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| 卒中后认知障碍的预测指标的研究进展 |
| Research Progress on Predictive Indicators of Post-Stroke Cognitive Impairment |
| 投稿时间：2025-05-06 修订日期：2025-05-06 |
| DOI： |
| 中文关键词: 卒中后认知障碍 预测指标 预测模型 |
| 英文关键词: Post-Stroke Cognitive Impairment Predictive Indicators Predictive Model |
| 基金项目:四川省卫健委临床研究专项基金：（急性大血管闭塞性卒中后认知障碍诊断预测模型的临床研究，基金编号：23LCYJ034），四川省医学科技创新研究会专项基金：（探讨红景天苷经PI3K/AKT通路对脑无复流现象中周细胞凋亡的保护作用，基金编号：YCH-KY-YCZD2024-037），四川省自然科学基金面上项目（基于PI3K/AKT和NF-κB信号通路探讨红景天苷对脑缺血/再灌注后无复流现象中周细胞凋亡的调控机制，基金编号：2025ZNSFSC0618），成都医学院—雅安市人民医院临床科学研究基金（川西高原地区急性大血管闭塞型脑卒中防治关键技术和人工智能预测模型的研究，基金编号：24LHLNYX1-38） |
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| 中文摘要: |
| 卒中是全球成人死亡和残疾的主要原因,缺血性卒中尤为严重。尽管卒中诊疗技术的进步显著降低了死亡率和肢体残障率,但卒中后认知障碍(PSCI)作为常见并发症,依然严重影响患者的生活质量。PSCI表现为记忆、抽象思维、视空间能力和人格行为的损害,且与生存率密切相关。尽管已有大量研究致力于探索PSCI的早期预警标志物,但这些成果尚未实现临床转化,导致患者可能错过最佳治疗时机。因此,PSCI的早期识别和干预应成为卒中管理的重要环节。本综述总结了PSCI的最新研究进展,探讨了影像学检查、体液标志物及神经功能评估等检测手段的潜力。未来研究应聚焦于通过影像学与人工智能结合,早期识别认知损害风险,探索精准的诊断指标,并构建临床可应用的PSCI风险预测模型,推动早预警、早诊断、早治疗,从而为PSCI的临床管理提供新思路。 |
| 英文摘要: |
| Stroke is a leading cause of death and disability in adults worldwide, with ischemic stroke being particularly prevalent. Although advancements in stroke treatment have significantly reduced mortality and disability rates, post-stroke cognitive impairment (PSCI) remains a common complication that severely impacts patients" quality of life. PSCI is characterized by impairments in memory, abstract thinking, visuospatial abilities, and personality changes, and is closely linked to survival rates. Despite extensive research on early biomarkers for PSCI, these findings have not yet been successfully translated into clinical practice, leading to missed opportunities for optimal treatment. Therefore, early identification and intervention of PSCI should be a key component of comprehensive stroke management. This review summarizes the latest research on PSCI, exploring the potential of neuroimaging, fluid biomarkers, and neurofunctional assessments. Future research should focus on combining neuroimaging with artificial intelligence to identify cognitive impairment risks early, developing precise diagnostic markers, and creating clinically applicable PSCI risk prediction models. This approach aims to enable early warning, early diagnosis, and early treatment, offering new perspectives for PSCI clinical management. |
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