Inconspicuous Daily Monitoring to Reduce Heart Failure Hospitalizations

项目摘要/摘要 家庭监控技术有可能通过使医疗保健系统 从反应性护理向主动性和预防性护理过渡。这对心血管疾病尤其重要。 疾病(心血管疾病)；世界范围内的主要死亡原因。心力衰竭(HF)是一种心血管疾病，其特征是 心肌衰弱，影响大约650万美国人，每年新增病例超过96万例。 据估计，高频每年花费美国307亿美元，预计到2030年将增长127%，达到697亿美元。 由于与心力衰竭相关的总费用中约有80%是由于住院，因此有机会 通过远程患者监护降低住院率，从而降低心力衰竭的成本。自患者以来 对症状的认识往往滞后于病情恶化，成功跟踪家庭中的生理变化是 这是早期干预战略的关键组成部分。家庭监控的经典方法，例如 血压和体重监测的成效有限，患者的坚持被认为是主要障碍 减少住院率。 这项研究的中心假设是心力衰竭患者的住院率和住院时间 通过不显眼的家庭监测和早期干预可以显著减少。这项研究 将利用高度创新的技术进行心血管家庭日常监测；完全集成 马桶座圈(适合)。Fit Seat自动捕获全面的心血管评估 家，同时确保患者长期坚守。一个由工程师组成的多学科研究团队， 医生、高级实践提供商(APP)、数据科学家、生物统计学家、设计师和软件 开发人员将推出一种自动化系统，为医疗保健提供者提供患者的早期预警 使用在家中捕获的FIT系统测量来恶化。这一系统的成功将是 通过心力衰竭患者的家庭临床试验进行评估。 具体目标1寻求从家庭中的心衰患者创建学习数据集和数据可视化架构 生理数据、感知健康和不良事件。Fit座椅将在家中部署90天 对200名心力衰竭患者的研究，通过定制应用程序捕获患者的感知健康和活动。 这些生理、健康和活动数据将与来自电子医疗的不良事件相结合 记录以创建集成数据集，用于回溯分析和警报模型开发。在Aim 2中，一个 将使用新机器创建全原因住院早期警报的自动预测模型 学习技巧。目标3目的是证明不显眼的家庭监控和 早期干预可以减少第二组200名心力衰竭患者的住院率。我们假设 集成的基于FIT的警报系统将减轻所有原因住院的负担，并将改善 患者的生活质量。

Project Summary/Abstract In-home monitoring technologies have the potential to transform the healthcare system by enabling the transition from reactive care to proactive and preventive care. This is especially important for cardiovascular disease (CVD); the leading cause of death worldwide. Heart failure (HF), a type of CVD characterized by a weakened heart muscle, impacts approximately 6.5 million Americans with over 960,000 new cases each year. HF costs the US an estimated $30.7 billion annually and is expected to increase 127% to $69.7 billion by 2030. With approximately 80% of the total cost associated with HF due to hospitalization, there is an opportunity to reduce the cost of HF by lowering hospitalization rates through remote patient monitoring. Since patient awareness of symptomology often lags deterioration, successfully tracking physiologic changes in the home is a critical component of an early intervention strategy. Classical approaches to in-home monitoring, such as blood pressure and weight monitoring have had limited success, with patient adherence cited as major barrier to reducing hospitalizations. The central hypothesis of this research is that hospitalization rates and duration of stay for heart failure patients can be significantly reduced through inconspicuous in-home monitoring and early intervention. This research will leverage a highly innovative technology for cardiovascular in-home daily monitoring; the fully integrated toilet seat (FIT). The FIT seat automatically captures a comprehensive cardiovascular assessment in the home, while ensuring long-term patient adherence. A multidisciplinary research team, comprised of engineers, physicians, advanced practice providers (APP), data scientists, biostatisticians, designers, and software developers, will advance an automated system that provides health care providers with early warning of patient deterioration using the FIT system measurements captured in the home. The success of this system will be evaluated through an in-home clinical trial of heart failure patients. Specific Aim 1 seeks to create a learning dataset and data visualization architecture from HF patient in-home physiologic data, perceived wellness, and adverse events. The FIT seat will be deployed for a 90-day in-home study of 200 HF patients, with patient perceived wellness and activity captured through a custom application. This physiologic, wellness, and activity data will be combined with adverse events from the electronic medical record to create an integrated dataset for retrospective analysis and alert model development. In Aim 2, an automated prediction model for early alert of all-cause hospitalizations will be created using novel machine learning techniques. The objective of Aim 3 is to demonstrate that inconspicuous in-home monitoring and early intervention can reduce hospitalizations in a second cohort of 200 HF patients. We hypothesize that the integrated FIT-based alert system will reduce the burden of all-cause hospitalization and will improve the quality of life for patients.