公共卫生 公共卫生
Yi-Han Hu1, Harris Jamal1, Xinlei Deng2
1Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
环境因素,如邻里特征和空气污染,除了个别风险因素之外,并不能显著改善中风预测. 然而,当临床数据有限时,将人口统计数据与环境数据结合起来是有希望的.
科学领域:
- 流行病学 流行病学
- 环境健康 环境健康
- 机器学习在医学中的应用
背景情况:
- 脑卒中是血管痴呆的主要原因.
- 传统的中风预测模型侧重于个体风险因素.
- 环境暴露可能会影响脑血管健康,需要新的预测方法.
研究的目的:
- 开发和比较用于中风风险预测的机器学习模型.
- 评估环境风险模型 (Env-RiskModel) 与标准风险因素模型 (Standard-RiskModel) 的预测性能.
主要方法:
- 利用了来自研究中风的地理和种族差异原因 (REGARDS) 的数据 (n=24,251).
- 开发了五种随机生存森林模型,包括人口统计,社区特征 (绿地,社会脆弱性,灾害风险) 和环境空气污染 (臭氧).
- 使用C指数和时间依赖的接收器操作特征 (AUROC) 曲线下的区域进行模型性能比较.
主要成果:
- 在12.1年的时间里,6.0%的人患上了中风.
- 纳入环境因素的模型显示,与仅仅人口统计数据相比,环境因素的改善幅度微乎其微.
- 环境风险模型 (C指数=66.1%) 和标准风险模型 (C指数=65.7%) 显示出可比的预测性能.
结论:
- 环境因素并没有显著提高超出已确定的个人风险因素中风预测.
- 环境暴露与人口统计数据相结合,在临床数据稀缺的情况下,为中风预测提供了可行的替代方案.
- 需要进一步的研究来探索额外的环境变量,以评估中风风险.
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