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作 者:胡菲菲 王芳 王永妮 黄诗倪 明瑶 Hu Feifei;Wang Fang;Wang Yongni;Huang Shini;Ming Yao(School of Nursing,Chengdu University of Traditional Chinese Medicine,Chengdu 610075,China;Guang'an Hospital of Traditional Chinese Medicine,Guang'an 638001,China)
机构地区:[1]成都中医药大学护理学院,四川成都610075 [2]广安市中医院,四川广安638001
出 处:《临床荟萃》2025年第2期107-116,共10页Clinical Focus
摘 要:目的系统评价机械通气脱机失败风险预测模型。方法计算机检索PubMed、Embase、Web of Science、Cochrane Library、CBM、CNKI、万方、维普数据库中有关机械通气脱机失败的风险预测模型,检索时限为建库至2025年2月。由2名研究员独立筛选文献和提取资料,采用PROBAST偏倚风险评估工具对纳入文献进行偏倚风险和适用性评价。结果共纳入21篇文献,包含44个机械通气脱机失败风险预测模型。预测因子数量在3~21个之间,最常见的预测因子为机械通气时间、年龄、急性生理与慢性健康评分系统(acute physiology and chronic health evaluation,APACHEⅡ评分)。纳入模型AUC范围为0.689~0.926。模型总体预测性能较好,但整体偏倚风险高。结论目前机械通气脱机失败风险预测模型整体预测性能较好,但由于多数研究未进行外部验证且结局指标的定义存在差异,因此模型的临床适用性有待进一步验证。Objective To systematically evaluate the risk prediction models for mechanical ventilation weaning failure.Methods Computerised searches of PubMed,Embase,Web of Science,Cochrane Library,CBM,CNKI,Wanfang and VIP databases were conducted to collect risk prediction models for weaning failure from mechanical ventilation from the establishment of the database to February 2025.The risk of bias and applicability of risk prediction models were evaluated using the prediction model risk of bias assessment tool(PROBAST)after the literatures were independently screened and data extracted by two researchers.Results Twenty-one literatures representing 44 risk prediction models for weaning failure from mechanical ventilation were included.The number of predictors ranged from 3-21,with the most common predictors being the duration of mechanical ventilation,age,and the acute physiology and chronic health evaluationⅡ(APACHEⅡ).The area under the receiver operating characteristic curve(AUC)of the included models ranged 0.689-0.926.The models had a good overall predictive performance,but the overall risk of bias was high.Conclusion Currently,the risk prediction models for weaning failure from mechanical ventilation has well overall predictive performance,but the clinical applicability of the model requires further validation because most studies have not been externally validated and there are variations in the definitions of outcome metrics.
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