临床表现 临床表现
Ryan D Frank1, Teresa J Christianson1, Winnie Z Fan1
1Mayo Clinic, Rochester, MN, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
这项研究开发了一种新的模型,使用梅奥测试驱动 (MTD) 数字评估来跟踪认知变化. 该模型准确地预测了轻度认知障碍 (MCI) 或痴呆症 (DEM) 患者的认知衰退.
科学领域:
- 神经科学和认知科学 神经科学和认知科学
- 数字健康和医疗信息学
背景情况:
- 远程数字认知评估为认知查和监测提供了可扩展的解决方案.
- 梅奥测试驱动 (MTD) 是一个经过验证的数字评估工具,包括内存和处理速度测试.
- 在数字评估中存在对纵向认知变化预测模型的需求.
研究的目的:
- 开发一种有条件的规范模型,用MTD预测纵向认知变化.
- 为了验证这个预测模型在认知不受损 (CU) 和轻度认知障碍 (MCI) /痴呆 (DEM) 参与者的独立队列中.
主要方法:
- 开发了一种条件规范模型,使用线性混合效应模型对1355名CU参与者进行2-4次MTD评估.
- 包括年龄,性别,教育和基线得分作为预测因素;评估了额外的变量>1%的r平方增加.
- 在独立的CU和MCI/DEM队列中对模型进行外部验证,比较观察和预测的得分.
主要成果:
- 年龄,基线得分,性别和教育在最终模型中是重要的预测因素.
- 该模型显示了CU开发和验证队列中观察到的和预测的得分之间的良好一致.
- 该模型表明对纵向认知衰退的敏感性,在MCI/DEM参与者中,残留z-score明显较差.
结论:
- 开发了用于MTD评估的纵向规范模型.
- 这些模型可以识别表现出异常纵向认知衰退的个体.
- 这些发现支持使用基于MTD的模型来监测认知轨迹.
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