目的 系统评价机械通气患者衰弱风险预测模型。方法 检索PubMed、Web of Science、Embase、Cochrane Library、知网、万方和维普数据库,搜集关于机械通气患者衰弱风险预测模型的研究,检索时限为建库至2023年12月。由2名研究者独立筛选文献、提取资料并评价纳入文献的偏倚风险和适用性。结果 共纳入16篇文献。纳入模型的受试者工作特征曲线下面积为0.710~0.926。偏倚风险评估显示模型均存在高偏倚风险,适用性较好,出现频次前5个预测因子依次为机械通气时间、年龄、急性生理与慢性健康评估II评分、血乳酸水平和多器官功能障碍。6个验证模型的合并曲线下面积（Area Under Curve, AUC）值为0.800（95%CI：0.740-0.850）,表明具有良好的区分度。结论 机械通气患者衰弱风险预测模型整体预测性能较好,但在数据来源、构建设计和统计分析方面有待进一步优化。未来应对现有模型开展外部验证或开发性能优良的高质量预测模型。
Abstract
Objective To conduct a systematic evaluation of prediction models for the risk of acquired weakness in mechanically ventilated patients. Methods We retrieved literature on prediction models for the risk of acquired weakness in mechanically ventilated patients in PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang, and VIP. The retrieval period spanned from the inception of the databases to December 2023. Two researchers independently screened the literature, extracted data, and assessed the bias risk and applicability of the included studies. Results Sixteen articles were included. The area under the receiver operating characteristic curve for the included models ranged from 0.710 to 0.926. Bias risk assessment showed high bias risk in all models, but good applicability. The top 5 predictive factors in terms of frequency were mechanical ventilation time, age, Acute Physiology and Chronic Health Evaluation II score, blood lactate level, and multi-organ dysfunction. The combined AUC value of the six validation models was 0.800 (95%CI: 0.740-0.850), indicating good discrimination. Conclusion Prediction models for the risk of acquired weakness in mechanically ventilated patients demonstrate overall good predictive performance, but further optimization is needed in terms of data sources, design, and statistical analysis. Future efforts should focus on external validation of existing models or the development of high-quality predictive models.
关键词
机械通气 /
获得性衰弱 /
重症监护室 /
风险预测模型 /
系统评价
Key words
mechanical ventilation /
acquired weakness /
intensive care unit /
risk prediction model /
systematic review
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