Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations

doi:10.1038/s41398-024-02762-w

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	Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations
	Song, Yuxiang 1; Zhang, Di 1; Wang, Qian 1; Liu, Yuqing 1; Chen, Kunsha 1; Sun, Jingjia 1; Shi, Likai 1; Li, Baowei 1; Yang, Xiaodong 2; Mi, Weidong 1,3; Cao, Jiangbei 1,3
	2024-01-25
发表期刊	TRANSLATIONAL PSYCHIATRY
ISSN	2158-3188
卷号	14 期号:1 页码:8
摘要	Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (>= 65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.
DOI	10.1038/s41398-024-02762-w
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation (No. L222100)
WOS研究方向	Psychiatry
WOS类目	Psychiatry
WOS记录号	WOS:001148269700002
出版者	SPRINGERNATURE
引用统计	被引频次：2[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/2XEOYT63/38390
专题	中国科学院计算技术研究所
通讯作者	Mi, Weidong; Cao, Jiangbei
作者单位	1.Peoples Liberat Army Gen Hosp, Dept Anesthesiol, Med Ctr 1, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Natl Clin Res Ctr Geriatr Dis, Gen Hosp Peoples Liberat Army, Beijing 100853, Peoples R China
推荐引用方式 GB/T 7714	Song, Yuxiang,Zhang, Di,Wang, Qian,et al. Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations[J]. TRANSLATIONAL PSYCHIATRY,2024,14(1):8.
APA	Song, Yuxiang.,Zhang, Di.,Wang, Qian.,Liu, Yuqing.,Chen, Kunsha.,...&Cao, Jiangbei.(2024).Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations.TRANSLATIONAL PSYCHIATRY,14(1),8.
MLA	Song, Yuxiang,et al."Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations".TRANSLATIONAL PSYCHIATRY 14.1(2024):8.