Lee Keni Cheng-Siang, Breznen Boris, Ukhova Anastasia, Martin Seth Shay, Koehler Friedrich
General Medicines Global Business Unit, Sanofi, Paris, France.
Evidence Synthesis, Evidinno Outcomes Research Inc., Vancouver, BC, Canada.
Front Cardiovasc Med. 2023 Sep 7;10:1231000. doi: 10.3389/fcvm.2023.1231000. eCollection 2023.
The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.
移动技术的广泛应用为心力衰竭（HF）患者出院后护理提供了一种新方法。通过实现诊所与居家患者之间的非侵入式远程监测及双向实时通信，以及众多其他功能，移动技术有潜力显著改善远程患者护理。这篇文献综述总结了与虚拟医疗保健（VHC）相关的临床证据，VHC被定义为在HF患者出院后护理中由护理团队+连接设备+数字解决方案组成。检索了Embase（2020年12月6日）。总共纳入171项研究进行数据提取和证据综合：96项研究与VHC疗效相关，75项研究与HF中的人工智能相关。此外，在检索关于在现实环境中扩大HF的VHC解决方案规模的研究时纳入了15篇出版物。以报告的显著结果数量衡量，最成功的VHC干预措施是那些旨在降低再住院率的措施。就相对成功率而言，两种最有效的干预措施在其主要终点针对患者自我护理和全因住院就诊。在本综述确定的VHC三类（远程监测、远程患者管理和患者自我赋能）中，远程患者管理解决方案中的综合方法在降低HF患者再入院率和总体住院就诊方面表现最佳。鉴于VHC技术产生的数据量增加，人工智能（AI）正在作为一种工具进行研究，以辅助在初级诊断、识别疾病表型和预测治疗结果的背景下进行决策。目前，大多数AI算法是使用在诊所收集的数据开发的，只有少数研究在VHC背景下部署AI。在预测HF结果方面报告了大多数成功案例。由于HF中的VHC领域相对较新且仍在变化，这不是一篇涵盖该领域所有已发表研究的典型系统综述。尽管遵循了此类综述的标准方法，但本综述的性质是定性的。主要目标是总结最有前景的结果并确定潜在的研究方向。
