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

Yun Li,Qi Zhou,Lina Zhao,Jie Gao,Haiyun Wang,Zhiqiang Yu,Xizhe Zhang,Jiannan Song,Qiang Wang,Chenyi Yang

doi:10.3389/fnins.2021.644845

Abstract

BackgroundSleep 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.ObjectivesThis study aimed to develop and validate a risk prediction model for sleep disorders in ICU adults.MethodsData 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.Results2,082 patients were included in the analysis, 80% of whom (n = 1,666) and the remaining 20% (n = 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.ConclusionThe 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.

Highlights

Sleep is an important part of maintaining the normal physiological and psychological state of human beings (Irwin, 2015)
Of the 42,425 patients retrieved from the MIMIC-III database, 2,643 patients were diagnosed with sleep disorders, and
Our results show that the combined set of these seven predictors can be effectively used for the clinical prediction model of sleep disorders in intensive care unit (ICU) patients

Summary

Introduction

Sleep is an important part of maintaining the normal physiological and psychological state of human beings (Irwin, 2015). Sleep disorders are common and severe in the intensive care unit (ICU) patients, with a prevalence of more than 50% (Bani Younis and Hayajneh, 2018), and even up to approximately 80% in severe cases (Little et al, 2012). ICU patients have the feature of severity and complexity, coupled with special monitoring and intensive treatment, which make their sleep problems more likely to be ignored (Devlin et al, 2018). The mechanisms leading to sleep disorders in ICU patients are complex and multivariate, with relevance to the environmental, pharmaceutical and medical factors, as well as their interactions (Kamdar et al, 2012). 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

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Discussion

Conclusion