清醒期间瞬间心率序列与心血管事件之间的关联:基于睡眠心脏健康研究的研究
1Department of Cardiovascular Medicine, Liuzhou People's Hospital, Liuzhou, Guangxi, China - huangnan1101@outlook.com.
Minerva cardiology and angiology
|September 10, 2024
概括
即时心率 (IHR) 序列有效预测心血管事件. 机器学习模型,特别是XGBoost,表现出强大的预测性能,强调IHR是心血管疾病研究的关键因素.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 在医疗保健中的数据科学.
背景情况:
- 心率变化 (HRV) 与心血管疾病 (CVD) 有关.
- 对于心脏病预测即时心率 (IHR) 的研究有限.
- 这项研究探讨了用于预测心血管事件的IHR序列.
研究的目的:
- 使用IHR序列开发心血管事件的预测模型.
- 评估后勤回归,随机森林和XGBoost模型的性能.
主要方法:
- 从睡眠心脏健康研究 (SHHS) 中对2977名参与者的回顾性队列研究.
- 在睡眠前的清醒期间测量的IHR指标.
- 使用后勤回归,随机森林和XGBoost模型,并使用曲线下的面积 (AUC) 进行评估.
主要成果:
- 1460名参与者在15年内经历了心血管疾病事件.
- 较高的SDHR,CVHR,LF,HF和TP与较低的心血管疾病风险有关.
- 更高的SD比率与心血管疾病风险增加有关. XGBoost模型实现了最高的AUC (0.828).
结论:
- IHR序列是心血管事件的显著预测因素.
- 在心血管疾病的临床研究中,IHR指标需要得到更多的关注.
- 机器学习模型,特别是XGBoost,在使用IHR的CVD风险预测方面表现有前途.
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