A clinical scoring tool validated with machine learning for predicting severe hand–foot syndrome from sorafenib in hepatocellular carcinoma

Abstract

Purpose: Sorafenib is an effective therapy for advanced hepatocellular carcinoma (HCC). Hand–foot syndrome (HFS) is a serious adverse effect associated with sorafenib therapy. This study aimed to develop an updated clinical prediction tool that allows personalized prediction of HFS following sorafenib initiation. Methods: Individual participant data from Phase III clinical trial NCT00699374 were used in Cox proportional hazard analysis of the association between pre-treatment clinicopathological data and grade ≥ 3 HFS occurring within the first 365 days of sorafenib treatment for advanced HCC. Multivariable prediction models were developed using stepwise forward inclusion and backward deletion and internally validated using a random forest machine learning approach. Results: Of 542 patients, 116 (21%) experienced grades ≥ 3 HFS. The prediction tool was optimally defined by sex (male vs female), haemoglobin (< 130 vs ≥ 130 g/L) and bilirubin (< 10 vs 10–20 vs ≥ 20 µmol/L). The prediction tool was able to discriminate subgroups with significantly different risks of grade ≥ 3 HFS (P ≤ 0.001). The high (score = 3 +)-, intermediate (score = 2)- and low (score = 0–1)-risk subgroups had 40%, 27% and 14% probability of developing grade ≥ 3 HFS within the first 365 days of sorafenib treatment, respectively. Conclusion: A clinical prediction tool defined by female sex, high haemoglobin and low bilirubin had high discrimination for predicting HFS risk. The tool may enable improved evaluation of personalized risks of HFS for patients with advanced HCC initiating sorafenib.