Clinical Prediction of HBV-associated Cirrhosis Using Machine Learning Based on Platelet and Bile Acids

Objectives: The present study was conducted to evaluate the performance of serum bile acids in the prediction of cirrhosis in chronic hepatitis B (CHB) population.

Methods: Dysregulated metabolites were explored using untargeted and targeted metabolomic analyses. A machine learning model based on platelet (PLT) and several bile acids was constructed using light gradient boosting machine (LightGBM), to differentiate HBV-associated cirrhosis (BAC) from CHB patients.

Results: Serum bile acids were dysregulated in BAC compared to CHB patients. The LightGBM model consisted of PLT, TUDCA, UDCA, TLCA, LCA and CA. The model demonstrated a strong discrimination ability in the internal test subset of the training cohort to diagnose BAC from CHB patients (AUC = 0.97). The high diagnostic accuracy of the model was further validated in an independent validation cohort. In addition, the model had high predictive efficacy in discriminating compensated BAC from CHB patients (AUC = 0.89). The performance of the model was better than AST/ALT ratio and the gradient boosting (GB)-based model reported in previous studies.

Conclusions: Our study showed that this LightGBM model based on PLT and 5 bile acids has potential in clinical assessments of CHB progression and will be useful for early detection of cirrhosis in CHB patients.