基于随机森林算法建立非急诊大手术后延迟拔管的预测模型

doi:10.12089/jca.2024.01.002

科研成果详情

题名	基于随机森林算法建立非急诊大手术后延迟拔管的预测模型
其他题名	Predictive model for extubation delay undergoing non-emergency major surgery based on random forest algorithm
作者	李鹏1; 朱静文 2; 许开伟1; 张玉 1; 傅海峰1; 杜文文1
发表日期	2024-01-15
发表期刊	临床麻醉学杂志影响因子和分区
语种	中文
原始文献类型	学术期刊
关键词	随机森林大手术延迟拔管危险因素预测模型
其他关键词	Random forest ; Major surgery ; Extubation delay ; Risk factors ; Prediction model
摘要	目的基于随机森林算法分析非急诊大手术后延迟拔管的影响因素，建立并验证术后延迟拔管的预测模型。方法回顾性收集2018年1月至2022年12月全麻下行非急诊大手术的7 528例患者的临床资料。根据术后2 h内是否拔管，将患者分为两组：非延迟拔管组(≤2 h)和延迟拔管组(>2 h)。将患者按照7∶3分为训练集和验证集，通过LASSO回归、Logistic回归筛选术后延迟拔管的预测因素，采用随机森林算法建立并验证预测模型。结果有123例(1.6%)出现术后延迟拔管。ASA分级、科室、术中使用氟比洛芬酯、右美托咪定、激素、术中出现低钙血症、重度贫血、术中输血、气道痉挛是术后延迟拔管的独立预测因素。基于随机森林算法建立的预测模型在验证集中的曲线下面积(AUC)为0.751(95%CI 0.742～0.778),敏感性98.1%,特异性41.9%。结论基于随机森林算法建立的非急诊大手术后拔管延迟的预测模型具有较好的预测性能，利用该模型有助于预防非急诊大手术后延迟拔管。
其他摘要	Objective To construct and validate a clinical prediction model for delayed extubation undergoing non-emergency major surgery based on the random forest algorithm.Methods Clinical data of 7 528 patients undergoing non-emergency major surgery under general anesthesia from January 2018 to De-cember 2022 were retrospectively collected.The patients were divided into two groups according to whether extubation was performed within 2 hours after surgery:non-delayed extubation group(≤2 hours)and de-layed extubation group(＞2 hours).All the patients were randomly divided into a training set and a valida-tion set in a ratio of 7 ∶ 3.The predictive factors for delayed extubation after surgery were screened through LASSO regression and Logistic regression.The random forest model was established and verified by random forest algorithm.Results There were 123 patients(1.6%)experienced delayed extubation after surgery.ASA physical status,department,intraoperative use of flurbiprofen ester,dexmedetomidine,glucocorticoid,hypocalcemia,severe anemia,intraoperative blood transfusion,and airway spasm were identified as inde-pendent predictive factors for delayed extubation.The area under curve(AUC)value of the random forest prediction model in the validation set was0.751(95%CI0.742-0.778),and the sensitivity was98.1%,and the specificity was 41.9%.Conclusion The predictive model of delayed extubation undergoing non-e-mergency major surgery based on random forest algorithm has a good predictive value,which may be helpful to prevent delayed extubation undergoing non-emergency major surgery.
资助项目	浙江省中医药卫生科技项目(2023ZL086);
ISSN	1004-5805
卷号	40 期号:01 页码:7-12
DOI	10.12089/jca.2024.01.002
页数	6
收录类别	CNKI ; 万方 ; 维普 ; 北大核心 ; CSCD
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/208711
专题	附属第一医院附属第一医院_麻醉科
通讯作者	杜文文
作者单位	1.温州医科大学附属第一医院麻醉科; 2.浙江省人民医院麻醉科
第一作者单位	附属第一医院; 麻醉科
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	李鹏,朱静文,许开伟,等. 基于随机森林算法建立非急诊大手术后延迟拔管的预测模型[J]. 临床麻醉学杂志,2024,40(01):7-12.
APA	李鹏, 朱静文, 许开伟, 张玉, 傅海峰, & 杜文文. (2024). 基于随机森林算法建立非急诊大手术后延迟拔管的预测模型. 临床麻醉学杂志, 40(01), 7-12.
MLA	李鹏,et al."基于随机森林算法建立非急诊大手术后延迟拔管的预测模型".临床麻醉学杂志 40.01(2024):7-12.