Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 multicenter implementation study

背景心力衰竭(HF)是一个主要的健康负担，占HF医疗费用的80% 可归因于住院治疗。减少心衰再入院是退伍军人管理局的一个主要战略目标。我们的初步研究 证明了使用小型可穿戴传感器进行多变量生理遥测具有很高的 依从率，并提供准确的早期检测即将再次入院的心力衰竭恶化。 我们现在建议在5个退伍军人医疗中心实施非侵入性远程监控。 意义/影响尽管治疗取得了进展，但因心力衰竭恶化而住院的情况仍然存在 很普遍，而且成本高昂。准确和及时地检测到早期心力衰竭恶化可能是一种途径 减少心衰患者的再入院。 创新这项研究将制定非侵入性远程监测的实施策略 预测性分析和对来自分析的临床警报的算法处理 站台。结果将是临床警报和干预之间的可靠联系，可以影响 患者的临床结果。对设备警报的算法响应将是一个持续的主题 确认和更新是学习保健系统概念的一部分。这将允许集成 利用电子健康档案，优化和规范应对流程和 减少警觉疲劳。此外，我们还将评估患者和提供商对使用Remote 监测以指导HF治疗，以及这一方法对关键临床结果的影响。 具体目标1.将远程监护落实到心衰护理的临床工作流程中。 目标1a。设计非侵入式远程监控的实施策略和算法 对预测分析平台生成的临床警报做出响应。 目标1b。评估实施结果，包括临床医生和患者的看法和采用 使用非卧床远程监测数据。 目的2.对慢性心衰患者进行无创性远程监测的可行性研究。 目标2a。定义将为非侵入性远程试验的关键试验提供信息的关键特征 旨在减少心衰患者再住院和提高生活质量的监测。 目标2b。确定与高频实施和非侵入性远程监测相关的成本。 方法我们将使用I-PARiHS框架和三个 实施阶段：1)实施干预计划；2)试点形成性评估 在2个先锋站点实施；以及3)实施保真度监测。我们将招收240人 在5个参与的退伍军人管理局中心因心力衰竭恶化而住院的患者。所有研究对象都将收到 监测试剂盒，出院后将使用90天。受试者将以1：1的比例随机分配 干预臂，临床医生将收到临床警报并遵循响应算法 修改心力衰竭治疗或建议紧急门诊/急诊室就诊，并向控制臂， 将从传感器收集信息，但不会生成临床警报或 与供应商沟通。研究结果将包括随机选择的患者的比例 满足至少一个警报的算法标准，即远程监视器的使用时间比例 功能正常、心力衰竭住院率、住院时间和健康相关生活质量。 下一步，这项工作将为设计非侵入性远程监测的关键试验提供信息，旨在 减少再住院，提高心衰患者的生活质量。制定的实施战略 这项研究不仅可用于实施所测试的监测方法，还可用于其他 心力衰竭和其他慢性健康状况的远程监测战略。

Background Heart failure (HF) represents a major health burden, with 80% of the HF health care costs attributable to hospitalizations. Reducing HF readmissions is a major VA strategic goal. Our pilot study demonstrated that multivariate physiological telemetry using a small wearable sensor has a high compliance rate and provides accurate early detection of impending readmission for HF exacerbation. We now propose to implement non-invasive remote monitoring at 5 VA medical centers. Significance/Impact Despite treatment advances, hospitalizations for HF exacerbation remain prevalent and costly. Accurate and timely detection of incipient HF exacerbation may be one path to reducing HF readmissions. Innovation The study will develop an implementation strategy for noninvasive remote monitoring with predictive analytics and an algorithmic treatment response to clinical alerts coming from the analytical platform. The result will be a reliable link between the clinical alert and an intervention that can affect the clinical outcome of the patient. Algorithmic response to the device alert will be a subject of ongoing validation and update as part of the learning health-care system concept. This will allow for integration with the electronic health record, optimization and standardization of the response process and decrease alert fatigue. Furthermore, we will evaluate patient and provider attitudes toward using remote monitoring to guide HF therapy, as well as the impact of this approach on key clinical outcomes. Specific Aims Aim 1. Implement remote monitoring into the clinical workflow of HF care. Aim 1a. Design implementation strategies for non-invasive remote monitoring and algorithmic response to clinical alerts generated by the predictive analytics platform. Aim 1b. Evaluate implementation outcomes, including clinician and patient perceptions and adoption of the use of ambulatory remote monitoring data. Aim 2. Conduct a feasibility study of non-invasive remote monitoring in chronic HF. Aim 2a. Define key characteristics that will inform design of a pivotal trial of non-invasive remote monitoring aimed at reducing rehospitalization and improving quality of life in HF. Aim 2b. Identify costs associated with implementation and non-invasive remote monitoring in HF. Methodology We will design implementation processes using the i-PARiHS framework and three implementation phases: 1) implementation intervention planning; 2) formative evaluation of pilot implementation at 2 vanguard sites; and 3) Implementation fidelity monitoring. We will enroll 240 patients hospitalized for HF exacerbation at 5 participating VA centers. All study subjects will receive the monitoring kit, which will be used for 90 days after discharge. Subjects will be randomized 1:1 to an intervention arm, where clinicians will be notified of clinical alerts and will follow response algorithm to modify HF treatment or recommend urgent clinic visit/emergency room visit, and to the control arm, where information from the sensors will be collected, but clinical alerts will not be generated or communicated to providers. Study outcomes will include the proportion of randomized patients who meet the algorithm’s criteria for at least one alert, the proportion of time the remote monitor is in use and functioning properly, HF hospitalization rate, hospital stay length, and health-related quality of life. Next Steps This work will inform design of a pivotal trial of non-invasive remote monitoring aimed at reducing rehospitalization and improving quality of life in HF. Implementation strategies developed in this study may be used not only for implementation of the monitoring approach tested, but also for other remote monitoring strategies in HF and additional chronic health conditions.