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Updated: Jun 9, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Validation of Risk Models for Predicting Post-SVR HCC in Real-World Surveillance Across Global Geographic Regions
Hidenori Toyoda1, Yujin Hoshida2, Neehar D Parikh3
1Department of Gastroenterology, Ogaki Municipal Hospital, Ogaki, Japan.
Summary
Hepatocellular carcinoma (HCC) risk models show variable performance across global regions in patients with chronic hepatitis C virus (HCV) after sustained virologic response (SVR). No single model is universally applicable, necessitating external validation for regional accuracy in HCC risk stratification.
Area of Science:
- Hepatology
- Clinical Epidemiology
- Biostatistics
Background:
- Hepatocellular carcinoma (HCC) risk stratification is crucial for patients with chronic hepatitis C virus (HCV) achieving sustained virologic response (SVR).
- Existing clinical risk models require validation in diverse global populations beyond monocentric or country-specific cohorts.
- Regional variations in model performance for HCC risk stratification in post-SVR patients are not well characterized.
Purpose of the Study:
- To assess the performance of established HCC clinical risk models in a global cohort of post-SVR patients.
- To investigate regional variations in the accuracy of HCC risk stratification models.
- To determine if current models can be universally applied for HCC risk assessment.
Main Methods:
- Analysis of four HCC clinical risk models (aMAP, FIB-4, GES, THRI) in six real-world cohorts comprising 8796 post-SVR patients.
- Assessment of model discrimination using Harrel's c-statistic index.
- Comparison of HCC incidence rates across risk groups (low, intermediate, high) stratified by each model.
Main Results:
- Significant variation in patient characteristics and HCC incidence rates was observed across different geographic regions.
- While model performance was comparable within individual cohorts, the best-performing model differed by region.
- Overall model performance was lower than reported in original studies, with c-statistics frequently below 0.70 across most regions.
Conclusions:
- Current clinical models require improvement in discrimination and calibration for accurate HCC risk stratification in post-SVR patients.
- Geographic region significantly impacts the accuracy of HCC risk models, highlighting the need for external validation.
- No single HCC risk model demonstrates universal applicability, emphasizing the importance of assessing model transportability in diverse populations.
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