充血性心力衰竭(CONAN)患者使用自我监督对比学习衍生风险指数进行充血评估:一项前瞻性队列研究的方案和设计

IF 4.4 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2025-08-04 eCollection Date: 2025-09-01 DOI:10.1093/ehjdh/ztaf004
Bojan Hartmann, Niels-Ulrik Hartmann, Julia Brandts, Marlo Verket, Nikolaus Marx, Niveditha Dinesh, Lisa Schuetze, Anna Emilia Pape, Dirk Müller-Wieland, Markus Kollmann, Katharina Marx-Schütt, Martin Berger, Andreas Puetz, Felix Michels, Luca Leon Happel, Lars Müller, Guido Kobbe, Malte Jacobsen
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引用次数: 0

摘要

目的:反复充血性发作是心衰患者住院的主要原因。迄今为止,门诊管理采用被动的方式,通过频繁的随访对患者进行临床评估,早期发现充血。本研究旨在利用连续记录的可穿戴时间序列数据,评估自我监督对比学习衍生风险指数检测急性失代偿性心力衰竭(ADHF)发作的能力。方法和结果:这是一项单臂前瞻性队列先导研究的方案,将包括290例ADHF患者。急性失代偿性心力衰竭可通过临床体征和症状以及其他诊断(如NT-proBNP)进行诊断。患者将接受标准护理治疗,辅以持续的基于可穿戴设备的生命体征和身体活动监测,并随访90天。在随访期间,将进行研究访问,无临床ADHF的表现将被称为“常规”,这些发作的数据将被呈现给深度神经网络,该网络由自监督对比学习目标训练,以从时间序列中提取常规时期的典型特征。该模型用于计算风险指数,衡量观测到的特征与正常周期的特征的差异。本研究的主要结果将是风险指数检测ADHF发作的准确性。作为次要结果,将评估数据完整性和经过验证的问卷系统可用性量表得分。结论:通过可穿戴式和自我监督对比学习的持续监测,展示可靠的充血检测,有助于临床护理中先发制人的心力衰竭管理。临床试验注册:该研究已在德国临床试验注册(DRKS00034502)中注册。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Congestion assessment using a self-supervised contrastive learning-derived risk index in patients with congestive heart failure (CONAN): protocol and design of a prospective cohort study.

Congestion assessment using a self-supervised contrastive learning-derived risk index in patients with congestive heart failure (CONAN): protocol and design of a prospective cohort study.

Congestion assessment using a self-supervised contrastive learning-derived risk index in patients with congestive heart failure (CONAN): protocol and design of a prospective cohort study.

Aims: Recurrent congestive episodes are a primary cause of hospitalizations in patients with heart failure. Hitherto, outpatient management adopts a reactive approach, assessing patients clinically through frequent follow-up visits to detect congestion early. This study aims to assess the capabilities of a self-supervised contrastive learning-derived risk index to detect episodes of acute decompensated heart failure (ADHF) in patients using continuously recorded wearable time-series data.

Methods and results: This is the protocol for a single-arm, prospective cohort pilot study that will include 290 patients with ADHF. Acute decompensated heart failure is diagnosed by clinical signs and symptoms, as well as additional diagnostics (e.g. NT-proBNP). Patients will receive standard-of-care treatment, supplemented by continuous wearable-based monitoring of vital signs and physical activity, and are followed for 90 days. During follow-up, study visits will be conducted and presentations without clinical ADHF will be referred to as 'regular' and data from these episodes will be presented to a deep neural network that is trained by a self-supervised contrastive learning objective to extract features from the time-series that are typical in regular periods. The model is used to calculate a risk index measuring the dissimilarity of observed features from those of regular periods. The primary outcome of this study will be the risk index's accuracy in detecting episodes with ADHF. As secondary outcome data integrity and the score in the validated questionnaire System Usability Scale will be evaluated.

Conclusion: Demonstrating reliable congestion detection through continuous monitoring with a wearable and self-supervised contrastive learning could assist in pre-emptive heart failure management in clinical care.

Clinical trial registration: The study was registered in the German clinical trials register (DRKS00034502).

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