Screening tools, predictors and predictive models for post-stroke delirium / 国际脑血管病杂志

Delirium is a common complication after stroke. Post-stroke delirium is associated with the poor outcome and increased mortality. This article reviews the screening tools, predictive factors and predictive models of post-stroke delirium.

Construction and validation of predictive model for post-stroke delirium in patients with acute ischemic stroke / 国际脑血管病杂志

Objective:To construct a predictive model of post-stroke delirium (PSD) in patients with acute ischemic stroke (AIS), and to verify its predictive value.Methods:Patients with AIS admitted to the Department of Neurology, Lianyungang Hospital Affiliated to Xuzhou Medical University from February to May 2022 were enrolled prospectively. They were divided into modeling group and validation group according to the order of enrollment. Depending on whether the patients had delirium or not, the patients in the modeling group were divided into delirium group and non-delirium group. The independent risk factors for PSD were determined by multivariable logistic regression analysis, and the prediction model of PSD was constructed accordingly. The predictive value of the model was verified by the receiver operating characteristic curve. Results:Three hundred and fifty patients with AIS were included in the modeling group, of which 71 (20.28%) had PSD. The validation group included 150 patients with AIS, and 36 of them (24.00%) had PSD. Multivariate logistic regression analysis showed that age (odds ratio [ OR] 1.036, 95% confidence interval [ CI] 1.000-1.074; P=0.050], National Institutes of Health Stroke Scale (NIHSS) score ( OR 1.607, 95% CI 1.438-1.797; P<0.001), neutrophil/lymphocyte ratio (NLR) ( OR 1.135, 95% CI 1.016-1.267; P=0.025), and atrial fibrillation ( OR 5.528, 95% CI 1.315-23.245; P=0.020) were the independent risk factors for PSD. The predictive model was Z=0.036×age+0.475×NIHSS score+0.127×NLR+1.710×assignment of atrial fibrillation - 10.160. The area under the curve of the model was 0.935, and the sensitivity and specificity were 97.2% and 82.5% respectively. Conclusion:This model can effectively predict the PSD risk of patients with AIS, with higher sensitivity and specificity, and can provide a basis for PSD screening of patients with AIS.