Clinical feasibility of a non-invasive, low-cost wearable for measuring air trapping in COPD

用于测量 COPD 空气滞留的非侵入性低成本可穿戴设备的临床可行性

基本信息

批准号：
10080272
负责人：
Maria Artunduaga
金额：
$ 35万
依托单位：
SAMAY, INC.
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-20 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10080272
关键词：
Acoustics Activities of Daily Living Adult Air Algorithmic Analysis Algorithms American Asthma Bluetooth Bronchoconstriction Caring Cellular Phone Chronic Bronchitis Chronic Obstructive Airway Disease Clinic Clinical Clinical Data Clinical Research Clinical Trials Computer software Cross-Sectional Studies Data Data Analyses Detection Deterioration Development Device Designs Devices Diagnosis Disease Progression Dyspnea Early Intervention Edema Emergency department visit Exhalation Exposure to Flare Frequencies Functional disorder Future General Hospitals Goals Health Health Care Costs Heart Rate Home environment Hospitalization Hospitals Humidity Individual Inhalators Intervention Laboratories Lung Machine Learning Measurement Measures Medical Medical History Medical center Methods Military Personnel Miniaturization Minor Monitor Morbidity - disease rate Mucous body substance Noise Oral Oxygen saturation measurement Particulate Patients Pharmaceutical Preparations Phase Physicians Physiology Plethysmography Population Pulmonary Emphysema Pulmonary function tests Pulse Oximetry Quality of life Questionnaires Refractory Regulatory Pathway Reporting Respiratory Muscles Respiratory physiology Risk Running San Francisco Secure Signal Transduction Small Business Technology Transfer Research Smoker Spirometry Standardization Symptoms System Technology Temperature Testing Therapeutic Intervention Time Translating Ultrasonography Veterans Whole Body Plethysmography Wireless Technology X-Ray Computed Tomography accurate diagnosis air monitoring base cloud based cohort cost design experience improved indexing machine learning algorithm meter microphone miniaturize mobile application mortality novel ozone exposure prevent prototype recruit respiratory screening sensor smartphone Application software development success tool usability user-friendly wearable device

项目摘要

ABSTRACT Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of hospitalization in the US. Exacerbations - a worsening or “flare up” of symptoms - cause most COPD hospitalizations. Since most exacerbations can be treated with changes of inhalers and/or oral medications, at-home detection of lung function deterioration may facilitate earlier intervention and help delay or prevent hospitalizations. The Standards of Care for monitoring lung function are spirometry, plethysmography, and CT scan. However, these are expensive methods and unsuited for continuous monitoring or at-home use. Various patient self-monitoring approaches have been tried, for example, pulse oximetry, respiratory rate monitoring, and peak flow metering, but their efficacy in reducing hospitalizations has been limited. A common finding for all forms of COPD is air trapping, defined, as an abnormal increase in the volume of air remaining in the lungs after exhalation is complete. A body of evidence definitively shows that air trapping increases during exacerbations and decreases when exacerbations resolve. Recent reports show that air trapping is an earlier harbinger of deteriorating lung function than spirometric changes, and can be measured by low-frequency ultrasound (1-40 kHz). Acoustic monitoring of air trapping could provide clinicians with a non- invasive tool when medical intervention is needed to avoid unnecessary ER visits and hospitalizations. Respira Labs has developed a low-cost, non-invasive, acoustic-based wearable device that can continually monitor lung resonance: Sylvee. The device uses known acoustic-based technology with machine- learning algorithms to detect minor changes in lung resonance, which our preliminary results suggest correspond to changes in air trapping. The overall objective of this project is to validate Sylvee's air trapping algorithms in a cohort of 20 healthy controls and 40 COPD patients with and without air trapping, respectively. In Aims 1 and 2, we will miniaturize and add sensors to the Sylvee device and develop a user interface (UI) and a mobile application. In Aims 3 and 4, we will create an Air Trapping Index Report and validate it in a cross-sectional study vis-à-vis whole body plethysmography as a control. Results of this project provide a go/no-go development decision based on device function. We can apply these results in STTR Phase II, in a larger clinical study to evaluate Sylvee as an at-home monitoring system, with a goal of reducing hospitalizations by at least 30%. Ultimately, Sylvee will allow physicians to remotely monitor their patients' lung function and adjust their medications to reduce healthcare costs and improve patients' quality of life.

抽象的慢性阻塞性肺疾病（COPD）是美国住院的第三大主要原因。恶化 - 症状的令人担忧或“爆发” - 导致大多数COPD住院。自大多数可以通过遗传和/或口服药物的变化来治疗恶化，肺检测功能定义可能有助于较早的干预措施，并有助于延迟或预防住院。这监测肺功能的护理标准是肺活量测定法，体积学和CT扫描。但是，这些是昂贵的方法，不适合连续监视或在家使用。各种患者自我监控已经尝试了方法，例如脉搏血氧饱和度，呼吸速率监测和峰值流量计量，但是它们在降低住院的效率受到限制。各种形式的COPD的一个共同发现是空气诱捕，定义，因为体积异常增加呼气完成后，肺中剩余的空气。一大批证据明确表明空气陷阱加重过程中增加并减少加重。最近的报道表明空气诱捕是肺部功能恶化的预兆，而不是肺活量变化，可以测量通过低频超声（1-40 kHz）。空气捕获的声学监测可以为临床医生提供非 - 当需要进行医疗干预时，以避免不必要的急诊就诊和住院治疗时进行侵入性工具。 Respira Labs开发了一种低成本，非侵入性，基于声学的可穿戴设备，可以连续监测肺部共振：Sylvee。该设备与机器一起使用已知的基于声学的技术 - 学习算法以检测肺部共振的微小变化，我们的初步结果表明对应于空气陷阱的变化。该项目的总体目的是验证Sylvee的空气陷阱分别由20个健康对照组和40名患有和没有空气捕获的COPD患者组成的算法。在AIMS 1和2中，我们将小型化并在Sylvee设备中添加传感器并开发用户界面（UI）和一个移动应用程序。在AIMS 3和4中，我们将创建一个空气捕获索引报告，并在横断面研究相对于整个身体作为对照。该项目的结果提供了基于设备功能进行/NO-GO开发决策。我们可以将这些结果应用于sttr II阶段更大的临床研究将Sylvee评估为在家监测系统，目的是减少住院至少30％。最终，Sylvee将允许医生远程监测患者的肺功能并调整其药物以降低医疗保健成本并改善患者的生活质量。