摘要
目的分析与提取颈动脉支架植入术后患者发生谵妄的危险因素,为针对性干预提供参考。方法统计350例颈动脉狭窄支架植入术后患者谵妄发生率,行单因素和多因素分析获得术后患者谵妄相关危险因素,基此构建列线图预测模型,采用校正曲线和ROC曲线评估其准确度和区分度。结果60例术后发生谵妄,发生率17.14%;高龄、术前NIHSS评分和术前焦虑是术后发生谵妄的独立危险因素(均P<0.05);由3项独立危险因素构建的谵妄风险列线图预测模型,预测曲线和观察曲线基本吻合,AUC=0.888。结论颈动脉支架植入术后患者谵妄发生率较高;高龄、术前焦虑及脑卒中倾向是术后患者发生谵妄的危险因素;构建的列线图预测模型具有较好的准确度和区分度,可提高筛选效能。
Objective To analyze the risk factors of postoperative delirium(PD)in patients undergoing carotid artery stenting(CAS)and to provide reference for targeted intervention.Methods The prevalence of PD in 350 patients undergoing CAS was calculated.Risk factors of PD were determined by using t-test,Mann-Whitney U-test,Chi-square test and multivariate logistic regression,then a nomogram was developed based on the regression-based coefficients.The calibration curve and ROC curve were performed to evaluate the accuracy and discrimination of the nomogram model.Results Sixty patients developed PD,with the prevalence of 17.14%.Multivariate logistic regression revealed that advanced age,preoperative National Institutes of Health Stroke Scale score and preoperative Self-rating Anxiety Scale score were risk factors for PD(P<0.05 for all).A nomogram based on these variables was established,and the calibration curves for the probability of PD showed optimal agreement between the probability as predicted by the nomogram and the actual probability.The model showed a robust discrimination,with an area under the ROC curve of 0.888.Conclusion The prevalence of PD is relatively high in patients undergoing CAS.Advanced age,preoperative anxiety and stroke tendency are risk factors for PD.The nomogram model has good accuracy and discrimination,and could improve the screening efficiency.
作者
田甜
景慧
荆莉
Tian Tian;Jing Hui;Jing Li(Department of The First Operating Room,Shengjing Hospital of China Medical University,Shenyang 110004,China)
出处
《护理学杂志》
CSCD
北大核心
2021年第12期26-30,共5页
Journal of Nursing Science
关键词
颈动脉狭窄
支架植入术
谵妄
焦虑
神经缺损程度
列线图
危险因素
预测模型
carotid artery stenosis
stent implantation
delirium
anxiety
neurologic deficit severity
nomogram
risk factor
predictive model