Taylor Joanne K, Ahmed Fozia Zahir
Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Arrhythm Electrophysiol Rev. 2023 Apr 24;12:e15. doi: 10.15420/aer.2022.13. eCollection 2023.
Research examining the utility of cardiac device data to manage patients with heart failure (HF) is rapidly evolving. COVID-19 has reignited interest in remote monitoring, with manufacturers each developing and testing new ways to detect acute HF episodes, risk stratify patients and support self-care. As standalone diagnostic tools, individual physiological metrics and algorithm-based systems have demonstrated utility in predicting future events, but the integration of remote monitoring data with existing clinical care pathways for device HF patients is not well described. This narrative review provides an overview of device-based HF diagnostics available to care providers in the UK, and describes the current state of play with regard to how these systems fit in with current HF management.
研究心脏设备数据在心力衰竭(HF)患者管理中的效用的相关研究正在迅速发展。新冠疫情重新激发了人们对远程监测的兴趣,各制造商纷纷开发并测试检测急性心力衰竭发作、对患者进行风险分层以及支持自我护理的新方法。作为独立的诊断工具,个体生理指标和基于算法的系统已证明在预测未来事件方面具有效用,但远程监测数据与现有心脏设备患者临床护理路径的整合情况尚未得到充分描述。本叙述性综述概述了英国医疗服务提供者可获得的基于设备的心力衰竭诊断方法,并描述了这些系统与当前心力衰竭管理的契合现状。