随机森林的集成分类算法对心胸外科ICU患者谵妄风险的预测分析  

Predictive analysis of delirium risk in ICU patients with cardiothoracic surgery by ensemble classification algorithm of random forest

作　　者：陈苗[1] 陈青[1] 尹晓清[1] CHEN Miao;CHEN Qing;YIN Xiaoqing(The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine,Changsha,410007,P.R.China)

机构地区：[1]湖南中医药大学第一附属医院,长沙410007

出　　处：《中国胸心血管外科临床杂志》2022年第7期886-891,共6页Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

摘　　要：目的分析随机森林的集成分类算法对心胸外科ICU患者谵妄风险的预测效能。方法回顾性分析2019年6月—2020年12月于湖南中医药大学第一附属医院心胸外科ICU治疗360例患者的临床资料,其中男193例、女167例,年龄18~80(56.45±9.33)岁。根据患者住院期间是否发生谵妄分为谵妄组和对照组。比较两组的临床资料,分别通过多因素logistic回归分析模型和随机森林的集成分类算法对影响心胸外科ICU患者发生谵妄风险的相关因素进行预测,并比较两者间预测效能的差异。结果纳入研究的患者中有19例脱落,剩余患者中有165例发生了ICU谵妄列为谵妄组,ICU谵妄发生率为48.39%;176例未发生ICU谵妄者列为对照组。两组性别、文化水平等一般资料差异无统计学意义(P>0.05);但相比于对照组,谵妄组年龄较大,住院时间长,急性生理学和慢性健康状况评分系统Ⅱ(acute physiology and chronic health evaluationⅡ,APACHEⅡ)得分、机械辅助通气所占比例、身体约束所占比例和使用镇静药物所占比例均较高(P<0.05)。多因素logistic回归分析显示:年龄(OR=1.162)、住院时间(OR=1.238)、APACHEⅡ得分(OR=1.057)、机械辅助通气(OR=1.329)、身体约束(OR=1.345)和使用镇静药物(OR=1.630)是心胸外科ICU患者发生谵妄风险的独立危险因素。对随机森林模型各变量的重要程度进行排序,排名在前的重要预测变量为:年龄、住院时间、APACHEⅡ得分、机械辅助通气、身体约束和使用镇静药物。随机森林的集成分类算法的诊断效能明显高于多因素logistic回归分析的诊断效能,其中随机森林的集成分类算法受试者工作特征曲线下面积为0.87,多因素logistic回归分析模型曲线下面积为0.79。结论随机森林的集成分类算法分析预测心胸外科ICU患者发生谵妄的诊断效能更高,可于临床推广应用,有助于早期识别和加强护理高危患者。Objective To analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery.Methods A total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed.There were 193 males and 167females,aged 18-80(56.45±9.33)years.The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not.The clinical data of the two groups were compared,and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively,and the difference of the prediction efficiency between the two groups was compared.Results Of the included patients,19 patients fell out,165 patients developed ICU delirium and were enrolled into the delirium group,with an incidence of 48.39%in ICU,and the remaining 176 patients without ICU delirium were enrolled into the control group.There was no statistical significance in gender,educational level,or other general data between the two groups(P>0.05).But compared with the control group,the patients of the delirium group were older,length of hospital stay was longer,and acute physiology and chronic health evaluationⅡ(APACHEⅡ)score,proportion of mechanical assisted ventilation,physical constraints,sedative drug use in the delirium group were higher(P<0.05).Multivariate logistic regression analysis showed that age(OR=1.162),length of hospital stay(OR=1.238),APACHEⅡscore(OR=1.057),mechanical ventilation(OR=1.329),physical constraints(OR=1.345)and sedative drug use(OR=1.630)were independent risk factors for delirium of cardiothoracic ICU patients.The variables in the random forest model for sorting,on top of important predictor variable were:age,length of hospital stay,APACHEⅡscore,mechanical ventilation,physical constraints and sedative d

关 键 词：随机森林 集成分类算法 谵妄 预测效能 人工智能 

分 类 号：R473.6[医药卫生—护理学]