Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study

作者：Yuxiang Song, Xiaodong Yang, Yungen Luo, Chun-lei Ou-yang, Yao Yu, Yulong Ma, Hao Li, Jingsheng Lou, Yanhong Liu, Yi‐qiang Chen, Jiangbei Cao, Weidong Mi · 发表于：CNS Neuroscience & Therapeutics · 年份：2022 · DOI：10.1111/cns.13991 · 被引用次数：65 · 研究领域：Intensive Care Unit Cognitive Disorders、Enhanced Recovery After Surgery、Cardiac, Anesthesia and Surgical Outcomes

AIMS: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. METHOD: This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision. RESULTS: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. CONCLUSIONS: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its bet...