OBJECTIVE HOME MANAGEMENT OF PEDIATRIC ASTHMA EXACERBATION USING MOBILE TECHNOLOGY AND MACHINE LEARNING

使用移动技术和机器学习对小儿哮喘发作进行客观家庭管理

• 批准号: 10010457
• 负责人: Shilpa Patel
• 金额: $ 25.79万
• 依托单位: AUSCULTECH DX, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10010457
• 关键词: Accident and Emergency department; Acute; Affect; Algorithms; Asthma; Auscultation; Cessation of life; Child; Childhood; Childhood Asthma; Chronic; Chronic Disease; Clinical; Clinical Data; Code; Color; Communication; Custom; Data; Databases; Development; Devices; Digital Signal Processing; Disease; Effectiveness; Emergency Situation; Emergency department visit; Emergent care; Family; Goals; Grant; Home environment; Hospitalization; Institution; Institutional Review Boards; Legal patent; Machine Learning; Maps; Measurement; Measures; Medical; Medical Staff; Medical center; Modeling; Morbidity - disease rate; Oxygen; Parents; Patients; Peripheral; Phase; Physicians; Physiologic pulse; Pulse Oximetry; Research; Respiratory Sounds; Severities; Small Business Technology Transfer Research; Speech; Stethoscopes; Symptoms; Technology; Testing; Time; Training; United States; United States National Institutes of Health; Wheezing; Wireless Technology; Work of Breathing; asthma exacerbation; asthmatic patient; automated algorithm; base; computerized; cost; data acquisition; digital; improved; machine learning algorithm; medication administration; mobile application; mobile computing; mortality; new technology; novel; pediatric emergency; premature; prospective; respiratory

PROJECT SUMMARY Asthma is the most common chronic pediatric disease in the United States, affecting 6.2 million or 1 in 12 children. Despite advances in the management of childhood asthma, asthma exacerbation results in approximately 550,000 emergency department (ED) visits, 80,000 hospitalizations, and hundreds of premature deaths each year. Early symptoms of asthma exacerbation, especially in children, are nonspecific and are unfortunately often not recognized by parents as asthma until the child demonstrates more severe symptoms, enough to require emergent care. The long-term goal of this STTR initiative is to empower parents to initiate timely therapy for acute asthma by supplementing their subjective assessment with an objective measure of acute asthma severity. The mobile technology we propose to develop, test and deploy toward this goal will use digital signal processing (DSP) and machine learning (ML) to determine three distinct severity zones (corresponding to the green, yellow and red zones on the asthma action plan) allowing parents to follow asthma action plans accurately. The resulting improved and timely home-based management of childhood asthma should reduce current excessive ED utilization and unacceptably high rates of morbidity and mortality. The proposed technology is inspired by and based on the pediatric asthma severity score (PASS). PASS is used in many pediatric EDs for objective assessment of acute asthma severity to aid management of acute asthma and critical discharge and hospitalization decisions. The components of PASS are five well-studied and validated clinical parameters. Our goal in the proposed project is to develop new technology that enables parents to make similar measurements in the home setting and map those to the 3 color-coded zones for easier execution of asthma action plans. The 2 specific aims of the project are to 1) build a pediatric asthma database with ground-truth clinical findings, and 2) develop and validate DSP and ML algorithms for automated assessment of acute asthma severity. The successful completion of these aims will result in a validated technology suitable for objective, home-based assessment of acute asthma severity. Such assessment is currently possible but only in medical facilities by trained medical staff. Our Phase II goals will be to (a) further improve and deploy the technology in homes and (b) conduct a prospective trial measuring its feasibility, utilization and effectiveness in controlling asthma emergencies and costs. In Phase II, we will also pursue the necessary regulatory approvals.

项目摘要 哮喘是美国最常见的慢性儿科疾病，影响620万或1/ 12个孩子尽管儿童哮喘的管理取得了进展，但哮喘急性发作导致 大约550，000次急诊（艾德）就诊，80，000次住院，以及数百次 每年过早死亡。哮喘急性发作的早期症状，尤其是儿童， 非特异性的，不幸的是，父母往往不承认哮喘，直到孩子 表现出更严重的症状足以需要紧急护理长期目标是 STTR倡议是授权父母及时开始治疗急性哮喘， 他们的主观评估与急性哮喘严重程度的客观测量。移动的 我们建议为实现这一目标而开发、测试和部署的技术将使用数字信号处理 (DSP)和机器学习（ML）来确定三个不同的严重性区域（对应于 哮喘行动计划上的绿色、黄色和红色区域），允许家长遵循哮喘行动计划 准确地由此产生的改善和及时的以家庭为基础的儿童哮喘管理， 降低当前过度使用艾德和不可接受的高发病率和死亡率。的 所提出的技术受到儿科哮喘严重性评分（PASS）的启发并基于该评分。通票是 用于许多儿科急诊科，用于客观评估急性哮喘严重程度， 急性哮喘和紧急出院和住院决定。PASS的组成部分有五个 经过充分研究和验证的临床参数。我们在拟议项目中的目标是开发新的 这项技术使父母能够在家中进行类似的测量，并将这些测量结果映射到 3个颜色编码区，便于执行哮喘行动计划。该项目的两个具体目标 是1）建立一个儿科哮喘数据库与地面真相的临床结果，2）开发和 验证DSP和ML算法用于急性哮喘严重程度的自动评估。成功 这些目标的完成将产生一种经过验证的技术，适用于客观的、基于家庭的 急性哮喘严重程度的评估。这种评估目前是可能的，但仅限于医疗领域 设施由训练有素的医务人员提供。我们第二阶段的目标是：（a）进一步改善和部署 （B）进行前瞻性试验，测量其可行性、利用率和 控制哮喘紧急情况的有效性和成本。在第二阶段，我们亦会继续推行 必要的监管批准。

项目成果

StethAid: A Digital Auscultation Platform for Pediatrics.

• DOI: 10.3390/s23125750
• 发表时间: 2023-06-20
• 期刊: Sensors (Basel, Switzerland)
• 作者: Arjoune Y;Nguyen TN;Salvador T;Telluri A;Schroeder JC;Geggel RL;May JW;Pillai DK;Teach SJ;Patel SJ;Doroshow RW;Shekhar R
• 通讯作者: Shekhar R