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Aims and Objectives: To explore the incidence of delirium in cerebrovascular patients admitted in our Neurosurgery Intensive Care Unit and analyse the risk factors leading to delirium., Background: Delirium is one of the most common mental disorders in general hospitals, but the incidence of different kinds of diseases and studies varies. Cerebrovascular patients in our Neurosurgery Intensive Care Unit are high-risk groups for delirium; identifying risk factors for delirium and taking early interventions are crucial for patient prognosis., Design: A prospective study., Methods: A convenience sampling method was used to collect data from 128 patients in the Neurosurgery Intensive Care Unit of Xuanwu Hospital, Capital Medical University, Beijing, China, between May 2016-January 2017. Researchers used Confusion Assessment Method for the Intensive Care Unit (Chinese version) to assess each patient's delirium statement twice a day at regular times. We also collected other independent data variables and followed up the short-term clinical outcomes daily., Results: On the basis of Confusion Assessment Method for the Intensive Care Unit evaluation, patients were divided into a delirium group and a nondelirium group. The prevalence of delirium among the 128 patients was 42.2%. Multivariate analysis showed that severity of illness, fever, the use of physical restraints and sleep deprivation were independent predictors of delirium in cerebrovascular patients in the Neurosurgery Intensive Care Unit., Conclusions: Cerebrovascular patients in the Neurosurgery Intensive Care Unit with a critical condition, fever or use of physical restraints or experiencing sleep deprivation were more prone to delirium., Relevance to Clinical Practice: Cerebrovascular patients in the Neurosurgery Intensive Care Unit showed a high incidence of delirium. There are many risk factors leading to delirium, some of which are independent predictors of intensive care delirium. Patients with delirium will suffer various adverse effects upon their short-term clinical outcomes. Therefore, nurses should pay close attention to changes in a patient's mental state and learn about the risk factors associated with delirium, in order to be able to take early measures to prevent delirium., (© 2017 John Wiley & Sons Ltd.)

Background: Neurosurgical intensive care unit patients are at high risk for delirium. A risk prediction model could help the staff screen for patients at high risk for delirium. On the basis of this risk, preventive measures could be taken to reduce the undesired effects of delirium. Objectives: To establish a delirium prediction model for neurosurgical intensive care unit patients and to verify the sensitivity and specificity of this model. Design A prospective, observational, single‐centre study. Methods: Data were collected from a total of 310 patients admitted to the neurosurgery intensive care unit between January 2017 and February 2018. A risk factor prediction model was then created using multivariate logistic regression. Further data were collected from another 60 patients between March 2018 and June 2018 to validate the model. Results: The model consisted of six predictors, namely, cognitive dysfunction on admission, fever, hypoalbuminaemia, abnormal liver function, sedative use four or more times, and physical restraint. The area under the curve of the model was 0.80, with sensitivity and specificity of 0.68 and 0.83, respectively. Conclusions: This study established a delirium prediction model for neurosurgical intensive care unit patients, which we believe would help focused prevention of delirium in intensive care unit patients. SUMMARY STATEMENT: What is already known about this topic? Delirium is an acute medical condition commonly seen in general hospitals.There are many reasons underpinning delirium in ICU patients, and early screening and prevention constitute the first steps of delirium management.There are a limited number of studies that have addressed delirium in neurosurgical ICU patients. What this paper adds? This study established and validated a delirium prediction model on the basis of the risk factors associated with neurosurgical ICU patients. The implications of this paper: This delirium prediction model for neurosurgical ICU patients allows medical staff to adopt early interventional measures for patients at high risk for developing delirium, thereby preventing and reducing the incidence of this condition. [ABSTRACT FROM AUTHOR]