Non-invasive Condition Monitoring of Ventricular Assistive Devices Using Automated Advanced Acoustic Methods

使用自动化先进声学方法对心室辅助装置进行无创状态监测

• 批准号: 10629554
• 负责人: Jason R Kolodziej
• 金额: $ 14.8万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629554
• 关键词: Academic Medical Centers; Acoustics; Address; Algorithms; Anastomosis - action; Aorta; Aortic Valve Insufficiency; Apical; Cannulas; Cardiac Output; Cardiovascular system; Categories; Chest; Classification; Clinical; Clinical Data; Complex; Computer software; Computers; Congestive Heart Failure; Custom; Data; Data Set; Devices; Diagnosis; Diagnostic; Disease; Echocardiography; Electrocardiogram; Electronics; Engineering; Environment; Equipment; Equipment Malfunction; Faculty; Failure; Frequencies; Functional disorder; Funding; Future; Generations; Goals; Health; Health Personnel; Health Status; Heart Rate; Heart Sounds; Heart Transplantation; Heart Valves; Hour; Human; Image; Implant; Institutional Review Boards; Knowledge; Lead; Left; Left Ventricular Function; Machine Learning; Mechanics; Medical Research; Medicine; Methods; Mitral Valve; Mitral Valve Insufficiency; Mitral Valve Stenosis; Monitor; Motor; Outcome; Output; Patient Care; Patients; Performance; Personal Satisfaction; Physiologic pulse; Procedures; Process; Protocols documentation; Pump; Research; Research Personnel; Research Project Grants; Running; Self-Help Devices; Severities; Signal Transduction; Speed; Stenosis; Stethoscopes; Students; Symptoms; System; Systemic hypertension; Techniques; Technology; Testing; Time; Tissues; Training; Transesophageal Echocardiography; Ultrasonography; Ventricular; Work; aortic valve; data fusion; design; digital; doctoral student; experience; health training; heart rhythm; hemodynamics; improved; innovation; input device; invention; machine learning model; mechanical device; monitoring device; motor disorder; novel; novel strategies; pressure; professor; research clinical testing; research study; signal processing; sound; symptomatology; transfer learning; ultrasound; undergraduate student; ventricular assist device; voltage

PROJECT SUMMARY The primary objective of this research project is to create a new approach to identifying the state-of-health of implanted ventricular assist devices (VAD) using nothing more than a smart electronic stethoscope and a single lead ECG. VADs are pumps permanently implanted into patients with poor left ventricular function. They run at a set speed with almost no internal automatic speed adjustments. For this reason, it is a complex problem for healthcare providers to diagnose VAD patients who present with symptoms that may or may not be due to reduced pump performance. The current state-of-the-art requires very highly trained healthcare providers with access to expensive clinical equipment and sometimes requires invasive or partially invasive procedures just to determine if the symptoms might be due to the VAD device, let alone what the specific problem is. We hypothesize that through the use of advanced signal processing and machine learning techniques, we can classify a VAD patient’s health status as it pertains to the VAD’s mechanical performance and hemodynamic flow into normal and dysfunctional states. To test this hypothesis, we will modify an existing hemodynamic simulator that creates not only time-varying pressure to the VAD input and output, but also has heart valves that create an acoustic signature similar to human valves, and can be underfilled to the point of ventricular collapse (all conditions similar to those commonly seen in a VAD-rich clinical environment). We will specifically address most VAD failure modes: (1) underfilling of the VAD inflow relative to the pump speed, (2) excessive afterload due to either systemic hypertension or the presence of occlusions within the VAD flow circuit, (3) presence of significant valvular disease (aortic regurgitation, mitral regurgitation, and mitral stenosis), and (4) the effect of heart rate and rhythm on VAD performance. Various severity levels of these conditions will be seeded in the simulator, and the audio signature of the VAD pump and associated native heart sounds will be recorded. The digital heart sound measurements will be processed using novel algorithms to be developed in this protocol to understand the root physical cause, and compared to the VAD function/dysfunction mode being tested. As the project proceeds actual patient data, collected by the research team in 2018 in a non-invasive manner (stethoscope, ECG, and ultrasound), will be compared versus the simulator results under the same conditions. If the proposed research is successful there is the opportunity to: (1) improve patient care by detecting common clinical conditions at an early stage, (2) avoid the need for advanced diagnostics in some cases, (3) allow medical personnel to test various VAD settings to optimize output without the need for advanced diagnostics, and (4) allow for remote diagnostics of VAD problems. In addition to Dr. Jason Kolodziej, Dr. Steven Day, Dr. Linwei Wang, Dr. Karl Schwarz, Dr. Igor Gosev, and Dr. Michael Richards, the research team will consist of three senior design teams (15 UG students), three to six undergraduate students, and a Ph.D. student.

项目概要 该研究项目的主要目标是创建一种新方法来确定健康状况 植入式心室辅助装置（VAD）仅使用智能电子听诊器和 单导联 ECG 是永久植入左心室功能不良患者体内的泵。 以设定速度运行，几乎没有内部自动速度调整，因此，这是一个复杂的问题。 供医疗保健提供者诊断 VAD 患者，这些患者的症状可能是或可能不是由以下原因引起的 当前最先进的技术需要训练有素的医疗保健提供者。 获得昂贵的临床设备，有时需要侵入性或部分侵入性手术才能 确定症状是否可能是由 VAD 设备引起的，更不用说具体问题是什么。 我们通过使用先进的信号处理和机器学习来探索这一点 技术，我们可以对 VAD 患者的健康状况进行分类，因为它与 VAD 的机械状态有关 表现和血流动力学进入正常和功能障碍状态。 为了检验这个假设，我们将修改现有的血流动力学模拟器，该模拟器不仅创建时变的 压力到 VAD 输入和输出，但也有心脏瓣膜，可以产生类似于人类的声学特征 瓣膜，并且可以填充不足到心室塌陷的程度（所有情况类似于常见的情况 在富含 VAD 的临床环境中）我们将专门解决大多数 VAD 失败模式：（1）填充不足。 VAD 流量相对于泵速，(2) 由于全身性高血压或系统性高血压导致后负荷过大 VAD 血流回路中存在闭塞，(3) 存在明显的瓣膜疾病（主动脉瓣疾病） 反流、二尖瓣反流和二尖瓣狭窄），以及（4）心率和节律对 VAD 的影响 这些条件的各种严重程度将被植入模拟器和音频签名中。 VAD 泵和相关的本地心音测量将被记录。 将使用本协议中开发的新颖算法进行处理，以了解根本的物理原因， 并与正在测试的 VAD 功能/功能障碍模式进行比较 随着项目继续进行实际患者数据， 研究团队于2018年以非侵入性方式（听诊器、心电图和超声波）收集的数据，将被 与相同条件下的模拟器结果进行比较。 如果拟议的研究成功，就有机会：（1）通过检测来改善患者护理 早期常见的临床状况，(2) 在某些情况下避免需要高级诊断，(3) 允许医务人员测试各种 VAD 设置以优化输出，而无需高级操作 (4) 除了 Jason Kolodziej 博士之外，Steven 博士还可以远程诊断 VAD 问题。 Day，王林伟博士、Karl Schwarz博士、Igor Gosev博士和Michael Richards博士，研究团队将包括 由三个高级设计团队（15名本科生）、三到六名本科生和一名博士生组成。