibration-Controlled Transient Elastography (VCTE)-based expert consensus, and guideline-defined criteria. Unit of Measure: Proportion (sensitivity and specificity in %, AUROC as a unitless value)
System Usability Scale (SUS) Score for FibroX Integration
时间窗: Immediately after each simulation period, up to 24 weeks
Usability of FibroX assessed using the System Usability Scale (SUS), a validated 10-item questionnaire scored from 0 to 100, where higher scores indicate better usability. Unit of Measure: Score (range: 0-100; higher scores = better usability)
Provider Trust in AI Tool (FibroX)
时间窗: Immediately after the FibroX-enabled simulation period, up to 24 weeks
Provider trust in FibroX assessed using the validated AI-Trust Scale, which includes 12 items scored on a Likert scale. Higher scores indicate greater trust in the AI tool. Unit of Measure: Score (range: 12-60; higher scores = greater trust)
Median Decision Time per Case
时间窗: Immediately after each simulation period, up to 24 weeks
Median time (in minutes) taken by providers to complete management decisions for simulated MASLD cases using FibroX versus usual care. Unit of Measure: Minutes
次要结局
- Appropriate Referral Rate(Immediately after each simulation period, up to 24 weeks)
- Net Reclassification Improvement (NRI)(Immediately after each simulation period, up to 24 weeks)
- Calibration of Risk Predictions(Immediately after each simulation period, up to 24 weeks)
- Provider Confidence in Decision-Making(Immediately after each simulation period, up to 24 weeks)
- Cognitive Load During Case Review(Immediately after each simulation period, up to 24 weeks)
- Intended Downstream Testing Burden(Immediately after each simulation period, up to 24 weeks)
- Adoption and Fidelity to Triage Recommendations(Immediately after each simulation period, up to 24 weeks)
- Override Rate and Reasons(Immediately after each simulation period, up to 24 weeks)
- Fairness Analysis Across Subgroups(Immediately after each simulation period, up to 24 weeks)
研究者
Basile Njei
Associate Director (Bioinformatics), Yale Liver Center
Yale University