脑卒中患者机械通气撤机影响因素的研究进展  

Progress in the Study of Factors Affecting Withdrawal of Mechanical Ventilation in Stroke Patients

作　　者：刘美颀 张雪辉 李媛青 刘安昊 颜冉冉 吴冬梅[2] LIU Meiqi;ZHANG Xuehui;LI Yuanqing;LIU Anhao;YAN Ranran;WU Dongmei(College of Nursing,Shandong First Medical University,Jinan 250021,China;不详)

机构地区：[1]山东第一医科大学(山东省医学科学院)护理学院,山东济南250021 [2]济宁医学院护理学院,山东济宁272067 [3]济宁医学院附属医院ICU,山东济宁272067

出　　处：《中国医学创新》2025年第6期179-184,共6页Medical Innovation of China

基　　金：济宁医学院2018年度教师科研扶持基金(JYFC2018KJ033)。

摘　　要：脑卒中是一种严重的脑血管疾病,具有高发病率和高死亡率,常导致患者依赖机械通气。撤机是脑卒中患者机械通气管理中的关键环节,其成功与否直接影响患者的预后和医疗资源的分配。本文综述了影响脑卒中患者撤机成功的因素及撤机预测模型的研究现状。研究发现,程序化撤机、护士主导撤机、卒中类型、部位、范围、超声评估、膈肌运动指标、早期肺康复训练等均对撤机成功有显著影响。同时,机器学习和人工智能技术在构建撤机预测模型方面展现出潜力,但需进一步的构建和验证。本文还讨论了撤机成功与否的定义,并强调了统一撤机成功标准的重要性。未来的研究需扩大样本量,开展多中心临床试验,探索个体化撤机策略,并利用机器学习算法优化撤机管理。Stroke is a serious cerebrovascular disease with high morbidity and mortality,which often leads to patients relying on mechanical ventilation.Withdrawal is a key link in mechanical ventilation management of stroke patients,and its success directly affects the prognosis of patients and the allocation of medical resources.This paper reviews the factors affecting the success of the withdrawal of stroke patients and the current research status of the withdrawal prediction model.It is found that programmed withdrawal,nurse-led withdrawal,stroke type,location,scope,ultrasonic assessment,diaphragm movement index,and early pulmonary rehabilitation training have significant effects on the success of withdrawal.At the same time,machine learning and artificial intelligence technologies show potential in building predictive models for downtime,but further construction and validation are needed.This paper also discusses the definition of successful withdrawal,and emphasizes the importance of unifying successful withdrawal criteria.Future studies need to expand the sample size,conduct multi-center clinical trials,explore individualized withdrawal strategies,and optimize withdrawal management using machine learning algorithms.

关 键 词：脑卒中 机械通气 撤机 预测模型 机器学习 

分 类 号：R743.3[医药卫生—神经病学与精神病学]