基于里程碑式模型的个人动态预测,使用认知查从轻度认知障碍转化为阿尔茨海默病
Jing Cui1, Durong Chen1, Jiajia Zhang1
1Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 Xin Jian South Road, Taiyuan, China.
Current Alzheimer research
|May 29, 2023
概括
这项研究开发了一种查策略,使用里程碑式的模型来预测轻度认知障碍 (MCI) 患者的阿尔茨海默病 (AD) 风险. 最优的模型,结合功能评估问卷 (FAQ) 和雷听力口语学习测试 (RAVLT) - 忘记,准确地识别了MCI-to-AD转换风险.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 生物统计学 生物统计学
背景情况:
- 早期识别患有阿尔茨海默病 (AD) 高风险的轻度认知障碍 (MCI) 个体对于及时干预至关重要.
- 认知查在AD的早期诊断和预防策略中发挥着至关重要的作用.
研究的目的:
- 提出一种新的查策略,利用里程碑式的模型来预测MCI-AD转换.
- 提供基于纵向神经认知评估的转换的动态预测概率.
主要方法:
- 开发和评估了三种类型的基准模型,使用了312名基线MCI患者的数据.
- 使用了纵向神经认知测试,包括迷你精神状态检查,阿尔茨海默病评估尺度-认知13项,雷雅听力语言学习测试 (RAVLT) 和功能评估问卷 (FAQ).
- 根据预测准确度 (C指数=0.898,布赖尔分数=0.027) 选择了最佳的基准模型 (模型3c),使用7:3的培训-验证分割.
主要成果:
- 在所有模型中,功能评估问卷 (FAQ),RAVLT-immediate和RAVLT-forgetting被确定为MCI-to-AD转换的重要预测因素.
- 最佳基准模型 (模型3c),结合FAQ和RAVLT忘记,显示出高预测性能.
结论:
- 开发的融合常见问题和RAVLT遗忘的里程碑式模型是识别MCI到AD转换风险的可行工具.
- 这一策略可以在早期AD风险评估的认知查计划中有效地实施.
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