ISSN 2096-7446　CN 10-1655/R
主管：中国科协技术协会
主办：中华护理学会
中华急危重症护理杂志

中华急危重症护理杂志 ›› 2023, Vol. 4 ›› Issue (8): 747-752.doi: 10.3761/j.issn.2096-7446.2023.08.016

• 证据综合与应用 • 上一篇    下一篇

脑卒中后认知障碍风险预测模型的系统评价

魏慧 杨红燕 杨婷 柳妙妙 吴旻昊 高杨琴   

  • 出版日期:2023-08-10 发布日期:2023-08-17

A systematic review of risk prediction models for post-stroke cognitive impairment

WEI Hui，YANG Hongyan，YANG Ting，LIU Miaomiao，WU Minhao，GAO Yangqin   

  • Online:2023-08-10 Published:2023-08-17

摘要： 目的 系统评价脑卒中后认知障碍（post-stroke cognitive impairment，PSCI）风险预测模型，以期为临床医护人员选择合适的风险预测模型提供参考。 方法 计算机检索PubMed、Embase、Web of Science、Cochrane Library、 中国生物医学文献数据库、中国知网、万方数据库和维普数据库中与PSCI风险预测模型相关的文献，检索时间为建库至2022年8月27日。 由2名研究者独立筛选文献及提取数据，采用预测模型偏倚风险评估工具评价纳入文献的偏倚风险和适用性，采用RevMan 5.3软件对纳入模型中具有共性预测因子进行Meta分析。 结果 共纳入8篇文献，涉及 8个PSCI风险预测模型， 文献总体偏倚风险较高， 适用性好， 其中7个模型的受试者工作特征曲线下面积为0.807～ 0.930。 Meta分析结果显示，年龄[OR=1.05，95%CI（1.03，1.07）］、性别[OR=2.39，95%CI（1.58，3.61）］、糖尿病[OR= 3.55，95%CI（1.67，7.55）］、非腔隙梗死数量[OR=1.59，95%CI（1.21，2.09）］是PSCI的有效预测因子。 结论 PSCI风险预测模型的预测效能较好，总体偏倚风险较高。 未来研究者在报告风险预测模型时，应参照预测模型偏倚风险评估工具的报告规范；临床医护人员应重点评估高龄、女性、糖尿病、非腔隙性梗死的脑卒中患者的PSCI风险。

关键词: 卒中, 认知障碍, 模型, 统计学, 系统评价专题, 循证护理学

Abstract: Objective To systematically evaluate the risk prediction model of post-stroke cognitive impairment （PSCI），so as to provide reference for clinical doctors and nurses to select appropriate risk prediction model. Methods We searched PubMed，Embase，Web of Science Cochrane Library，CBM，CNKI，WanFang，and VIP for literature related to risk prediction model of PSCI from the inception of database to August 27，2022. Two researchers independently screened the literature and extracted data，PROBAST was used to assess the bias risk and applicability of the included literature，and RevMan 5.3 software was used to conduct meta-analysis on the common predictors in the included models. Results A total of 8 articles were included. The overall risk of bias of the literature was high，and its applicability was good. The area under the working characteristic curve of subjects in the 8 models was 0.807~0.930. Meta analysis showed that age[OR=1.05，95%CI（1.03，1.07）］，gender[OR=2.39， 95%CI （1.58，3.61）］，diabetes [OR =3.55，95%CI （1.67，7.55）］，and number of non-lacunar infarcts [OR =1.59，95% CI （1.21，2.09）］ were all effective predictors of PSCI. Conclusion The PSCI risk prediction model has good prediction efficiency and high overall bias risk. When reporting the risk prediction model，future researchers should refer to PROBAST’s reporting specifications，and clinical medical staff should timely assess the risk of PSCI in elderly stroke patients，women，patients with diabetes and non-lacunar infarction.

Key words: Stroke, Cognition Disorders, Models, Statistical, Systematic Reviews as Topic, Evidence-Based Nursing