重症机械通气患者脱机失败预测模型的系统评价  

Prediction models for extubation failure in critically ill patients undergoing mechanical ventilation:a systematic review

作　　者：郭亚如 纪涵 王姿璎 乔建红[2,4] Guo Yaru;Ji Han;Wang Ziying;Qiao Jianhong(School of Nursing,Shandong First Medical University(Shandong Academy of Medical Sciences),Jinan 250000,China;School of Nursing and Rehabilitation,Shandong University,Jinan 250014,China;School of Nursing,Shandong Second Medical University,Weifang 261000,China;Outpatient Department,the First Affiliated Hospital of Shandong First Medical University(Shandong Provincial Qianfoshan Hospital),Jinan 250014,China)

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,济南250000 [2]山东大学护理与康复学院,济南250014 [3]山东第二医科大学护理学院,潍坊261000 [4]山东第一医科大学第一附属医院(山东省千佛山医院)门诊部,济南250014

出　　处：《中华现代护理杂志》2025年第6期797-802,共6页Chinese Journal of Modern Nursing

摘　　要：目的系统评价重症机械通气患者脱机失败预测模型,为医护人员选择合适的预测模型筛选高危人群提供参考。方法检索中国知网、万方数据库、维普网、中国生物医学文献服务系统、PubMed、Web of Science、Embase、Cochrane Library中有关重症机械通气患者脱机失败风险预测模型构建的文献,检索时限均为建库至2024年2月,由2名研究者独立筛选文献、提取数据,并采用偏倚风险评估工具评价预测模型的偏倚风险和适用性。结果共纳入9项研究,最常见的预测因子是机械通气时间、格拉斯哥昏迷量表评分、咳嗽反射强度、年龄、24 h出入量。模型的受试者工作特征曲线下面积为0.689~0.926,预测性能较好,但偏倚风险高,主要是由于样本量不足、基于单因素分析筛选预测因子、未进行正确的内部验证。结论现有预测模型的预测性能较好,但偏倚风险高。未来应完善研究设计并遵循模型开发与报告规范,构建性能良好、便于使用的预测模型以更加准确识别脱机失败的高危人群。ObjectiveTo systematically review the prediction models for extubation failure in critically ill patients undergoing mechanical ventilation,providing a reference for healthcare professionals in selecting appropriate models to identify high-risk populations.MethodsLiterature on the construction of prediction models for extubation failure risk in critically ill patients undergoing mechanical ventilation was retrieved from China National Knowledge Infrastructure,Wanfang Database,VIP,SinoMed,PubMed,Web of Science,Embase,and Cochrane Library.The search was limited from database inception to February 2024.Two researchers independently screened the literature and extracted data,using bias risk assessment tools to evaluate the bias risk and applicability of the prediction models.ResultsA total of nine studies were included,with the most common predictive factors being mechanical ventilation duration,Glasgow Coma Scale score,cough reflex strength,age,and 24-hour input/output volume.The area under the receiver operating characteristic curve for the models ranged from 0.689 to 0.926,indicating good predictive performance.However,the risk of bias was high,mainly due to small sample sizes,the selection of predictive factors based on univariate analysis,and lack of proper internal validation.ConclusionsExisting prediction models show good predictive performance,but they carry high bias risk.Future studies should improve research design,adhere to model development and reporting guidelines,and develop well-performing,user-friendly prediction models to more accurately identify high-risk populations for extubation failure.

关 键 词：机械通气 脱机失败 预测模型 系统评价 

分 类 号：R47[医药卫生—护理学]