In-home wearable system to detect early-stage decompensation in heart failure patients

家用可穿戴系统可检测心力衰竭患者的早期失代偿

• 批准号: 10676951
• 负责人: Yeonsik Noh
• 金额: $ 41.88万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676951
• 关键词: Acute; Adhesives; Affect; Age; Algorithms; American; Back; Bluetooth; Body Weight; Carbon Black; Cardiac; Cardiovascular Diseases; Cellular Phone; Chest; Chronic; Clinical; Clinical Research; Cloud Computing; Cohort Studies; Collection; Congestive Heart Failure; Connecticut; Data; Data Collection; Detection; Development; Devices; Diuretics; Dryness; Early Diagnosis; Early identification; Elderly; Electrocardiogram; Electrodes; Feasibility Studies; Feedback; Frustration; Gel; Goals; Grant; Heart Rate; Heart failure; Home; Hospital Costs; Hospitalization; Hour; Human; Impairment; Information Storage; Intervention; Lead; Learning; Leg; Light; Liquid substance; Lung; Machine Learning; Massachusetts; Measurement; Measures; Medical; Medical Staff; Monitor; Morbidity - disease rate; Morphologic artifacts; Noise; Oral; Patients; Performance; Persons; Pharmaceutical Preparations; Physiological; Population; Quality of life; Questionnaires; Reporting; Risk; Risk Factors; Running; SMART health; Shortness of Breath; Signal Transduction; Signs and Symptoms; Site; Skin; Spectrum Analysis; Staff Work Load; Swelling; Symptoms; System; Testing; Time; Titrations; Training; Universities; Weight; Weight Gain; clinical decision support; cloud based; cloud platform; connected health; data acquisition; data communication; data exchange; data quality; design; detection platform; electric impedance; flexibility; heart rate variability; heart rhythm; high risk; hospital readmission; improved; innovation; interest; machine learning algorithm; machine learning model; medical attention; medical schools; mobile application; mortality; multidisciplinary; new technology; novel; patient oriented; polydimethylsiloxane; pressure; prevent; programs; prospective; prototype; recruit; research clinical testing; respiratory; sensor; sex; signal processing; smartphone application; technology development; telehealth; transmission process; trend analysis; usability; wearable device; web site; wireless communication; wireless fidelity

Project Summary/Abstract In the U.S., heart failure (HF) affects over 6 million people and is one of the most common causes of hospitalization. New technologies are needed to enable in-home monitoring to guide and treatment changes for patients at risk of developing Acute Decompensated HF (ADHF). Early detection of decompensated HF often relies on monitoring weight gain, but weight alone does not accurately gauge the fluid accumulation that predicts worsening of HF. A device that can measure vital signs (including heart rates and respiratory rates), intrathoracic fluid status (using thoracic bioimpedance), and heart rhythm, may allow for more accurate identification of the early stages of acute decompensation in chronic HF patients. The multidisciplinary team from the University of Massachusetts (UMass) Amherst, UMass Medical School, and the University of Connecticut proposes to develop a novel device for in-home monitoring of HF patients who are at risk of decompensation. The central hypothesis is that an innovative bioimpedance and electrocardiogram monitor with re-usable, non-wetted, and flexible bioimpedance electrodes embedded in a wearable vest, used in conjunction with a smartphone and cloud server, will continuously collect, transmit, and monitor key physiologic data. Devices running decision-support algorithms will analyze these data to identify patients with an emergent HF decompensation that may be mitigated with prompt medical attention. A Bioimpedance and Electrocardiogram Device (BED) attached to the back of the vest collects 5 minutes of data once a day from the vest electrodes, calculates bioimpedance, and has fault- tolerant assessment circuits to enable reliable data collection. Collected data consisting of heart rhythm, some vital signs, intrathoracic fluid accumulation measurements, and information about the data reliability will be sent to a smartphone from the BED via Bluetooth-Low-Energy (BLE) wireless communication. This data, together with the patient's weight, and symptoms and signs noted in the application, such as shortness of breath or lower leg swelling, will be recorded by patients and be sent to a cloud server via 4/5G/WiFi mobile networks. These data will be used to develop a robust clinical decision support algorithm that accurately detects early ADHF. This project aims to: A1.1) develop a wearable vest with reusable carbon-black and polydimethylsiloxane (CB/PDMS) electrodes that capture 3-channel bioimpedance and electrocardiogram data; A1.2) develop a BED with fault- tolerant circuit; A1.3) develop a smartphone application, to include a machine learning algorithm that uses the collected physiological information for autonomous early ADHF detection; A2.1) establish a cloud infrastructure that allows for the collection of the aforementioned data from the in-home setting along with associated data reliability monitoring; and A2.2) evaluate the performance and usability of the system in a prospectively recruited cohort study of patients with known HF. The clinical study will target a diverse HF population that are at high risk for ADHF. A successful project will result in the design, testing, and clinical evaluation of a prototype telehealth monitoring system to collect real-time data about cardiac risk factors for people with HF.

项目摘要/摘要 在美国，心力衰竭（HF）影响超过600万人，是最常见的原因之一 住院。需要新技术来实现家庭监控以指导和治疗更改 患有急性代偿HF（ADHF）的风险。经常早期检测到代偿性HF 依靠监测体重增加，但仅体重不能准确评估预测的流体积累 HF恶化。可以测量生命体征（包括心率和呼吸率）的设备 流体状态（使用胸腔生物阻抗）和心律，可以更准确地识别 慢性HF患者的急性代偿性早期阶段。大学的多学科团队 马萨诸塞州（UMass）Amherst，UMass医学院和康涅狄格大学提议开发 一种用于家庭监测有代表性风险的HF患者的新型设备。中心假设 是具有可重复使用，无润湿和灵活的创新生物阻抗和心电图监视器 嵌入在可穿戴背心中的生物阻抗电极与智能手机和云服务器一起使用， 将连续收集，传输和监视关键的生理数据。运行决策支持算法的设备 将分析这些数据，以识别出可能会减轻HF代理的出现的患者 迅速医疗护理。附在背部背面的生物阻抗和心电图设备（床） 每天从背心电极收集5分钟的数据，计算生物阻抗，并具有断层 - 耐受性评估电路可以启用可靠的数据收集。收集的数据包括心律，有些 将发送生命体征，瞬间流体累积测量以及有关数据可靠性的信息 通过蓝牙 - 低能（BLE）无线通信到床上的智能手机。这些数据一起 患者的体重以及应用中指出的症状和迹象，例如呼吸急促或较低 腿部肿胀，将由患者记录，并通过4/5G/WiFi移动网络发送到云服务器。这些 数据将用于开发可准确检测早期ADHF的强大临床决策支持算法。这 项目的目的是：A1.1）开发具有可重复使用的碳黑色和聚二甲基硅氧烷（CB/PDMS）的可穿戴背心 捕获3通道生物阻抗和心电图数据的电极； A1.2）开发带断层的床 - 耐受电路； A1.3）开发一个智能手机应用程序，以包括使用机器学习算法 收集的生理信息以进行自动及早ADHF检测； A2.1）建立云基础架构 这允许从家庭设置收集上述数据以及相关数据 可靠性监视；和A2.2）评估该系统在前瞻性招聘中的性能和可用性 已知HF患者的队列研究。临床研究将针对高风险的不同HF人群 对于ADHF。一个成功的项目将导致原型远程医疗的设计，测试和临床评估 监测系统以收集有关HF患者心脏风险因素的实时数据。