Yang Yifang, Chen Yajing, Yang Yiyi, Yang Tingting, Wu Tingting, Chen Junbo, Yan Fanghong, Han Lin, Ma Yuxia
School of Nursing, Evidence-Based Nursing Center, Lanzhou University, Lanzhou, China.
Department of Nursing, Gansu Provincial Hospital, Lanzhou, China.
Public Health Nurs. 2025 May-Jun;42(3):1375-1388. doi: 10.1111/phn.13509. Epub 2025 Jan 9.
Stroke is one of the most serious illnesses worldwide and is the primary cause of acquired disability among adults. Post-stroke cognitive impairment (PSCI) is a complication of stroke that significantly impacts patients' daily activities and social functions. Therefore, developing a risk prediction model for PSCI is essential for identifying and preventing disease progression.
This study systematically reviewed and analyzed PSCI prediction models, identifying the associated risk factors.
We systematically retrieved literature from PubMed, Cochrane Library, Embase, and other sources. Two researchers independently extracted the literature and assessed the risk of bias using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and The Prediction Model Risk of Bias Assessment Tool (PROBAST).
A total of 20 articles describe the PSCI prediction model, with an incidence rate ranging from 8% to 75%. The area under the receiver operating characteristic curve (AUC) value for the development models ranged from 0.66 to 0.969, while the validation models ranged from 0.763 to 0.893. Age, diabetes, hypersensitive C-reactive protein (hs-CRP), hypertension, and homocysteine (hcy) were identified as the strongest predictors.
In this systematic review, several PSCI prediction models demonstrate promising prediction performance, although they often lack external validation and exhibit high heterogeneity in some predictive factors. Therefore, we recommend that medical practitioners utilize a comprehensive set of predictive factors to screen for high-risk PSCI patients. Furthermore, future research should prioritize refining and validating existing models by incorporating novel variables and methodologies.
中风是全球最严重的疾病之一，是成年人后天残疾的主要原因。中风后认知障碍（PSCI）是中风的一种并发症，会显著影响患者的日常活动和社交功能。因此，开发PSCI风险预测模型对于识别和预防疾病进展至关重要。
本研究系统回顾和分析PSCI预测模型，识别相关风险因素。
我们系统地从PubMed、Cochrane图书馆、Embase和其他来源检索文献。两名研究人员独立提取文献，并使用预测模型研究系统评价的关键评估和数据提取（CHARMS）清单以及预测模型偏倚风险评估工具（PROBAST）评估偏倚风险。
共有20篇文章描述了PSCI预测模型，发病率从8%到75%不等。开发模型的受试者工作特征曲线（AUC）值范围为0.66至0.969，而验证模型的范围为0.763至0.893。年龄、糖尿病、超敏C反应蛋白（hs-CRP）、高血压和同型半胱氨酸（hcy）被确定为最强的预测因素。
在本系统评价中，尽管一些PSCI预测模型往往缺乏外部验证且在某些预测因素上表现出高度异质性，但仍显示出有前景的预测性能。因此，我们建议医生利用一套全面的预测因素来筛查PSCI高危患者。此外，未来的研究应优先通过纳入新变量和方法来完善和验证现有模型。
