Risk factors of major bleeding detected by machine learning method in patients undergoing liver resection with controlled low central venous pressure technique

Abstract

Background: Controlled low central venous pressure (CLCVP) technique has been extensively validated in clinical practices to decrease intraoperative bleeding during liver resection process; however, no studies to date have attempted to propose a scoring method to better understand what risk factors might still be responsible for bleeding when CLCVP technique was implemented.

Methods: We aimed to use machine learning to develop a model for detecting the risk factors of major bleeding in patients who underwent liver resection using CLCVP technique. We reviewed the medical records of 1077 patients who underwent liver surgery between January 2017 and June 2020. We evaluated the XGBoost model and logistic regression model using stratified K-fold cross-validation (K = 5), and the area under the receiver operating characteristic curve, the recall rate, precision rate, and accuracy score were calculated and compared. The SHapley Additive exPlanations was employed to identify the most influencing factors and their contribution to the prediction.

Results: The XGBoost classifier with an accuracy of 0.80 and precision of 0.89 outperformed the logistic regression model with an accuracy of 0.76 and precision of 0.79. According to the SHapley Additive exPlanations summary plot, the top six variables ranked from most to least important included intraoperative hematocrit, surgery duration, intraoperative lactate, preoperative hemoglobin, preoperative aspartate transaminase, and Pringle maneuver duration.

Conclusions: Anesthesiologists should be aware of the potential impact of increased Pringle maneuver duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique. What is already known on this topic-Low central venous pressure technique has already been extensively validated in clinical practices, with no prediction model for major bleeding. What this study adds-The XGBoost classifier outperformed logistic regression model for the prediction of major bleeding during liver resection with low central venous pressure technique. How this study might affect research, practice, or policy-anesthesiologists should be aware of the potential impact of increased PM duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique.

Keywords: Pringle maneuver; XGBoost; liver resection; machine learning; major bleeding.