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.

项目总结/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测，以指导和治疗变化， 有发生急性失代偿性心力衰竭（ADHF）风险的患者。失代偿性HF的早期检测通常 依赖于监测体重的增加，但体重本身并不能准确地衡量液体的积累，预测 HF恶化。一种可以测量生命体征（包括心率和呼吸率）的设备， 液体状态（使用胸生物阻抗）和心律，可以允许更准确地识别 慢性心力衰竭患者急性失代偿的早期阶段。来自牛津大学的多学科团队 马萨诸塞州（UMass）阿默斯特大学、马萨诸塞州医学院和康涅狄格大学提议开发 一种新型装置，用于对有失代偿风险的HF患者进行家庭监测。核心假设 是一种创新生物阻抗和心电图监测仪， 嵌入可穿戴背心中的生物阻抗电极，与智能手机和云服务器结合使用， 将持续收集、传输和监控关键生理数据。运行决策支持算法的设备 将分析这些数据，以确定可能通过以下方法缓解的紧急HF失代偿患者 立即就医。生物阻抗和心电图设备（BED）附在背心背面 每天一次从背心电极收集5分钟的数据，计算生物阻抗，并有故障- 容许评估电路，以实现可靠的数据收集。收集的数据包括心律，一些 将发送生命体征、胸内积液测量结果和有关数据可靠性的信息 通过蓝牙低功耗（BLE）无线通信从床上连接到智能手机。这些数据， 患者的体重，以及应用中注意到的症状和体征，如呼吸急促或较低的 腿部肿胀，将由患者记录，并通过4/5G/WiFi移动的网络发送到云服务器。这些 数据将用于开发一种可靠的临床决策支持算法，该算法可准确检测早期ADHF。这 项目旨在：A1.1）开发一种可重复使用的炭黑和聚二甲基硅氧烷（CB/PDMS）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）开发具有故障的床- A1.3）开发智能手机应用程序，包括使用 收集生理信息，用于自主早期ADHF检测; A2.1）建立云基础设施 这允许从家庭内设置沿着的数据收集上述数据 可靠性监测;以及A2.2）在前瞻性招募的 已知HF患者的队列研究。临床研究将针对处于高风险的不同HF人群 关于ADHF一个成功的项目将导致远程医疗原型的设计、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。