Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies

Figure 1 Flow chart of patient inclusion.

Figure 2 Predictive performance of the developed model. Model discrimination was evaluated based on area under the curve values, which were (A) 0.844 in the training set and (B) 0.803 in the prediction set. ROC: Receiver operating characteristics.

Figure 3 Receiver operating characteristics curves for the prediction model compared with the individual variables included in the model. ROC: Receiver operating characteristics; AUC: Area under the curve; ALBI: Albumin bilirubin.

Figure 4 Correlogram demonstrating the correlation between variables in the prediction model. ALBI: Albumin bilirubin.

Figure 5 Nomogram for the prediction of intraoperative bleeding in patients with primary hepatic malignancies based on significant variable identified by the logistic regression model. ALBI: Albumin bilirubin.

Figure 6 Calibration curves for the prediction of intraoperative bleeding in patients with primary hepatic malignancies. Nomogram-predicted intraoperative bleeding in patients with primary hepatic malignancies is plotted on the X-axis, and actual intraoperative bleeding in patients with primary hepatic malignancies is plotted on the Y-axis.

Figure 7 Decision and clinical impact curves for use of the nomogram to predict intraoperative bleeding in patients with primary hepatic malignancies. The model showed superior standardized net benefit over the individual factors included (ascites, alcohol consumption, TNM staging, and albumin bilirubin score). ALBI: Albumin bilirubin.