Predicting postoperative delirium after microvascular decompression surgery with machine learning

The aim of this study was to predict early delirium after microvascular decompression using machine learning. Retrospective cohort study. Second Hospital of Lanzhou University. This study involved 912 patients with primary cranial nerve disease who had undergone microvascular decompression surgery between July 2007 and June 2018. None. We collected data on preoperative, intraoperative, and postoperative variables. Statistical analysis was conducted in R, and the model was constructed with python. The machine learning model was run using the following models: decision tree, logistic regression, random forest, gbm, and GBDT models. 912 patients were enrolled in this study, 221 of which (24.2%) had postoperative delirium. The machine learning Gbm algorithm finds that the first five factors accounting for the weight of postoperative delirium are CBZ use duration, hgb, serum CBZ level measured 24 h before surgery, preoperative CBZ dose, and BUN. Through machine learning five algorithms to build prediction models, we found the following values for the training group: Logistic algorithm (AUC value = 0.925, accuracy = 0.900); Forest algorithm (AUC value = 0.994, accuracy = 0.948); GradientBoosting algorithm (AUC value = 0.994, accuracy = 0.970) and DecisionTree algorithm (aucvalue = 0.902, accuracy = 0.861); Gbm algorithm (AUC value = 0.979, accuracy = 0.944). The test group had the following values: Logistic algorithm (aucvalue = 0.920, accuracy = 0.901); DecisionTree algorithm (aucvalue = 0.888, accuracy = 0.883); Forest algorithm (aucvalue = 0.963, accuracy = 0.909); GradientBoostingc algorithm (aucvalue = 0.962, accuracy = 0.923); Gbm algorithm (AUC value = 0.956, accuracy = 0.920). Machine learning algorithms predict the occurrence of delirium after microvascular decompression with an accuracy rate of 96.7%. And the major risk factors for the development of post-cardiac delirium are carbamazepine, hgb, and BUN.