Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension.

BACKGROUND

Hepatitis B-related cirrhosis represents a major contributor to liver-related events (LREs), with the development of clinically significant portal hypertension (CSPH) serving as a critical milestone in disease progression.

AIM: To establish predictive models based on multiple machine learning algorithms to improve the accuracy and clinical utility of LREs prediction.

METHODS

A total of 576 patients were retrospectively enrolled and randomly divided into training (n = 403) and validation (n = 173) cohorts. Features were selected through least absolute shrinkage and selection operator regression, random forest (RF), and support vector machine (SVM). Based on these features, five predictive models were constructed, including SVM, RF, logistic regression, extreme gradient boosting (XGBoost), and k-nearest neighbor. Model performance was evaluated using receiver operating characteristic and decision curve analysis, and feature importance and interactions were further explored using SHapley Additive exPlanations (SHAP).

RESULTS

Of the patients included, 313 (54.3%) developed LREs. Eight core predictive features were ultimately identified, with the liver stiffness measurement (LSM)-to-platelet ratio (LPR) contributing most significantly. The XGBoost and RF models demonstrated superior performance, achieving accuracies of 0.951 and areas under the curve of 0.975 and 0.965, respectively. SHAP analysis revealed that LPR, hemoglobin (HB), and LSM were key factors, with LPR exhibiting significant interactions with HB, international normalized ratio, and spleen thickness.

CONCLUSION

Machine learning-based prediction models, particularly XGBoost and RF, can effectively identify high-risk individuals among patients with compensated hepatitis B virus-related cirrhosis and CSPH. LPR that incorporates LSM is a valuable and robust predictive indicator.