Using prognostic and predictive clinical features to make personalised survival prediction in advanced hepatocellular carcinoma patients undergoing sorafenib treatment

Background: Sorafenib is the current standard of care for patients with advanced hepatocellular carcinoma (aHCC) and has been shown to improve survival by about 3 months compared to placebo. However, survival varies widely from under three months to over two years. The aim of this study was to build a statistical model that allows personalised survival prediction following sorafenib treatment. Methods: We had access to 1130 patients undergoing sorafenib treatment for aHCC as part of the control arm for two phase III randomised clinical trials (RCTs). A multivariable model was built that predicts survival based on baseline clinical features. The statistical approach permits both group-level risk stratification and individual-level survival prediction at any given time point. The model was calibrated, and its discrimination assessed through Harrell’s c-index and Royston-Sauerbrei’s R2D. Results: The variables influencing overall survival were vascular invasion, age, ECOG score, AFP, albumin, creatinine, AST, extra-hepatic spread and aetiology. The model-predicted survival very similar to that observed. The Harrell’s c-indices for training and validation sets were 0.72 and 0.70, respectively indicating good prediction. Conclusions: Our model (‘PROSASH’) predicts patient survival using baseline clinical features. However, it will require further validation in a routine clinical practice setting.