Predicting postoperative delirium after microvascular decompression surgery with machine learning |
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| Institution: | 1. Division of Cardiac Surgery, Department of Surgery, Dalhousie University, Halifax, Nova Scotia, Canada;2. Division of Cardiac Surgery, Department of Cardiac Sciences, King Saud bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Jeddah, Saudi Arabia;2. Department of Anesthesiology, Perioperative, and Pain Medicine Icahn School of Medicine at Mount Sinai, New York, NY;3. Department of Anesthesia, Critical Care and Pain Medicine Beth Israel, Deaconess Medical Center, Boston, MA;4. Department of Anesthesiology and Critical Care, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA;5. Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, AL;11. Department of Anesthesiology, University of Iowa Hospitals and Clinics, Iowa City, IA;12. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN;2. Department of Anesthesiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China;1. Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China;2. Anesthesia and Big Data Research Group, Department of Scientific Research, Zhaoqing Medical College, China |
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| Abstract: | ObjectiveThe aim of this study was to predict early delirium after microvascular decompression using machine learning.DesignRetrospective cohort study.SettingSecond Hospital of Lanzhou University.PatientsThis study involved 912 patients with primary cranial nerve disease who had undergone microvascular decompression surgery between July 2007 and June 2018.InterventionsNone.MeasurementsWe 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.Results912 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).ConclusionMachine 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. |
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