开发一个中年特定的CogDrisk算法 (CogDrisk-ML),使得从中年到晚年可验证实施痴呆症风险评估
Md Hamidul Huque1,2,3, Heidi Jane Welberry4, Ranmalee Eramudugolla1,2,3
1School of Psychology, University of New South Wales, Sydney, NSW, Australia.
Age and ageing
|July 21, 2025
概括
一种新的痴呆风险评估工具CogDrisk-ML显示,对于中年成人来说,其准确性有所提高. 该工具与CogDrisk结合,提供了一种全面的终身方法来预防痴呆症.
科学领域:
- 神经学 神经学
- 流行病学 流行病学
- 老年学是一门学科.
背景情况:
- 现有的痴呆风险评估工具在中年成人 (40-64岁) 中的验证有限.
- 心血管风险因素,衰老和痴呆发病率 (CAIDE) 工具对痴呆的预测准确度中等.
- 中年期出现的可修改痴呆风险因素需要一个新的,更准确的评估工具.
研究的目的:
- 开发CogDrisk-ML,一种新的痴呆症风险评估工具.
- 为了补充现有的CogDrisk工具,用于晚年痴呆症风险评估.
- 为了提高中年成人痴呆症风险预测.
主要方法:
- 利用英国生物库和社区动脉样硬化风险研究 (ARIC) 的数据进行开发和验证.
- 外部验证的CogDrisk-ML使用惠特霍尔II队列.
- 采用考克斯回归模型和随机效应元分析,使用Harrell's C统计数据进行可预测性测量.
主要成果:
- 与CAIDE相比,CogDrisk-ML在英国生物银行和惠特霍尔II队列中显示出更高的预测准确度.
- 对CogDrisk-ML的C统计是0.71 (ARIC),0.75 (英国生物银行) 和0.70 (惠特霍尔II).
- 可预测性与ARIC数据集中的CAIDE可比.
结论:
- 开发的CogDrisk-ML工具为中年人提供了更好的痴呆风险预测准确性.
- 当与CogDrisk一起使用CogDrisk-ML时,它为预防痴呆症建立了一个全面的终身框架.
- 这种方法支持在不同生命阶段积极主动地管理痴呆风险.
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