Zhang Yimei, Wang Yu, Yang Jingran, Li Qinglan, Zhou Min, Lu Jiafei, Hu Qiulan, Ma Fang
Department of Nursing, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, 650032, China.
ICU in Geriatric Department, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, 650032, China.
Eur J Med Res. 2025 Jul 24;30(1):666. doi: 10.1186/s40001-025-02930-8.
Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in critically ill patients, marked by symmetrical respiratory and limb muscle weakness, which adversely affects long-term outcomes. Early identification of high-risk patients and prevention are essential to mitigate its impact. Traditional risk prediction models, based on cohort data, have limitations in addressing the complex, non-linear relationships among diverse risk factors due to patient heterogeneity and the dynamic nature of critical illness. Machine learning offers a promising alternative by integrating heterogeneous data-clinical, laboratory, and physiological-to enhance predictive accuracy and individualization. Additionally, machine learning can identify novel risk factors and mechanisms overlooked by conventional methods, supporting early intervention and targeted prevention strategies to improve patient prognosis. Therefore, this study aims to develop and validate risk prediction models for ICUAW based on multiple machine learning algorithms.
Four machine learning algorithms were employed. Bedside ultrasound machines were used to assess ICUAW in patients admitted to the ICU twice, once within 24 hours of ICU admission and once on the 7th day of ICU admission. Eighteen features screened through a previous umbrella review informed the models. The performance of the models was evaluated based on multiple assessment metrics, such as the area under the receiver operating characteristic curve (AUC).
A total of 749 patients were enrolled in the study, and 382 patients (51%) developed ICUAW. Specifically, 524 patients were assigned to the training set, and 225 patients were assigned to the internal validation set. Among the four machine-learning models, AUC ranged from 0.830 to 0.978. The eXtreme Gradient Boosting exhibited the best performance, achieving an AUC of 0.978 (95%CI 0.962-0.994), with 0.924 accuracy, 0.911 sensitivity, 0.941 specificity, 0.924 F1 score, and a Brier score of 0.084. The results of the Decision Curve Analysis also corroborate these results.
A machine learning prediction model can be developed, leveraging its robust learning capabilities to identify patients at high risk of developing ICUAW. This approach facilitates standardized management of ICUAW, thereby potentially reducing its incidence.
重症监护病房（ICU）获得性肌无力（ICUAW）是危重症患者中常见的并发症，其特征为对称性呼吸肌无力和肢体肌无力，对长期预后产生不利影响。早期识别高危患者并进行预防对于减轻其影响至关重要。基于队列数据的传统风险预测模型，由于患者的异质性和危重症的动态性质，在处理各种风险因素之间复杂的非线性关系方面存在局限性。机器学习通过整合异构数据（临床、实验室和生理数据）提供了一种有前景的替代方法，以提高预测准确性和个性化程度。此外，机器学习可以识别传统方法忽略的新风险因素和机制，支持早期干预和针对性预防策略，以改善患者预后。因此，本研究旨在基于多种机器学习算法开发并验证ICUAW的风险预测模型。
采用四种机器学习算法。使用床边超声机器对入住ICU的患者进行两次ICUAW评估，一次在入住ICU后24小时内，一次在入住ICU的第7天。通过先前的综合综述筛选出的18个特征为模型提供信息。基于多个评估指标，如受试者操作特征曲线下面积（AUC），对模型的性能进行评估。
本研究共纳入749例患者，其中382例（51%）发生了ICUAW。具体而言，524例患者被分配到训练集，225例患者被分配到内部验证集。在四种机器学习模型中，AUC范围为0.830至0.978。极端梯度提升表现最佳，AUC为0.978（95%CI 0.962 - 0.994），准确率为0.924，灵敏度为0.911，特异度为0.941，F1评分为0.924，布里尔评分为0.084。决策曲线分析的结果也证实了这些结果。
可以开发一种机器学习预测模型，利用其强大的学习能力来识别发生ICUAW的高危患者。这种方法有助于ICUAW的标准化管理，从而有可能降低其发生率。
