Development and Validation of a Clinical Prediction Model for Sleep Disorders in the ICU: A Retrospective Cohort Study

• Published in: Frontiers in neuroscience Vol. 15; p. 644845
• Main Authors: Li, Yun, Zhao, Lina, Yang, Chenyi, Yu, Zhiqiang, Song, Jiannan, Zhou, Qi, Zhang, Xizhe, Gao, Jie, Wang, Qiang, Wang, Haiyun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 16.04.2021; Frontiers Media S.A
• Subjects: Blood pressure; Cardiovascular diseases; Circadian rhythm; Cohort analysis; Comorbidity; Delirium; Disease; Hemoglobin; Hypoventilation; ICU; Illnesses; Insomnia; Intensive care; Laboratories; MIMIC-III database; Mortality; Movement disorders; Multivariate analysis; Neuroscience; Patients; Potassium; prediction model; Prediction models; Respiration; Restless legs syndrome; risk; Risk factors; Sleep; Sleep apnea; Sleep disorders; Statistical analysis; MIMIC-III database; sleep disorders; ICU; risk; prediction model
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2021.644845

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.