Zhou Yue, Sun YuJian, Pan YuFan, Dai Yu, Xiao Yi, Yu YuFeng
College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Aust Crit Care. 2025 Jan;38(1):101066. doi: 10.1016/j.aucc.2024.05.003. Epub 2024 Jul 15.
Intensive care unit (ICU)-acquired weakness (ICU-AW) is a critical complication that significantly worsens patient prognosis. It is widely thought that risk prediction models can be harnessed to guide preventive interventions. While the number of ICU-AW risk prediction models is increasing, the quality and applicability of these models in clinical practice remain unclear.
The objective of this study was to systematically review published studies on risk prediction models for ICU-AW.
We searched electronic databases (PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang Database) from inception to October 2023 for studies on ICU-AW risk prediction models. Two independent researchers screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies.
A total of 2709 articles were identified. After screening, 25 articles were selected, encompassing 25 risk prediction models. The area under the curve for these models ranged from 0.681 to 0.926. Evaluation of bias risk indicated that all included models exhibited a high risk of bias, with three models demonstrating poor applicability. The top five predictors among these models were mechanical ventilation duration, age, Acute Physiology and Chronic Health Evaluation II score, blood lactate levels, and the length of ICU stay. The combined area under the curve of the ten validation models was 0.83 (95% confidence interval: 0.77-0.88), indicating a strong discriminative ability.
Overall, ICU-AW risk prediction models demonstrate promising discriminative ability. However, further optimisation is needed to address limitations, including data source heterogeneity, potential biases in study design, and the need for robust statistical validation. Future efforts should prioritise external validation of existing models or the development of high-quality predictive models with superior performance.
The protocol for this study is registered with the International Prospective Register of Systematic Reviews (registration number: CRD42023453187).
重症监护病房（ICU）获得性肌无力（ICU-AW）是一种严重的并发症，会显著恶化患者预后。人们普遍认为，可以利用风险预测模型来指导预防干预措施。虽然ICU-AW风险预测模型的数量在不断增加，但这些模型在临床实践中的质量和适用性仍不明确。
本研究的目的是系统评价已发表的关于ICU-AW风险预测模型的研究。
我们检索了电子数据库（PubMed、Web of Science、Cochrane图书馆、Embase、护理及相关健康文献累积索引（CINAHL）、中国知网（CNKI）、中国科技期刊数据库（VIP）和万方数据库），从建库至2023年10月，以查找关于ICU-AW风险预测模型的研究。两名独立研究人员筛选文献、提取数据，并评估纳入研究的偏倚风险和适用性。
共识别出2709篇文章。筛选后，选择了25篇文章，涵盖25个风险预测模型。这些模型的曲线下面积范围为0.681至0.926。偏倚风险评估表明，所有纳入模型均表现出较高的偏倚风险，其中三个模型的适用性较差。这些模型中排名前五的预测因素是机械通气时间、年龄、急性生理与慢性健康状况评分系统II评分、血乳酸水平和ICU住院时间。十个验证模型的合并曲线下面积为0.83（9５％置信区间：0.77-0.8８），表明具有较强的判别能力。
总体而言，ICU-AW风险预测模型显示出有前景的判别能力。然而，需要进一步优化以解决局限性，包括数据源异质性、研究设计中的潜在偏倚以及对可靠统计验证的需求。未来的工作应优先对现有模型进行外部验证，或开发具有卓越性能的高质量预测模型。
本研究方案已在国际前瞻性系统评价注册库注册（注册号：CRD42023453187）。
