Inconspicuous Daily Monitoring to Reduce Heart Failure Hospitalizations

项目概要/摘要 家庭监测技术有可能通过使医疗保健系统能够 从被动护理向主动和预防性护理转变。这对心血管疾病尤其重要。 心血管疾病（CVD）;全球死亡的主要原因。心力衰竭（HF）是一种CVD，其特征在于 心肌衰弱影响了大约650万美国人，每年有超过96万新病例。 HF每年花费美国约307亿美元，预计到2030年将增加127%至697亿美元。 由于住院治疗约占HF相关总费用的80%，因此有机会 通过远程患者监测降低住院率，从而降低HF的成本。因为患者 对生理学的认识往往滞后于恶化，成功地跟踪家中的生理变化， 这是早期干预战略的重要组成部分。传统的家庭监控方法，例如 血压和体重监测的成功有限，患者依从性被认为是主要障碍 减少住院治疗。 本研究的中心假设是心力衰竭患者的住院率和住院时间 可以通过不显眼的家庭监测和早期干预来显著减少。本研究 将利用高度创新的技术进行心血管家庭日常监测; 马桶座圈（FIT）FIT座椅可自动捕捉全面的心血管评估， 家庭，同时确保长期的患者依从性。一个由工程师组成的多学科研究团队， 医生、高级实践提供者（APP）、数据科学家、生物统计学家、设计师和软件 开发人员将推进一个自动化系统，为医疗保健提供者提供患者早期预警。 使用在家中捕获的FIT系统测量来确定劣化。这个系统的成功将是 通过心力衰竭患者的家庭临床试验进行评估。 具体目标1旨在从HF患者家中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座椅将部署在90天的家中 200名HF患者的研究，通过自定义应用程序捕获患者感知的健康和活动。 该生理、健康和活动数据将与来自电子医疗设备的不良事件相结合。 记录以创建用于回顾性分析和警报模型开发的集成数据集。在目标2中， 将使用新型机器创建全因住院早期警报的自动预测模型 学习技巧目标3的目的是证明不显眼的家庭监测和 早期干预可以减少第二组200名HF患者的住院率。我们假设 基于FIT的综合警报系统将减少全因住院的负担，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.