Establishment and validation of a delirium prediction model for neurosurgery patients in intensive care

Abstract

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.

The risk factors of postoperative delirium in general anesthesia patients with hip fracture

“…BMI is often used as a measure of body fat level and health index. [ 18 ] In recent years, studies [ 29 , 38 , 39 ] have shown that BMI may be a risk factor for postoperative delirium. Among the elderly patients who need long-term care of their families, those who are lean and have low body weight are more likely to have the attack of delirium.…”

Delirium in neurosurgery: a systematic review and meta-analysis

“…Two RCTs, seven prospective and eleven retrospective cohort studies were included. Disease type for patients undergoing intracranial surgery were categorized in mixed (33.9%, n p = 1478), [4,10,21,32,39,[41][42][43][44][45][46][47][48] functional neurosurgery (26.8%, n p = 552), [11,46,[49][50][51] neurovascular (10.5%, n p = 145), [52][53][54] neuro-oncology (18.4%, n p = 1969) [5,7,55], traumatic brain injury (TBI) (4.3%, n p = 27) [56,57] and microvascular decompression (MVD) (6.2%, n p = 912) [9]. The mixed group included neurovascular, neuro-oncologic, TBI or hydrocephalus operations and functional neurosurgery (solely deep brain stimulation (DBS) in patients with Parkinson's disease).…”

Development of an Early Prediction Model for Postoperative Delirium in Neurosurgical Patients Admitted to the ICU after Elective Craniotomy (E-PREPOD-NS): A Secondary Analysis of A Prospective Cohort Study

“…Several prediction models for POD have been developed in different types of surgical patients (Kim et al 2016;Kim et al 2020;Bohner et al 2003), but a risk model for POD speci c to neurosurgical patients may provide unique insights in this vulnerable population. Recently, there are a few studies developed risk models to predict POD in neurosurgical patients, but each of these models has its own limitation, including small sample size, only involving single disease, or predictors unavailable at intensive care unit (ICU) admission (Wang et al 2020b;Zhan et al 2020;Harasawa et al 2014;Flanigan et al 2018). In this study, we developed, internally validated, and tested a risk score model for POD in neurosurgical patients using data from our previous prospective cohort study of adult patients after elective craniotomy (Wang et al 2020a).…”

“…developed by Wang(Wang et al 2020b), but it did not include neurosurgery-speci c factors and cannot early predict POD at the time of ICU admission. In the present study, we developed a prediction model for POD in neurosurgical patients admitted to ICU involving 7 clinical and laboratory variables which were already reported in the previous literatures(Aldecoa et al 2017) and 2 neurosurgery-speci c factors.…”