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

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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.