Development and validation of a prediction model of post-stroke delirium in patients with acute stroke
10.3969/j.issn.1671-8283.2024.11.002
- VernacularTitle:急性脑卒中患者卒中后谵妄预测模型的构建与验证
- Author:
Caihong ZHOU
1
;
Canfang SHE
;
Zimeng CHANG
;
Can CHEN
;
Wei ZHU
;
Hua CHEN
Author Information
1. 长沙市第四医院医院感染管理部,湖南长沙,410006;湖南师范大学医学院护理系,湖南长沙,410013
- Publication Type:Journal Article
- Keywords:
acute stroke;
delirium;
risk factors;
prediction model;
Nomogram
- From:
Modern Clinical Nursing
2024;23(11):8-15
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a Nomogram prediction model for post-stroke delirium (PSD) in patients with acute stroke and to verify the effectiveness of the prediction model. Methods A total of 400 patients with acute stroke,admitted to the Department of Neurology in our hospital between June 2022 and March 2023,were retrospectively included in the study as the training group. Independent risk factors for PSD were identified by Logistic regression analysis. A calibration model was constructed to evaluate the consistency of the model. The area under ROC curve (AUC) was used to evaluate the accuracy of the prediction model. Between April and July 2023,172 patients with acute stroke were selected,as the validation group,for external validation of the model. Results The incidence of PSD was found at 27.50％ in the training group and 26.74％ in the validation group. A Nomogram prediction model was constructed with the five predictors:cerebrovascular interventional surgery,hypersensitive C-reactive protein,smoking,National Institutes of Health Stroke Scale (NIHSS) score and age. The calibration curve was found close to the ideal curve with an AUC at 0.797 for the training group,and the risk prediction corresponding to the maximum Youden index was 0.554 with a predicted threshold of 134.63. The calibration curve of the validation group was also found close to the ideal curve,with an AUC of 0.844. Conclusion The nomogram prediction model for PSD in patients with acute stroke demonstrates a good risk prediction value for risk assessment,which can help medical staff to effectively predict the risk for PSD in patients with acute stroke and to take corresponding measures in prevention of PSD.
