基于认知轨迹的轻度缺血性中风和短暂缺血性发作后预测认知障碍的诺莫грам的开发和内部验证:一个前性队列研究
Panpan Zhao1,2, Lin Shi2, Guimei Zhang2
1Department of Neurology, The First Affiliated Hospital of Henan University, Henan University, Kaifeng, China.
Frontiers in aging neuroscience
|February 13, 2025
概括
我们使用纵向数据开发了两种用于中风后认知障碍的预测模型. 这些基于潜在类增长分析的模型有助于早期识别和治疗认知衰退.
科学领域:
- 神经学 神经学
- 认知科学 认知科学
- 生物统计学 生物统计学
背景情况:
- 目前对中风后认知障碍的预测模型通常依赖于单个时间点评估.
- 需要模型来捕捉随时间推移的认知变化的动态性质.
研究的目的:
- 开发和验证两种用户友好的预测模型,用于轻度中风和过渡性缺血性攻击后的认知轨迹.
- 为了促进早期识别和及时干预认知障碍.
主要方法:
- 一项前性队列研究,对556名患者进行了3个月的随访.
- 隐性类增长分析 (LCGA) 应用于255名患者的2.5年内认知尺度.
- 使用逻辑回归,经ROC,校准和决策曲线分析验证的基于名ogram的预测模型的开发.
主要成果:
- 使用LCGA确定了两个不同的认知轨迹,认知障碍组的29.8%.
- 模型1 (基线MoCA,费里丁,年龄,前脑卒中) 的AUC达到0.973.
- 模型2 (年龄,之前的中风,教育,费里) 达到AUC为0.771,显示出出色的临床适用性.
结论:
- 开发了两种新的,临床上适用的基于脑电图的预测模型,用于中风后的认知轨迹.
- 这些模型为早期发现和及时管理中风后认知障碍提供了有价值的工具.
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