Hepatocellular carcinoma (HCC) remains a major cause of morbidity and mortality among patients with chronic hepatitis B (CHB). Existing HCC risk scores are largely based on baseline variables and do not account for longitudinal changes over time. We aimed to develop and validate a longitudinal predictive model for HCC using time-dependent Cox regression. We retrospectively analyzed 4,431 patients with CHB followed between 2009 and 2022. After excluding patients with viral co-infections, early HCC, or incomplete data, 2,245 patients were included. The cohort was randomly divided into a training set (n = 1,685) and a test set (n = 562). Time-updated demographic, clinical, and laboratory variables were incorporated into the model. During follow-up, 121 patients developed HCC. Independent predictors of HCC included age, sex, platelet count, cirrhosis, serum albumin, and α-fetoprotein (AFP). These variables were incorporated into the final model (PSU HCC score): (0.023 × age) − (0.627 × female sex) − (0.847 × albumin) − (0.005 × platelet count × 103) + (1.052 × log₁₀AFP) + (1.817 × cirrhosis). In the test cohort, the PSU HCC score demonstrated excellent discrimination (C-index 0.909; 95% CI, 0.870–0.947) and good calibration, outperforming ALBI, aMAP, REACH-B, CU-HCC, PAGE-B, and mPAGE-B scores. Risk stratification revealed significantly higher cumulative HCC incidence among patients in the high-risk group. Sensitivity analyses evaluating longer-term prediction at 3 and 5 years confirmed stable and robust performance. The PSU HCC score is a longitudinal prediction model that integrates dynamic clinical and laboratory parameters and provides superior HCC risk prediction in patients with CHB. This approach may support individualized, risk-based HCC surveillance strategies.
Kaewdech et al. (Tue,) studied this question.
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