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

项目摘要/摘要 在美国，心力衰竭(HF)影响着600多万人，是导致心脏病的最常见原因之一 住院治疗。需要新技术来实现家庭监测，以指导和治疗以下疾病的变化 有患急性失代偿性心衰(ADHF)风险的患者。失代偿性心衰的早期发现往往 依赖于监测体重增加，但仅靠体重并不能准确衡量预测的液体积累 心衰恶化。一种可以测量生命体征(包括心率和呼吸频率)的设备，胸腔内 体液状态(使用胸腔生物阻抗)和心率，可以更准确地识别 慢性心力衰竭患者急性失代偿早期。来自密歇根大学的多学科团队 马萨诸塞州阿默斯特大学、马萨诸塞州医学院和康涅狄格大学提议开发 一种用于家庭监护面临失代偿风险的心力衰竭患者的新设备。中心假说 是一种创新的生物阻抗和心电监护仪，具有可重复使用、不潮湿和灵活的特点 植入可穿戴背心的生物阻抗电极，与智能手机和云服务器一起使用， 将持续收集、传输和监测关键生理数据。运行决策支持算法的设备 将分析这些数据以确定患有紧急心衰失代偿的患者，这些患者可能会得到缓解 及时就医。附在背心背面的生物阻抗和心电图仪(床) 每天从背心电极收集5分钟的数据，计算生物阻抗，并出现故障- 容错评估电路，以实现可靠的数据收集。收集的数据包括心率，一些 生命体征、胸腔积液测量和有关数据可靠性的信息将被发送 通过蓝牙低能耗(BLE)无线通信从床上连接到智能手机。这些数据加在一起 患者的体重，以及在应用程序中注意到的症状和体征，如呼吸急促或更低 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 数据将被用来开发一种强大的临床决策支持算法，准确地检测早期ADHF。这 项目目标是：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 采集3通道生物阻抗和心电数据的电极；a1.2)开发有故障的床- 容错电路；a1.3)开发智能手机应用程序，以包括使用 为自主早期ADHF检测收集生理信息；a2.1)建立云基础设施 这允许从家庭设置中收集前述数据以及相关数据 可靠性监测；以及a2.2)评估系统的性能和可用性 已知心力衰竭患者的队列研究。这项临床研究将针对高危的不同类型的心力衰竭人群。 用于ADHF。一个成功的项目将导致远程保健原型的设计、测试和临床评估 用于收集心力衰竭患者心脏危险因素的实时数据的监测系统。

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.