Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations
文献类型：期刊论文
| 作者 | Song, Yuxiang3; Zhang, Di3; Wang, Qian3; Liu, Yuqing3; Chen, Kunsha3; Sun, Jingjia3; Shi, Likai3; Li, Baowei3; Yang, Xiaodong2; Mi, Weidong1,3 |
| 刊名 | TRANSLATIONAL PSYCHIATRY
 |
| 出版日期 | 2024-01-25 |
| 卷号 | 14期号:1页码:8 |
| ISSN号 | 2158-3188 |
| DOI | 10.1038/s41398-024-02762-w |
| 英文摘要 | 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. |
| 资助项目 | Beijing Natural Science Foundation (No. L222100) |
| WOS研究方向 | Psychiatry |
| 语种 | 英语 |
| WOS记录号 | WOS:001148269700002 |
| 出版者 | SPRINGERNATURE |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38389]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Mi, Weidong; Cao, Jiangbei |
| 作者单位 | 1.Natl Clin Res Ctr Geriatr Dis, Gen Hosp Peoples Liberat Army, Beijing 100853, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Peoples Liberat Army Gen Hosp, Dept Anesthesiol, Med Ctr 1, Beijing, 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. |
入库方式： OAI收割
来源：计算技术研究所
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