背景:术后谵妄,尤其是在老年患者腹部癌症手术后,在临床管理中提出了重大挑战。
目的:建立一种基于合成少数过采样技术(SMOTE)的老年腹部肿瘤患者术后谵妄预测模型。
方法:在这项回顾性队列研究中,我们分析了2020年9月至2022年10月在我院接受腹部恶性肿瘤手术的611例老年患者的数据.术后7d记录术后谵妄发生率。根据术后是否发生谵妄分为谵妄组和非谵妄组。使用多变量逻辑回归模型来识别危险因素并建立术后谵妄的预测模型。应用SMOTE技术通过过度采样谵妄病例来增强模型。然后验证了模型的预测准确性。
结果:在我们的研究中,包括611例老年腹部恶性肿瘤患者,多因素logistic回归分析确定了术后谵妄的重要危险因素。其中包括Charlson合并症指数,美国麻醉医师学会分类,脑血管病史,手术时间,围手术期输血,术后疼痛评分。术后谵妄发生率为22.91%。原始预测模型(P1)显示出受试者工作特性曲线下的面积为0.862。相比之下,基于SMOTE的逻辑预警模型(P2),它利用了SMOTE过采样算法,显示0.856的曲线下面积略低但相当,表明两种预测方法之间的性能没有显着差异。
结论:这项研究证实,SMOTE增强的老年腹部肿瘤患者术后谵妄预测模型表现出与传统方法相当的性能,有效解决数据不平衡问题。
BACKGROUND: Postoperative delirium, particularly prevalent in elderly patients after abdominal cancer surgery, presents significant challenges in clinical management.
OBJECTIVE: To develop a synthetic minority oversampling technique (SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.
METHODS: In this retrospective cohort study, we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022. The incidence of postoperative delirium was recorded for 7 d post-surgery. Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not. A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium. The SMOTE technique was applied to enhance the model by oversampling the delirium cases. The model\'s predictive accuracy was then validated.
RESULTS: In our study involving 611 elderly patients with abdominal malignant tumors, multivariate logistic regression analysis identified significant risk factors for postoperative delirium. These included the Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular disease, surgical duration, perioperative blood transfusion, and postoperative pain score. The incidence rate of postoperative delirium in our study was 22.91%. The original predictive model (P1) exhibited an area under the receiver operating characteristic curve of 0.862. In comparison, the SMOTE-based logistic early warning model (P2), which utilized the SMOTE oversampling algorithm, showed a slightly lower but comparable area under the curve of 0.856, suggesting no significant difference in performance between the two predictive approaches.
CONCLUSIONS: This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods, effectively addressing data imbalance.