在患有充血性心力衰竭 (CONAN) 的患者中使用自我监督的对比学习衍生的风险指数进行拥堵评估:前性队列研究的协议和设计
Bojan Hartmann1, Niels-Ulrik Hartmann1, Julia Brandts1
1Department of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
European heart journal. Digital health
|September 23, 2025
概括
这项研究评估了一个新的AI工具,使用可穿戴数据来检测心力衰竭拥堵. 目标是实现主动管理,减少急性不补偿性心力衰竭 (ADHF) 的住院治疗.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 经常出现的拥堵导致心力衰竭患者经常住院治疗.
- 目前的心力衰竭的门诊治疗是反应性的,依赖于随访期间的临床评估.
- 早期发现堵塞对于有效的心力衰竭管理至关重要.
研究的目的:
- 评估自主监督的对比学习衍生风险指数用于检测急性不补偿性心力衰竭 (ADHF) 情节的能力.
- 利用连续记录的可穿戴时间序列数据来加强心力衰竭监测.
- 探索AI在预防性心力衰竭管理中的潜力.
主要方法:
- 一个单臂前性队列试点研究,包括290名被诊断患有ADHF的患者.
- 在90天的随访期间,对生命体征和身体活动进行持续的可穿戴监测.
- 一个经过自我监督的对比学习训练的深度神经网络,以识别与"正常"生理状态的偏差,计算风险指数.
主要成果:
- 主要结果是风险指数在检测ADHF发作中的准确性.
- 二次结果包括数据完整性和患者报告的可用性通过系统可用性量表.
- 该研究协议已注册为DRKS00034502.2.
结论:
- 使用可穿戴设备的持续监测和人工智能驱动的风险评估显示,对早期拥堵检测有希望.
- 这种方法可以促进心力衰竭的预防性管理策略.
- 可靠的检测可能会显著减少与急性不补偿性心力衰竭相关的住院治疗.
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