Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding

Figure 1 Study design. A: Development and validation of the overt hepatic encephalopathy prediction model; B: Flowchart of patient enrollment and grouping. LR: Logistic regression; RFE: Recursive feature elimination; K-M: Kaplan-Meier; SHAP: SHapley Additive exPlanations; DCA: Decision curve analysis; AVB: Acute variceal bleeding; TIPS: Transjugular intrahepatic portosystemic shunt; ML: Machine learning.

Figure 2 Comparison of various machine learning models in a classification task and decision curve analysis for the logistic regression model. A: Receiver operating characteristic (ROC) curves for random forest, extreme gradient boosting, and logistic regression (LR); B: ROC curve for the LR model, presented individually with an area under the curve value of 0.825; C: Decision curve analysis for the LR model, illustrating the net benefit at various threshold probabilities in comparison to the “treat all” and “treat none” strategies. AUC: Area under the curve; XGB: Extreme gradient boosting; LR: Logistic regression.

Figure 3 Analysis of feature importance using SHapley Additive exPlanations values in the logistic regression model. A: Bar chart displaying the mean SHapley Additive exPlanations (SHAP) values, which indicate the average impact of each feature on the model output; B: Beeswarm plot showing the distribution of SHAP values for individual predictions; C: Summary plot combining SHAP values and feature importance, with feature values represented by color (blue for low and red for high). PVT: Portal vein thrombosis; CLIF-C: Chronic liver failure consortium; ALBI: Albumin-bilirubin; TBIL: Total bilirubin; PPG: Portal pressure gradient; EIS: Endoscopic injection sclerotherapy; CTPV: Cavernous transformation of the portal vein; PVP: Portal venous pressure; ALB: Albumin; SMV: Superior mesenteric vein thrombosis; CLIF-C AD: Chronic liver failure consortium acute decompensation; FIPS: Fibrosis-4 index for liver fibrosis; SHAP: SHapley Additive exPlanations.

Figure 4 Kaplan-Meier survival curves comparing different overt hepatic encephalopathy status groups. A: Survival analysis based on actual overt hepatic encephalopathy (OHE) status; B: Survival analysis based on predicted OHE status. CI: Confidence interval; HR Hazard ratio; OHE: Overt hepatic encephalopathy.

Figure 5 Multivariate Cox regression analyses of overall survival. A: Cox regression model established with preoperative data; B: Cox regression model established with preoperative data and actual overt hepatic encephalopathy (OHE) status; C: Cox regression model established with preoperative data and predicted OHE value. Na⁺: Sodion; AIC: Akaike information criterion; TBIL: Total bilirubin; OHE: Overt hepatic encephalopathy.

Figure 6 Calibration plot comparing the predicted risk to the actual risk for three different models: actual model (black line), traditional model (yellow line), and machine learning model (blue line). The performance metrics include the area under the curve and Brier score for each model. AUC: Area under the curve; ML: Machine learning.