Development and validation of a nomogram for predicting the risk of post-stroke cognitive impairment
10.19845/j.cnki.zfysjjbzz.2023.0141
- VernacularTitle:预测卒中后认知障碍风险列线图的开发和验证
- Author:
Yanxin SHEN
1
;
Li SUN
1
Author Information
1. Department of Neurology,the First Hospital of Jilin University,Changchun 130021,China
- Publication Type:Journal Article
- Keywords:
Post-stroke cognitive impairment;
Mild acute ischemic stroke;
Risk;
Prediction
- From:
Journal of Apoplexy and Nervous Diseases
2023;40(7):606-611
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop and validate a nomogram model to predict the risk of post-stroke cognitive impairment(PSCI) in patients with mild acute ischemic stroke(AIS). Methods We included 315 patients with mild AIS(181 in the PSCI group and 134 in the non-PSCI group) who were admitted to the Department of Neurology of the First Hospital of Jilin University from April 2019 to January 2021.Fifteen potential predictors associated with vascular cognitive impairment(VCI) were selected. The selection of predictors for the PSCI nomogram model was optimized by least absolute shrinkage and selection operator regression. The number of predictors with high effects on PSCI was finally reduced to 10.Based on the 10 predictors,we performed multivariable logistic regression analysis and construct the nomogram model. The accuracy,discriminatory ability,and clinical utility of the prediction model were assessed by using the C-index,calibration curve,and decision curve analysis(DCA). The Bootstrapping validation method was used for internal validation of the model. Results The nomogram model for PSCI risk prediction included five predictors:age,sex,education level,past stroke history,and the diameter of maximum transverse section(DMTS). The C-index of the nomogram model was 0.708(95% confidence interval:0.651-0.765),with good discriminatory ability. The C-index by internal validation was 0.682.The calibration curves showed good consistency. DCA indicated a higher net benefit by using this nomogram model for predicting the risk of PSCI when the probability threshold of PSCI was greater than 27%. Conclusion This PSCI risk nomogram model is based on age,sex,education level,past stroke history,and DMTS,which can help clinicians predict the risk of PSCI for patients with mild AIS. It is worthy of clinical promotion and application.
- Full text:2024061613564647424预测卒中后认知障碍风险列线图的开发和验证.pdf
