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

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.

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.

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.

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.