Li Yun, Zhao Lina, Yang Chenyi, Yu Zhiqiang, Song Jiannan, Zhou Qi, Zhang Xizhe, Gao Jie, Wang Qiang, Wang Haiyun
The Third Central Clinical College of Tianjin Medical University, Tianjin, China.
Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China.
Front Neurosci. 2021 Apr 16;15:644845. doi: 10.3389/fnins.2021.644845. eCollection 2021.
Sleep disorders, the serious challenges faced by the intensive care unit (ICU) patients are important issues that need urgent attention. Despite some efforts to reduce sleep disorders with common risk-factor controlling, unidentified risk factors remain.
This study aimed to develop and validate a risk prediction model for sleep disorders in ICU adults.
Data were retrieved from the MIMIC-III database. Matching analysis was used to match the patients with and without sleep disorders. A nomogram was developed based on the logistic regression, which was used to identify risk factors for sleep disorders. The calibration and discrimination of the nomogram were evaluated with the 1000 bootstrap resampling and receiver operating characteristic curve (ROC). Besides, the decision curve analysis (DCA) was applied to evaluate the clinical utility of the prediction model.
2,082 patients were included in the analysis, 80% of whom ( = 1,666) and the remaining 20% ( = 416) were divided into the training and validation sets. After the multivariate analysis, hemoglobin, diastolic blood pressure, respiratory rate, cardiovascular disease, and delirium were the independent risk predictors for sleep disorders. The nomogram showed high sensitivity and specificity of 75.6% and 72.9% in the ROC. The threshold probability of the net benefit was between 55% and 90% in the DCA.
The model showed high performance in predicting sleep disorders in ICU adults, the good clinical utility of which may be a useful tool for providing clinical decision support to improve sleep quality in the ICU.
睡眠障碍是重症监护病房（ICU）患者面临的严峻挑战，是亟待关注的重要问题。尽管通过控制常见风险因素在减少睡眠障碍方面做出了一些努力，但仍存在未识别的风险因素。
本研究旨在开发并验证一种针对ICU成年患者睡眠障碍的风险预测模型。
从MIMIC-III数据库中检索数据。采用匹配分析对有睡眠障碍和无睡眠障碍的患者进行匹配。基于逻辑回归开发列线图，用于识别睡眠障碍的风险因素。通过1000次自助重采样和受试者工作特征曲线（ROC）评估列线图的校准和辨别能力。此外，应用决策曲线分析（DCA）评估预测模型的临床实用性。
2082例患者纳入分析，其中80%（n = 1666）和其余20%（n = 416）分别分为训练集和验证集。多因素分析后，血红蛋白、舒张压、呼吸频率、心血管疾病和谵妄是睡眠障碍的独立风险预测因素。列线图在ROC中的敏感性和特异性分别为75.6%和72.9%。在DCA中，净效益的阈值概率在55%至90%之间。
该模型在预测ICU成年患者睡眠障碍方面表现出高性能，其良好的临床实用性可能是为改善ICU睡眠质量提供临床决策支持的有用工具。
