Inconspicuous Daily Monitoring to Reduce Heart Failure Hospitalizations

不显眼的日常监测可减少心力衰竭住院率

• 批准号: 10413910
• 负责人: Linwei Wang
• 金额: $ 59.9万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-18 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10413910
• 关键词: Address; Admission activity; Adverse event; American; Architecture; Awareness; Behavior; Blood; Blood Pressure; Cardiac Output; Cardiovascular Diseases; Cardiovascular system; Caring; Cause of Death; Cessation of life; Clinical; Clinical Data; Clinical Trials; Computer software; Computerized Medical Record; Control Groups; Custom; Data; Data Scientist; Data Set; Deterioration; Early Intervention; Electrocardiogram; Engineering; Ensure; Equilibrium; Generations; Habits; Health Personnel; Healthcare Systems; Heart Rate; Heart failure; Home; Hospitalization; Hospitals; Instruction; Interdisciplinary Study; Intervention; Intervention Studies; Interview; Label; Lead; Learning; Length of Stay; Machine Learning; Measurement; Measures; Modeling; Monitor; Myocardium; Patients; Pattern; Pharmaceutical Preparations; Physicians; Physiological; Predictive Value; Preventive care; Probability; Provider; Quality of life; Research; Risk; Slide; System; Techniques; Technology; Telephone; Testing; Time; Training; Update; Visit; Weight; Work; base; clinically relevant; cohort; compliance behavior; cost; data streams; data visualization; heart rate variability; hospitalization rates; improved; innovative technologies; machine learning model; model development; multidimensional data; novel; predictive modeling; provider intervention; recurrent neural network; remote patient monitoring; success; trend

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.

项目摘要/摘要 在家监测技术有可能通过启用医疗系统来改变医疗保健系统 从反应性护理过渡到主动和预防保健。这对于心血管尤其重要 疾病（CVD）；全球死亡的主要原因。心力衰竭（HF），一种以A为特征的CVD 心肌减弱，每年影响约650万美国人，新病例超过960,000例。 HF估计每年损失307亿美元，预计到2030年将增加127％，至697亿美元。 由于住院的大约80％与HF相关的总成本的80％，有机会 通过远程患者监测来降低住院率，降低HF的成本。自病人以来 症状的意识经常落后于恶化，成功跟踪家庭生理变化的是 早期干预策略的关键组成部分。经典的家庭监控方法，例如 血压和体重监测的成功有限，患者依从性是主要障碍 减少住院。 这项研究的核心假设是心力衰竭患者的住院率和住院时间 通过不起眼的家庭监测和早期干预可以显着降低。这项研究 将利用一种高度创新的技术来进行家庭内部的日常监测；完全集成 厕所座椅（适合）。适合座椅会自动捕获全面的心血管评估 在家，同时确保长期的患者依从性。由工程师组成的多学科研究团队， 医师，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，该系统为医疗保健提供者提供病人的预警 使用在房屋中捕获的拟合系统测量结果恶化。该系统的成功将是 通过心力衰竭患者的家庭内临床试验进行评估。 特定目标1试图从HF患者内部创建学习数据集和数据可视化架构 生理数据，感知的健康和不良事件。合适的座椅将部署为90天的家庭 研究200例HF患者，患者通过自定义应用捕获了患者的健康和活动。 这种生理，健康和活动数据将与电子医学的不良事件结合 记录以创建一个集成数据集，以进行回顾性分析和警报模型开发。在AIM 2中 将使用新颖机器创建全因住院的早期警报的自动预测模型 学习技巧。目标3的目的是证明不起眼的内部监测和 早期干预可以减少200名HF患者的第二次队列中的住院治疗。我们假设 基于拟合的综合警报系统将减轻全因住院的负担，并将改善 患者的生活质量。