在阿尔茨海默病中使用纵向生物标志物进行临床前认知衰退预测的整体生存分析
Dhrubajyoti Ghosh1, Samhita Pal2, Michael Lutz3
1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Journal of Alzheimer's disease : JAD
|August 14, 2025
概括
使用整体生存模型预测阿尔茨海默病的进展可以改善早期风险评估. 一次随访显著提高了对轻度认知障碍和阿尔茨海默病的预测准确度.
科学领域:
- 神经科学是一个神经科学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 早期预测阿尔茨海默病 (AD) 从认知正常 (CN) 状态到轻度认知障碍 (MCI) 或AD的进展对于及时干预至关重要.
- 传统的生存模型与AD进展固有的复杂的纵向生物标志物模式作斗争.
研究的目的:
- 开发和验证综合生存分析框架,以改善最初认知正常的个体临床进展的早期预测.
- 评估纵向生物标志物数据 (包括后续检查次数) 对预测准确性的影响.
主要方法:
- 利用了阿尔茨海默病神经成像计划 (ADNI) 队列中的721名参与者的纵向生物标记数据.
- 整合了受罚的考克斯回归 (LASSO,弹性网) 与先进的生存模型 (随机生存森林,DeepSurv,XGBoost).
- 采用集体平均和贝叶斯模型平均 (BMA) 进行模型预测聚合;使用C指数和时间依赖的AUC评估性能.
主要成果:
- 整体模型达到0.907的峰值C指数和0.904的综合时间依赖AUC,显著超过基线模型 (C指数0.608).
- 在基线后纳入一个后续访问大大提高了预测准确度 (48.1%的C指数,48.2%的AUC增长).
- 第二次后续访问只带来了预测性表现的边际改善.
结论:
- 拟议的整体生存框架有效地整合了多种模型和聚合技术,以提高临床前AD的早期预测.
- 纵向生物标志物数据,特别是从一个后续访问,对于准确的风险分层和个性化干预在AD至关重要.
- 这种方法提供了一种可靠的方法来识别患有AD进展高风险的个体,促进早期治疗策略.
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