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

doi:10.1111/cns.13991

	Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study
	Song, Yu-Xiang 1,2; Yang, Xiao-Dong 3; Luo, Yun-Gen 1,2; Ouyang, Chun-Lei 1; Yu, Yao 1; Ma, Yu-Long 1; Li, Hao 1; Lou, Jing-Sheng 1; Liu, Yan-Hong 1; Chen, Yi-Qiang 3; Cao, Jiang-Bei 1; Mi, Wei-Dong 1
	2022-10-11
发表期刊	CNS NEUROSCIENCE & THERAPEUTICS
ISSN	1755-5930
页码	10
摘要	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 better sensitivity, fewer variables, and easier interpretability than the machine learning model.
关键词	aged delirium machine learning nomograms risk assessment
DOI	10.1111/cns.13991
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC2001901]
WOS研究方向	Neurosciences & Neurology ; Pharmacology & Pharmacy
WOS类目	Neurosciences ; Pharmacology & Pharmacy
WOS记录号	WOS:000865651300001
出版者	WILEY
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19803
专题	中国科学院计算技术研究所期刊论文
通讯作者	Chen, Yi-Qiang; Cao, Jiang-Bei; Mi, Wei-Dong
作者单位	1.Chinese Peoples Liberat Army Gen Hosp, Dept Anesthesiol, Med Ctr 1, 28 Fuxing Rd, Beijing 100853, Peoples R China 2.Chinese Peoples Liberat Army, Med Sch, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Song, Yu-Xiang,Yang, Xiao-Dong,Luo, Yun-Gen,et al. Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study[J]. CNS NEUROSCIENCE & THERAPEUTICS,2022:10.
APA	Song, Yu-Xiang.,Yang, Xiao-Dong.,Luo, Yun-Gen.,Ouyang, Chun-Lei.,Yu, Yao.,...&Mi, Wei-Dong.(2022).Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.CNS NEUROSCIENCE & THERAPEUTICS,10.
MLA	Song, Yu-Xiang,et al."Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study".CNS NEUROSCIENCE & THERAPEUTICS (2022):10.