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

ABSTRACT

BACKGROUND: Surgical resection remains the primary treatment for hepatic malignancies, and intraoperative bleeding is associated with a significantly increased risk of death. Therefore, accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment. AIM: To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes. METHODS: The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020. Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding. A prediction model was developed using Python programming language, and its accuracy was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Among 406 primary liver cancer patients, 16.0% (65/406) suffered massive intraoperative bleeding. Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients ascites [odds ratio (OR) 22.839; P < 0.05], history of alcohol consumption (OR 2.950; P < 0.015), TNM staging (OR 2.441; P < 0.001), and albumin-bilirubin score (OR 2.361; P < 0.001). These variables were used to construct the prediction model. The 406 patients were randomly assigned to a training set (70%) and a prediction set (30%). The area under the ROC curve values for the model's ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set. CONCLUSION: The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors ascites, history of alcohol consumption, TNM staging, and albumin-bilirubin score. Consequently, this model holds promise for enhancing individualised surgical planning.