Shi Ying, Li Mengwei, Li Lixuan, Yan Wei, Cao Desen, Zhang Zhengbo, Yan Muyang
Chinese PLA Medical School, Beijing 100853, P. R. China.
Center for Artificial Intelligence, Chinese PLA General Hospital, Beijing 100853, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Aug 25;42(4):857-862. doi: 10.7507/1001-5515.202406007.
Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
心力衰竭（HF）是所有心脏疾病的终末期，具有高患病率、高死亡率以及沉重的社会和经济负担的特点。HF病情恶化的早期预警对于门诊管理和降低再入院率具有重要价值。目前，能够捕捉HF患者血液动力学和生理参数变化的远程动态监测技术已成为早期预警的主要方法，也是临床研究中的一个热门研究课题。本文系统回顾了该领域的进展，其被分为基于植入设备的侵入性监测、基于可穿戴设备的非侵入性监测以及基于音频和视频的其他监测技术。侵入性监测主要涉及诸如左心房压力和肺动脉压力等直接血液动力学参数，而非侵入性监测涵盖胸阻抗、心电图、呼吸和活动水平等参数。这些参数在HF病情恶化的早期阶段呈现出特征性变化。鉴于HF患者的临床异质性，多源信息融合分析可显著提高预警模型的预测准确性。本研究结果表明，与侵入性监测相比，非侵入性监测技术具有患者依从性好、操作简便和性价比高的优势，结合人工智能驱动的多模态数据分析方法，在建立HF门诊管理系统方面显示出显著的临床应用潜力。
