一种机器学习方法来预测阿尔茨海默病临床试验中的认知衰退
Bhargav T Nallapu1, Kellen K Petersen2, Tianchen Qian3
1Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY.
Neurology
|March 25, 2025
概括
机器学习模型可以识别不太可能在阿尔茨海默病 (AD) 临床试验中进展的个体. 这有助于通过选择会随着时间的推移表现出认知能力下降的参与者来改善试验设计.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 临床试验 临床试验
背景情况:
- 大约40%的阿尔茨海默病 (AD) 临床试验参与者在80周内没有表现出认知能力下降.
- 识别非进展的个体可以提高在AD试验中检测治疗效应的统计能力.
研究的目的:
- 开发和验证能够预测在80周的安慰剂治疗期间不太可能经历认知衰退的机器学习 (ML) 模型.
- 改善AD临床试验参与者的选择标准.
主要方法:
- 利用了EXPEDITION3 AD试验和阿尔茨海默氏病神经成像计划 (ADNI) 的安慰剂组的数据.
- 训练有素的ML分类器使用人口统计,APOE基因型,神经心理测试和体积MRI生物标志物来区分临床上有意义的认知衰退 (CMCD) 和认知稳定 (CS) 参与者.
- 通过对70%的EXPEDITION3安慰剂样本进行5倍交叉验证验证模型,然后在匹配的ADNI样本上进行内部和外部验证.
主要成果:
- 在EXPEDITION3验证样本中,ML模型显示出高灵敏度和适度的特异性,积极预测值 (PPV) 至少比CMCD的基线患病率高11%.
- 在匹配的ADNIAD样本 (N=105) 中,经验证的模型也显示出高灵敏度,适度特异性,PPV至少比基线流行率高15%.
- 这些模型成功地确定了在试验期间保持认知稳定的参与者.
结论:
- 预测模型显示,通过基于预期的认知衰退的选择性包含/排除标准来提高AD试验设计的前景.
- 这些预测模型在各种AD临床试验数据集中的进一步验证是有必要的.
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