基于HINODE除器患者的生理传感器数据预测心力衰竭事件
Nobuhiro Nishii1, Yasushi Sakata2, Toyoaki Murohara3
1Department of Cardiovascular Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
ESC heart failure
|July 3, 2024
概括
HeartLogic算法成功地检测到心力衰竭事件 (HFEs) 的恶化,灵敏度为80.8%,使得早期干预成为可能. 这种远程监控工具预测风险增加,可能减少心力衰竭患者住院治疗.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 远程监控患者 远程监控患者
背景情况:
- 心力衰竭住院经常发生,导致高死亡率,再入院和经济负担.
- 早期发现恶化的心力衰竭 (HF) 对于及时干预和减少住院治疗至关重要.
- 心灵逻辑算法利用多传感器设备数据来预测即将发生的高频恶化.
研究的目的:
- 评估HeartLogic算法在预测心力衰竭事件 (HFEs) 中的灵敏度.
- 为了评估HeartLogic算法的假阳性警报率 (FPR).
- 为了比较患有和没有HeartLogic警报的患者中HFEs的发病率.
主要方法:
- 在HINODE研究中,有144名患者 (ICD/CRT-D) 获得了远程监测数据.
- 心灵逻辑警报被追溯模拟;临床医生和患者对警报是盲目的.
- 独立委员会对HFE进行了裁决; 计算了灵敏度,FPR和事件率.
主要成果:
- 心灵逻辑算法检测到名义值的HFE的80.8%.
- 在警报状态下,HFE的发生率是警报状态下的8.4倍,而非警报状态的8.4倍 (P < 0.001).
- 错误的阳性警报率为每年患者1.16个警报.
结论:
- 使用HeartLogic远程监测患者,成功检测到恶化的心力衰竭的早期迹象.
- 该算法可以预测风险增加的时期,促进早期干预.
- 在心力衰竭患者群体中,HeartLogic具有减少住院的潜力.
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