对吸烟类型和中风风险的横截面研究:开发用于识别中风风险的预测模型
Chao Ding1, Minjia Yuan2, Jiwei Cheng1
1Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Frontiers in physiology
|April 8, 2025
概括
吸烟可燃香烟显著增加了中风的风险. 一个XGBoost模型准确地预测了中风风险,帮助针对这个日益增长的全球健康问题制定个性化预防策略.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 卒中是全球死亡和残疾的主要原因之一.
- 增加的中风发病率与人口老龄化和风险因素的增加有关.
- 目前的中风风险预测工具需要提高准确性和个性化,特别是在吸烟等可修改因素方面.
研究的目的:
- 调查不同类型的烟草使用与中风风险之间的关联.
- 开发和评估机器学习模型,以改善中风风险预测.
- 为了确定个性化中风预防的关键预测因素.
主要方法:
- 利用了来自国家健康和营养检查调查 (NHANES) 和行为风险因素监测系统 (BRFSS) 的数据.
- 参与者被分为非吸烟者,独家可燃香烟使用者,独家电子烟使用者和双重使用者.
- 采用多变量逻辑回归和四种机器学习模型 (XGBoost,逻辑回归,随机森林,高斯天真贝叶斯) 来进行分析和预测,并使用AUC和Shapley值进行评估.
主要成果:
- 这项研究包括273,028人.
- 仅限于燃烧性香烟的吸烟与明显增加的中风风险有关 (β: 1.36,95% CI: 1.26-1.47,P < 0.0001).
- XGBoost模型表现出卓越的性能,曲线下的面积 (AUC) 为0.794 (95% CI = 0.787-0.802).
结论:
- 不同类型的吸烟对中风风险有重大影响.
- 基于XGBoost的模型为中风风险评估提供了更高的准确性.
- 这种预测模型可以为预防中风提供个性化干预措施,从而减少医疗负担.
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