ISSN 2097-6046(网络)
ISSN 2096-7446(印刷)
CN 10-1655/R
主管:中国科学技术协会
主办:中华护理学会

中华急危重症护理杂志 ›› 2026, Vol. 7 ›› Issue (6): 755-763.doi: 10.3761/j.issn.2096-7446.2026.06.020

• 综述 • 上一篇    下一篇

缺血性脑卒中后认知障碍风险预测模型的范围综述

赵婉辰1(), 张慧1, 方一帆1, 马聪1, 郭娜2,*()   

  1. 1 中国医学科学研究院北京协和医院护理部 北京市 100730
    2 中国医学科学研究院北京协和医院组织处 北京市 100730
  • 收稿日期:2025-08-10 出版日期:2026-06-10 发布日期:2026-06-02
  • 通讯作者: 郭娜 E-mail:1640182890@qq.com;guonauss@163.com
  • 作者简介:赵婉辰:女,本科(硕士在读),E-mail:1640182890@qq.com
    第一联系人:

    赵婉辰:整理并分析数据、论文撰写;张慧、方一帆:整理数据;马聪:对文章的知识性内容作批评性审阅;郭娜:获取研究经费、指导

  • 基金资助:
    北京协和医院临研专项(2022-PUMCH-B-031)

A scoping review of risk prediction models for cognitive impairment after ischemic stroke

ZHAO Wanchen1(), ZHANG Hui1, FANG Yifan1, MA Cong1, GUO Na2,*()   

  1. 1 Department of NursingPeking Union Medical College Hospital,Chinese Academy of Medical SciencesBeijing 100730, China
    2 Organizational DepartmentPeking Union Medical College Hospital,Chinese Academy of Medical SciencesBeijing 100730, China
  • Received:2025-08-10 Online:2026-06-10 Published:2026-06-02
  • Contact: GUO Na E-mail:1640182890@qq.com;guonauss@163.com
  • Supported by:
    Peking Union Medical College Hospital Clinical Research Special Project(2022-PUMCH-B-031)

摘要:

目的 对国内外缺血性脑卒中后认知障碍风险预测模型进行范围综述,为临床护理实践及后续研究提供参考与借鉴。 方法 全面检索9个中英文数据库,检索时限为建库至2025年3月2日,提取缺血性脑卒中后认知障碍的发生率、模型纳入的预测因子、建模方法及性能指标并进行汇总分析。 结果 共纳入24项研究,缺血性脑卒中后认知障碍的发生率为30.4%~57.4%。每项研究纳入的预测因子数目为1~17个不等,其中年龄和教育水平为出现频次最高的预测因子。模型的构建方式为Logistic回归和机器学习。纳入模型的受试者工作特征曲线下面积(Area Under the Curve,AUC)为0.65~0.98。 结论 现有模型普遍具有高偏倚风险且性能差异大,未来应通过合理纳入预测因子、妥善处理缺失数据、加强内部验证和外部验证等途径综合提升模型质量。

关键词: 缺血性脑卒中, 卒中, 认知障碍, 风险预测, 范围综述, 护理

Abstract:

Objective To conduct a scoping review of risk prediction models for cognitive impairment after ischemic stroke both domestically and internationally,aiming to provide references for clinical nursing practice and subsequent research. Methods A comprehensive search was conducted in 9 Chinese and English databases from the establishment of the databases to March 2,2025. The incidence of cognitive impairment after ischemic stroke,the predictive factors included in the models,the modeling methods,and performance indicators were extracted and summarized. Results A total of 24 studies were included. The incidence of cognitive impairment after ischemic stroke ranged from 30.4% to 57.4%. The number of predictive factors included in each study ranged from 1 to 17,with age and education level were the most frequently included factors. The modeling methods were logistic regression and machine learning. The area under the curve(AUC) values of the included models ranged from 0.65 to 0.98. Conclusion The existing models generally have a high risk of bias and significant performance differences. In the future,the quality of models should be comprehensively enhanced through reasonable inclusion of predictive factors,proper handling of missing data,and strengthening of internal validation and external validation.

Key words: Ischemic Stroke, Stroke, Cognitive Impairment, Risk Prediction, Scoping Review, Nursing Care