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基于机器学习的检测 睡眠呼吸障碍 在多变性心肌病症中的检测
Keitaro Akita1, Shigetaka Kageyama2, Sayumi Suzuki1
1Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Heart (British Cardiac Society)
|April 8, 2024
概括
一个新的机器学习模型有效地识别了高性心肌病 (HCM) 患者的睡眠呼吸障碍 (SDB). 这种工具有助于早期检测和治疗SDB,可能防止HCM个体的不良心血管事件.
科学领域:
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
- 医疗保健中的机器学习
背景情况:
- 增高性心肌病 (HCM) 经常与睡眠呼吸障碍 (SDB) 一起发生.
- 未经治疗的SDB可以导致HCM患者的不良心血管事件.
- 目前在HCM群体中SDB检测率低于最佳水平.
研究的目的:
- 开发基于机器学习的歧视模型,用于识别HCM患者的SDB.
- 改进在HCM患者同时发生的SDB的检测.
主要方法:
- 一项涉及369名HCM患者的多中心研究,这些患者接受了夜间氧计.
- 使用临床参数来预测高氧不和指数 (ODI>10) 的随机森林歧视模型的开发.
- 将模型的性能与之前的后勤回归模型进行比较.
主要成果:
- 机器学习模型在测试组中实现了接收器运行特征曲线下的面积为0.86.
- 在模型中观察到高灵敏度 (0.91) 和特异性 (0.68).
- 该模型显著优于之前的逻辑回归模型 (p=0.03).
结论:
- 这是第一个开发用于在HCM患者中检测SDB的机器学习模型的研究.
- 该模型显示了在这个人群中具有成本效益的查和及时治疗SDB的潜力.
- 改善SDB管理可以帮助预防心脏重塑和HCM的不良事件.
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