人工智能应用于老年人电气和非侵入性血液动力学标记器 失补偿慢性心力衰竭患者
Gianfranco Piccirillo1, Federica Moscucci2, Martina Mezzadri1
1Department of Internal and Clinical Medicine, Anesthesiology and Cardiovascular Sciences, Policlinico Umberto I, "Sapienza" University of Rome, 00185 Rome, Italy.
Biomedicines
|April 27, 2024
概括
通过心电图测量的Tend间隔 (Te) 可以预测无补偿慢性心力衰竭 (CHF) 患者的死亡率. 这些非侵入性标记物可能能够早期检测到恶化的条件,以便进行远程监控.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
背景情况:
- 脱补偿性慢性心力衰竭 (CHF) 具有显著的死亡风险.
- 对CHF患者的非侵入性监测对于及时干预至关重要.
- 可以改进现有的评估CHF严重程度和预测结果的方法.
研究的目的:
- 为了评估Tend区间 (Te) 和生物阻抗衍生的血液动力学标记物的预测能力,去补偿的CHF.
- 为了比较降低 (HFrEF) 和保留 (HFpEF) 喷射分数的CHF患者之间的复极化和血液动力学数据.
- 为了评估复极化和血液动力学数据的变化,临床改善或恶化.
主要方法:
- 243名缺乏补偿的CHF患者接受了5分钟的ECG记录,以测量Te和Te标准偏差 (TeSD).
- 一个129名患者的小组收集了额外的非侵入性血液动力学和再极化数据.
- 多变量逻辑回归被用来分析死亡率预测因素.
主要成果:
- 与幸存者相比,已故患者的Tend间隔 (Te) 显著更高.
- Te独立地与增加的总和心血管死亡率有关.
- 与HFpEF患者相比,HFrEF患者表现出明显的复极化和血液动力学概况.
- 治疗后显示NT-proBNP减少的患者改善了Te,心率,血压和收缩能力.
结论:
- 电心电图衍生的电信号和生物阻抗标记器有效监测先进的不补偿性心血管功能障碍.
- 这些简单,非侵入性和可重复的标记可以帮助远程监测患者.
- 这些标记物显示出使用人工智能和机器学习早期检测CHF患者恶化的潜力.
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