Validation of Risk Models for Predicting Post‐SVR HCC in Real‐World Surveillance Across Global Geographic Regions

BACKGROUND & AIMS: Several clinical risk models have been proposed to stratify hepatocellular carcinoma (HCC) risk in patients with chronic hepatitis C virus (HCV) after sustained virologic response (SVR). However, validation efforts have focused on monocentric or country-specific cohorts, and it is unclear if clinical risk models can be broadly applied to global populations. We characterised regional variation in model performance for HCC risk stratification in post-SVR patients. METHODS: Four HCC clinical risk models (aMAP score, FIB-4 index, GES score, and Toronto HCC risk index [THRI]) were analysed in six real-world cohorts, which included 8796 post-SVR patients from different geographic regions globally. Model discrimination was assessed using Harrel's c-statistic index. HCC incidence rates were compared across low-, intermediate-, and high-risk groups for each model. RESULTS: Distributions of patient characteristics and HCC incidence rates varied across geographic regions. Predictive performances of models were comparable within each cohort despite the model with the highest c-statistics differing by regions. Performance was lower than those from original reports overall; c-statistics of models across most regions remained below 0.70. CONCLUSIONS: There remains a continued need to improve discrimination and calibration of clinical models to stratify HCC risk in post-SVR patients. Accuracy of models may differ by geographic region, underscoring the importance of external validation to assess transportability of models and suggesting no single model can be universally applied.