SHI Ying 1,2 , LI Mengwei 2,3 , LI Lixuan 2 , YAN Wei 4 , CAO Desen 5 , ZHANG Zhengbo 2 , YAN Muyang 3
  • 1. Chinese PLA Medical School, Beijing 100853, P. R. China;
  • 2. Center for Artificial Intelligence, Chinese PLA General Hospital, Beijing 100853, P. R. China;
  • 3. Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, Hebei 066000, P. R. China;
  • 4. Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, P. R. China;
  • 5. Department of Medical Engineering, Medical Supplies Center , Chinese PLA General Hospital, Beijing 100853, P. R. China;
Heart failure; Remote dynamic monitoring; Early warning; Disease management
10.7507/1001-5515.202406007
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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.

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