描述和描述ALS疾病进展的变异性
Muzammil Arif Din Abdul Jabbar1,2, Ling Guo3, Yang Guo3
1University of Cambridge, Cambridge, United Kingdom of Great Britain and Northern Ireland.
Amyotrophic lateral sclerosis & frontotemporal degeneration
|October 5, 2023
概括
在修订后的骨髓变形侧面硬化功能评分表 (ALSFRS-R) 评分中,一种新的测量可变性有助于描述疾病进展. 这种变化与疾病的持续时间有关,并且可以使用临床数据来预测.
科学领域:
- 神经学 神经学
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 修订后的肌缩侧面硬化功能评分表 (ALSFRS-R) 评分是衡量ALS进展的标准.
- 然而,ALSFRS-R评分并不能完全捕捉到ALS的多样性.
- 为了更好地表征疾病,引入了ALSFRS-R得分的新型变异性测量方法.
研究的目的:
- 引入和验证ALSFRS-R得分的新变化度量.
- 探索ALSFRS-R得分变化和疾病跨度之间的关系.
- 开发一种机器学习模型,用于预测ALS变异性.
主要方法:
- 利用来自ALS临床试验数据库的5030名ALS临床试验患者的数据.
- 计算了使用新型措施的疾病进展变化率,并将其与疾病持续时间相关联.
- 开发并验证了一种机器学习模型,使用临床,实验室和人口统计数据来预测类的变化.
主要成果:
- 增加ALSFRS-R得分的变化与更长的疾病持续时间相关.
- 机器学习模型在预测变化类的准确度达到60.1-72.7%.
- 确定了ALS变异性的关键临床,实验室和人口预测因素.
结论:
- ALSFRS-R得分的变化是疾病持续时间的一个重要因素,特别是在快速进展的ALS.
- 已识别的预测因素可能为ALS变化的病理生理学提供见解.
- 未来的临床试验可能旨在增加快速进展的ALS患者的变异性.
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